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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-27 06:27:46
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| likes
int64 0
11.7k
| library_name
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Pierre-obi/Mistral_solar-slerp | Pierre-obi | 2024-01-12T11:13:00Z | 1,379 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"mistralai/Mistral-7B-Instruct-v0.2",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T11:08:37Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Nous-Hermes-2-SOLAR-10.7B
- mistralai/Mistral-7B-Instruct-v0.2
---
# Mistral_solar-slerp
Mistral_solar-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Nous-Hermes-2-SOLAR-10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B)
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
layer_range: [0, 32]
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Pierre-obi/Mistral_solar-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"])
``` |
beenish0092/my_awesome_wnut_model | beenish0092 | 2024-01-12T11:09:18Z | 91 | 0 | transformers | [
"transformers",
"tensorboard",
"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 | 2024-01-12T09:47:47Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_awesome_wnut_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_awesome_wnut_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: 0.2759
- Precision: 0.5525
- Recall: 0.2827
- F1: 0.3740
- Accuracy: 0.9407
## 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 | 0.2882 | 0.5 | 0.2354 | 0.3201 | 0.9378 |
| No log | 2.0 | 426 | 0.2759 | 0.5525 | 0.2827 | 0.3740 | 0.9407 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0
|
MaziyarPanahi/MythoMist-7b-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T11:07:32Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Gryphe/MythoMist-7b",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T11:02:26Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Gryphe/MythoMist-7b
---
# MythoMist-7b-Mistral-7B-Instruct-v0.2-slerp
MythoMist-7b-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Gryphe/MythoMist-7b](https://huggingface.co/Gryphe/MythoMist-7b)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Gryphe/MythoMist-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/MythoMist-7b-Mistral-7B-Instruct-v0.2-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"])
``` |
Hemg/Demoaudioclass | Hemg | 2024-01-12T10:59:27Z | 147 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-01-12T10:28:05Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: Demoaudioclass
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. -->
# Demoaudioclass
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6391 | 0.0265 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
|
jysssacc/mt0-base_fine_lr5e-06_bs4_epoch5_wd0.01 | jysssacc | 2024-01-12T10:57:27Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:bigscience/mt0-base",
"base_model:finetune:bigscience/mt0-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-01-11T15:11:09Z | ---
license: apache-2.0
base_model: bigscience/mt0-base
tags:
- generated_from_trainer
model-index:
- name: mt0-base_fine_lr5e-06_bs4_epoch5_wd0.01
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. -->
# mt0-base_fine_lr5e-06_bs4_epoch5_wd0.01
This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0028
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5508 | 1.0 | 157 | 0.1788 |
| 0.1687 | 2.0 | 314 | 0.0292 |
| 0.0729 | 3.0 | 471 | 0.0057 |
| 0.0339 | 4.0 | 628 | 0.0032 |
| 0.028 | 5.0 | 785 | 0.0028 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
ltg/nort5-large-en-no-translation | ltg | 2024-01-12T10:49:39Z | 154 | 3 | transformers | [
"transformers",
"pytorch",
"text2text-generation",
"Norwegian",
"English",
"translation",
"custom_code",
"no",
"nb",
"nn",
"en",
"arxiv:2305.03880",
"license:cc-by-4.0",
"autotrain_compatible",
"region:us"
] | translation | 2024-01-12T09:57:56Z | ---
language:
- 'no'
- nb
- nn
- en
inference: false
tags:
- Norwegian
- English
- translation
license: cc-by-4.0
pipeline_tag: translation
---
# NorT5 large finetuned for English β Norwegian (BokmΓ₯l or Nynorsk) translation
<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%>
## Example usage
This model is specifically finetuned for translating documents in the English-to-Norwegian direction.
Unlike traditional NMT models, it is trained on paragraph-to-paragraph translation β the translation quality is thus better if you feed it whole paragraphs instead of segmented sentences.
A simple example of how to use this model can be found in the `translate.py` file:
```python
import torch
import transformers
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers.generation import LogitsProcessor
class RepetitionPenaltyLogitsProcessor(LogitsProcessor):
def __init__(self, penalty: float, model):
last_bias = model.classifier.nonlinearity[-1].bias.data
last_bias = torch.nn.functional.log_softmax(last_bias)
self.penalty = penalty * (last_bias - last_bias.max())
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
penalized_score = torch.gather(scores + self.penalty.unsqueeze(0).to(input_ids.device), 1, input_ids).to(scores.dtype)
scores.scatter_(1, input_ids, penalized_score)
return scores
class Translator:
def __init__(self, model_path="ltg/nort5-large-en-no-translation", device="cpu"):
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.cls_index = self.tokenizer.convert_tokens_to_ids("[CLS]")
self.sep_index = self.tokenizer.convert_tokens_to_ids("[SEP]")
self.eos_index = self.tokenizer.convert_tokens_to_ids("[EOS]")
self.pad_index = self.tokenizer.convert_tokens_to_ids("[PAD]")
self.eng_index = self.tokenizer.convert_tokens_to_ids(">>eng<<")
self.nob_index = self.tokenizer.convert_tokens_to_ids(">>nob<<")
self.nno_index = self.tokenizer.convert_tokens_to_ids(">>nno<<")
self.model = AutoModelForSeq2SeqLM.from_pretrained(model_path, trust_remote_code=True)
self.device = device
print(f"SYSTEM: Running on {self.device}", flush=True)
self.model = self.model.to(device)
self.model.eval()
print(f"Sucessfully loaded the model to the memory")
self.LANGUAGE_IDS = {
"en": self.eng_index,
"nb": self.nob_index,
"nn": self.nno_index
}
def __call__(self, source, source_language, target_language):
source = [s.strip() for s in source.split('\n')]
source_subwords = self.tokenizer(source).input_ids
source_subwords = [[self.cls_index, self.LANGUAGE_IDS[target_language], self.LANGUAGE_IDS[source_language]] + s + [self.sep_index] for s in source_subwords]
source_subwords = [torch.tensor(s) for s in source_subwords]
source_subwords = torch.nn.utils.rnn.pad_sequence(source_subwords, batch_first=True, padding_value=self.pad_index)
source_subwords = source_subwords[:, :512].to(self.device)
def generate(model, **kwargs):
with torch.inference_mode():
with torch.autocast(enabled=self.device != "cpu", device_type="cuda", dtype=torch.bfloat16):
return model.generate(**kwargs)
generate_kwargs = dict(
input_ids=source_subwords,
attention_mask=(source_subwords != self.pad_index).long(),
max_new_tokens = 512-1,
num_beams=8,
length_penalty=1.6,
early_stopping=True,
do_sample=False,
use_cache=True,
logits_processor=[RepetitionPenaltyLogitsProcessor(0.5, self.model), transformers.LogitNormalization()]
)
output = generate(self.model, **generate_kwargs).tolist()
paragraphs = [self.tokenizer.decode(c, skip_special_tokens=True).strip() for c in output]
translation = '\n'.join(paragraphs)
return translation
if __name__ == "__main__":
translator = Translator()
en_text = "How are you feeling right now? Better?"
no_text = translator(en_text, "en", "nb")
print(en_text)
print(no_text)
```
## The NorT5 and NorBERT family
The official release of a new generation of NorT5 language models described in paper [**NorBench β A Benchmark for Norwegian Language Models**](https://arxiv.org/abs/2305.03880). Plese read the paper to learn more details about the model.
## Other sizes:
- [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs)
- [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small)
- [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base)
- [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large)
## Encoder-only NorBERT siblings:
- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small)
- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-norbench,
title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models",
author = "Samuel, David and
Kutuzov, Andrey and
Touileb, Samia and
Velldal, Erik and
{\O}vrelid, Lilja and
R{\o}nningstad, Egil and
Sigdel, Elina and
Palatkina, Anna",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.61",
pages = "618--633",
abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.",
}
``` |
MaziyarPanahi/zephyr-7b-alpha-sharded-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T10:48:58Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"anakin87/zephyr-7b-alpha-sharded",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T10:44:04Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- anakin87/zephyr-7b-alpha-sharded
---
# zephyr-7b-alpha-sharded-Mistral-7B-Instruct-v0.2-slerp
zephyr-7b-alpha-sharded-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [anakin87/zephyr-7b-alpha-sharded](https://huggingface.co/anakin87/zephyr-7b-alpha-sharded)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: anakin87/zephyr-7b-alpha-sharded
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/zephyr-7b-alpha-sharded-Mistral-7B-Instruct-v0.2-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"])
``` |
MaziyarPanahi/MetaMath-Tulpar-7b-v2-Slerp-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T10:40:21Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Weyaxi/MetaMath-Tulpar-7b-v2-Slerp",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T10:35:38Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Weyaxi/MetaMath-Tulpar-7b-v2-Slerp
---
# MetaMath-Tulpar-7b-v2-Slerp-Mistral-7B-Instruct-v0.2-slerp
MetaMath-Tulpar-7b-v2-Slerp-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Weyaxi/MetaMath-Tulpar-7b-v2-Slerp](https://huggingface.co/Weyaxi/MetaMath-Tulpar-7b-v2-Slerp)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Weyaxi/MetaMath-Tulpar-7b-v2-Slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/MetaMath-Tulpar-7b-v2-Slerp-Mistral-7B-Instruct-v0.2-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"])
``` |
jysssacc/opt-350m_IA3_lr5e-06_bs4_epoch5_wd0.01 | jysssacc | 2024-01-12T10:26:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"region:us"
] | null | 2024-01-11T13:22:52Z | ---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/opt-350m
model-index:
- name: opt-350m_IA3_lr5e-06_bs4_epoch5_wd0.01
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. -->
# opt-350m_IA3_lr5e-06_bs4_epoch5_wd0.01
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8352
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0396 | 1.0 | 157 | 3.8444 |
| 4.0533 | 2.0 | 314 | 3.8425 |
| 4.0101 | 3.0 | 471 | 3.8394 |
| 4.0263 | 4.0 | 628 | 3.8363 |
| 4.0251 | 5.0 | 785 | 3.8352 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0 |
MaziyarPanahi/Metis-0.4-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T10:15:59Z | 25 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Mihaiii/Metis-0.4",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T10:11:01Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Mihaiii/Metis-0.4
---
# Metis-0.4-Mistral-7B-Instruct-v0.2-slerp
Metis-0.4-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Mihaiii/Metis-0.4](https://huggingface.co/Mihaiii/Metis-0.4)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Mihaiii/Metis-0.4
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Metis-0.4-Mistral-7B-Instruct-v0.2-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"])
``` |
trustyai/sarcasm_plus | trustyai | 2024-01-12T10:14:40Z | 174 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:raquiba/Sarcasm_News_Headline",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-01-12T09:31:49Z | ---
license: apache-2.0
datasets:
- raquiba/Sarcasm_News_Headline
language:
- en
metrics:
- perplexity
---
# Model Card for `sarcasm_plus`
This model is a `facebook/bart-large` fine-tuned on sarcastic comments from `raquiba/Sarcasm_News_Headline` dataset.
## Model Details
This model is not intended to be used for plain inference as it is very likely to predict non-sarcastic content.
It is intended to be used instead as "utility model" for detecting and fixing sarcastic content as its token probability distributions will likely differ from comparable models not trained/fine-tuned over sarcastic data.
Its name `sarcasm_plus` refers to the _G+_ model in [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
### Model Description
- **Developed by:** [tteofili]
- **Shared by :** [tteofili]
<!--- **Model type:** [More Information Needed]-->
<!--- **Language(s) (NLP):** [More Information Needed]-->
- **License:** [apache-2.0]
- **Finetuned from model :** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
<!--
### 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 model is fine-tuned over non-sarcastic comments from `raquiba/Sarcasm_News_Headline` and it is very likely to produce non-sarcastic content.
For this reason this model should only be used in combination with other models for the sake of detecting / fixing sarcastic content, see for example [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
<!--
This section is meant to convey both technical and sociotechnical limitations.
[More Information Needed]
### Recommendations
This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering.
[More Information Needed]
### Training Procedure
This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure.
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
**Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision
#### Speeds, Sizes, Times [optional]
- This section provides information about throughput, start/end time, checkpoint size if relevant, etc.
[More Information Needed]
-->
## Evaluation
This section describes the evaluation protocols and provides the results.
### Testing Data, Factors & Metrics
#### Testing Data
This model was tested on `raquiba/Sarcasm_News_Headline` testset.
<!--
#### Factors
These are the things the evaluation is disaggregating by, e.g., subpopulations or domains.
[More Information Needed]
-->
#### Metrics
Model was evaluated using `perplexity` (on the MLM task).
### Results
Perplexity: _1.09_
<!--
#### 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]
|
MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T10:04:58Z | 21 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T10:00:09Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp
---
# MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.2-slerp
MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp](https://huggingface.co/Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.2-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"])
``` |
Zanshinmu/SDXL_SMOKING | Zanshinmu | 2024-01-12T10:03:33Z | 24 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] | text-to-image | 2024-01-12T10:03:25Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
Steps: 20, Sampler: DPM++ 3M SDE Karras, CFG scale: 7, Seed: 4028220007,
Size: 2048x2048, Model hash: c6bcee2753, Model:
SDXL_Cybergirl_v3-step00011600, Denoising strength: 0.33, Version: v1.7.0
output:
url: images/00000-4028220007.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: smkng, smoking a cigarette
license: apache-2.0
---
# SDXL_SMOKING
<Gallery />
## Model description
Trained on Apple Silicon with Draw Things
## Trigger words
You should use `smkng` to trigger the image generation.
You should use `smoking a cigarette` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Zanshinmu/SDXL_SMOKING/tree/main) them in the Files & versions tab.
|
rsaketh02/imdb | rsaketh02 | 2024-01-12T09:58:37Z | 46 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-01-12T08:37:21Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: rsaketh02/imdb
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. -->
# rsaketh02/imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3502
- Validation Loss: 0.3425
- Train Accuracy: 0.8734
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 80, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3609 | 0.3425 | 0.8734 | 0 |
| 0.3502 | 0.3425 | 0.8734 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Suva/bge-large-finetuned | Suva | 2024-01-12T09:58:11Z | 19 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-01-12T09:44:40Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 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('{MODEL_NAME}')
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 40 with parameters:
```
{'batch_size': 4, '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": 10,
"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": 40,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
jaydip-tss/my_awesome_food_model | jaydip-tss | 2024-01-12T09:56:55Z | 180 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-01-12T09:47:54Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_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_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8266
- Accuracy: 0.922
## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1714 | 0.99 | 62 | 1.1657 | 0.896 |
| 0.8623 | 2.0 | 125 | 0.9087 | 0.915 |
| 0.7595 | 2.98 | 186 | 0.8252 | 0.925 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
MaziyarPanahi/MelloGPT-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T09:54:18Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"steve-cse/MelloGPT",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T09:49:24Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- steve-cse/MelloGPT
---
# MelloGPT-Mistral-7B-Instruct-v0.2-slerp
MelloGPT-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [steve-cse/MelloGPT](https://huggingface.co/steve-cse/MelloGPT)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: steve-cse/MelloGPT
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/MelloGPT-Mistral-7B-Instruct-v0.2-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"])
``` |
tuantran1632001/Psyfighter2-Orca2-13B-ties-GGUF | tuantran1632001 | 2024-01-12T09:51:46Z | 155 | 0 | null | [
"gguf",
"GGUF",
"KoboldAI/LLaMA2-13B-Psyfighter2",
"microsoft/Orca-2-13b",
"base_model:tuantran1632001/Psyfighter2-Orca2-13B-ties",
"base_model:quantized:tuantran1632001/Psyfighter2-Orca2-13B-ties",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-01-11T15:52:06Z | ---
license: other
license_name: microsoft-research-license
tags:
- GGUF
- KoboldAI/LLaMA2-13B-Psyfighter2
- microsoft/Orca-2-13b
model_type: llama
model_name: Psyfighter2-Orca2-13B-ties
quantized_by: tuantran1632001
base_model: tuantran1632001/Psyfighter2-Orca2-13B-ties
---
This is the GGUF quantize of the merged model [tuantran1632001/Psyfighter2-Orca2-13B-ties](https://huggingface.co/tuantran1632001/Psyfighter2-Orca2-13B-ties).
| File | Quantize | Size |
|------|----------|------|
| [Psyfighter2-Orca2-13B-ties-fp16.gguf](Psyfighter2-Orca2-13B-ties-fp16.gguf) | fp16 | 25GiB |
| [Psyfighter2-Orca2-13B-ties-Q2_K.gguf](Psyfighter2-Orca2-13B-ties-Q2_K.gguf) | Q2_K | 5.1GiB |
| [Psyfighter2-Orca2-13B-ties-Q3_K_L.gguf](Psyfighter2-Orca2-13B-ties-Q3_K_L.gguf) | Q3_K_L | 6.5GiB |
| [Psyfighter2-Orca2-13B-ties-Q3_K_M.gguf](Psyfighter2-Orca2-13B-ties-Q3_K_M.gguf) | Q3_K_M | 6.0GiB |
| [Psyfighter2-Orca2-13B-ties-Q3_K_S.gguf](Psyfighter2-Orca2-13B-ties-Q3_K_S.gguf) | Q3_K_S | 5.3GiB |
| [Psyfighter2-Orca2-13B-ties-Q4_0.gguf](Psyfighter2-Orca2-13B-ties-Q4_0.gguf) | Q4_0 | 6.9GiB |
| [Psyfighter2-Orca2-13B-ties-Q4_1.gguf](Psyfighter2-Orca2-13B-ties-Q4_1.gguf) | Q4_1 | 6.8GiB |
| [Psyfighter2-Orca2-13B-ties-Q4_K_M.gguf](Psyfighter2-Orca2-13B-ties-Q4_K_M.gguf) | Q4_K_M | 7.7GiB |
| [Psyfighter2-Orca2-13B-ties-Q4_K_S.gguf](Psyfighter2-Orca2-13B-ties-Q4_K_S.gguf) | Q4_K_S | 7.0GiB |
| [Psyfighter2-Orca2-13B-ties-Q5_0.gguf](Psyfighter2-Orca2-13B-ties-Q5_0.gguf) | Q5_0 | 8.4GiB |
| [Psyfighter2-Orca2-13B-ties-Q5_1.gguf](Psyfighter2-Orca2-13B-ties-Q5_1.gguf) | Q5_1 | 9.2GiB |
| [Psyfighter2-Orca2-13B-ties-Q5_K_M.gguf](Psyfighter2-Orca2-13B-ties-Q5_K_M.gguf) | Q5_K_M | 8.6GiB |
| [Psyfighter2-Orca2-13B-ties-Q5_K_S.gguf](Psyfighter2-Orca2-13B-ties-Q5_K_S.gguf) | Q5_K_S | 8.4GiB |
| [Psyfighter2-Orca2-13B-ties-Q6_K.gguf](Psyfighter2-Orca2-13B-ties-Q6_K.gguf) | Q6_K | 10GiB |
| [Psyfighter2-Orca2-13B-ties-Q8_0.gguf](Psyfighter2-Orca2-13B-ties-Q8_0.gguf) | Q8_0 | 13GiB | |
synergex7/synergex7 | synergex7 | 2024-01-12T09:48:14Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"Synergex 7",
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-12T09:46:50Z | ---
license: creativeml-openrail-m
language:
- en
library_name: sentence-transformers
tags:
- Synergex 7
---
[Synergex 7](https://groups.google.com/g/ibm.software.network.directory-integrator/c/l9m-K-vuHk8) Worked on Sexual Execution: Upgrades could prompt expanded endurance, drive, and perseverance during sexual activity.Increased Certainty: Feeling more fit in the room can support confidence and confidence.Better Erectile Capability: Improvements might assist with erectile brokenness (ED) or different issues connected with accomplishing and keeping an erection.
VISIT HERE FOR OFFICIAL WEBSITE:-https://atozsupplement.com/synergex-7/
|
MaziyarPanahi/OpenHermes-2.5-neural-chat-v3-3-Slerp-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T09:45:24Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T09:40:21Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
---
# OpenHermes-2.5-neural-chat-v3-3-Slerp-Mistral-7B-Instruct-v0.2-slerp
OpenHermes-2.5-neural-chat-v3-3-Slerp-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/OpenHermes-2.5-neural-chat-v3-3-Slerp-Mistral-7B-Instruct-v0.2-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"])
``` |
NhatTranKKK/Reinforce_model1 | NhatTranKKK | 2024-01-12T09:39:30Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-01-12T09:39:22Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce_model1
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
|
MaziyarPanahi/Marcoroni-neural-chat-7B-v2-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T09:36:37Z | 26 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Toten5/Marcoroni-neural-chat-7B-v2",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T09:31:33Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Toten5/Marcoroni-neural-chat-7B-v2
---
# Marcoroni-neural-chat-7B-v2-Mistral-7B-Instruct-v0.2-slerp
Marcoroni-neural-chat-7B-v2-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Toten5/Marcoroni-neural-chat-7B-v2](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v2)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Toten5/Marcoroni-neural-chat-7B-v2
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Marcoroni-neural-chat-7B-v2-Mistral-7B-Instruct-v0.2-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"])
``` |
Suhaanthvv/distilhubert-finetuned-gtzan | Suhaanthvv | 2024-01-12T09:32:55Z | 146 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-01-12T07:56:24Z | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: gtzan
type: gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.81
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the gtzan dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5663
- Accuracy: 0.81
## 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
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9532 | 1.0 | 113 | 1.7671 | 0.43 |
| 1.2976 | 2.0 | 226 | 1.1920 | 0.6 |
| 1.062 | 3.0 | 339 | 0.8834 | 0.73 |
| 0.8596 | 4.0 | 452 | 0.7682 | 0.75 |
| 0.5926 | 5.0 | 565 | 0.6428 | 0.82 |
| 0.406 | 6.0 | 678 | 0.6709 | 0.78 |
| 0.4717 | 7.0 | 791 | 0.5713 | 0.82 |
| 0.1326 | 8.0 | 904 | 0.5567 | 0.82 |
| 0.3459 | 9.0 | 1017 | 0.5477 | 0.83 |
| 0.091 | 10.0 | 1130 | 0.5663 | 0.81 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
aisuko/prefix-tuning-t5-large-with-sentences-allagree | aisuko | 2024-01-12T09:25:25Z | 6 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google-t5/t5-large",
"base_model:adapter:google-t5/t5-large",
"region:us"
] | null | 2024-01-09T23:51:35Z | ---
library_name: peft
base_model: t5-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 0.7.1 |
FeiiYin/lora-trained-xl-audi-blue-800-1e-5 | FeiiYin | 2024-01-12T09:23:37Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-01-12T09:12:54Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'A photo of sks car on the street'
output:
url:
"image_0.png"
- text: 'A photo of sks car on the street'
output:
url:
"image_1.png"
- text: 'A photo of sks car on the street'
output:
url:
"image_2.png"
- text: 'A photo of sks car on the street'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks car
license: openrail++
---
# SDXL LoRA DreamBooth - FeiiYin/lora-trained-xl-audi-blue-800-1e-5
<Gallery />
## Model description
These are FeiiYin/lora-trained-xl-audi-blue-800-1e-5 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks car to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](FeiiYin/lora-trained-xl-audi-blue-800-1e-5/tree/main) them in the Files & versions tab.
|
sandeepksingh1/Llama-2-7b-chat-hf-IA3_50_V3 | sandeepksingh1 | 2024-01-12T09:15:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-12T09:15:13Z | ---
library_name: peft
base_model: NousResearch/Llama-2-7b-chat-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 0.7.2.dev0 |
Suvir-Misra/GSTLawyer | Suvir-Misra | 2024-01-12T09:10:01Z | 1 | 0 | peft | [
"peft",
"legal",
"en",
"arxiv:1910.09700",
"base_model:TheBloke/Llama-2-7B-Chat-GPTQ",
"base_model:adapter:TheBloke/Llama-2-7B-Chat-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-01-12T08:41:15Z | ---
library_name: peft
base_model: TheBloke/Llama-2-7B-Chat-GPTQ
license: apache-2.0
language:
- en
tags:
- legal
---
# Model Card for Model ID
Trained on the Indian GST and Service Tax legal pronouncements till 2023
## Model Details
### Model Description
- **Developed by:** [Suvir Misra, Commissioner, GST, India]
- **Shared by [optional]:** [Suvir Misra, Commissioner, GST, India]
- **Model type:** [Llama-2-7B-Chat]
- **Language(s) (NLP):** [English]
- **License:** [apache-2.0]
- **Finetuned from model [optional]:** [TheBloke/Llama-2-7B-Chat-GPTQ]
### Model Sources [optional]
TheBloke/Llama-2-7B-Chat-GPTQ
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
Try asking some question on the GST or Service Tax law/ ruling till the year 2023.
### Direct Use
Try asking some question on the GST or Service Tax law/ ruling till the year 2023.
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.01
- desc_act: False
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
- max_input_length: None
### Framework versions
- PEFT 0.6.2 |
Chattiori/GranadaMix | Chattiori | 2024-01-12T09:07:48Z | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-11T11:25:20Z | ---
license: creativeml-openrail-m
---
|
MaziyarPanahi/dolphin-2.6-mistral-7b-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T09:06:34Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"cognitivecomputations/dolphin-2.6-mistral-7b",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T09:01:38Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- cognitivecomputations/dolphin-2.6-mistral-7b
---
# dolphin-2.6-mistral-7b-Mistral-7B-Instruct-v0.2-slerp
dolphin-2.6-mistral-7b-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [cognitivecomputations/dolphin-2.6-mistral-7b](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: cognitivecomputations/dolphin-2.6-mistral-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/dolphin-2.6-mistral-7b-Mistral-7B-Instruct-v0.2-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"])
``` |
mtc/meta-llama-Llama-2-7b-hf-arxiv-summarization-5000-no_quantization-2k-lora-full | mtc | 2024-01-12T09:06:07Z | 1 | 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 | 2024-01-12T06:30:18Z | ---
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 0.7.1 |
mtc/meta-llama-Llama-2-7b-hf-pubmed-summarization-5000-no-quantization-2k-lora-full | mtc | 2024-01-12T09:05:04Z | 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 | 2024-01-12T06:19:30Z | ---
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 0.7.1 |
MaziyarPanahi/Mistral-7B-v0.1-sharded-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T08:57:12Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"alexsherstinsky/Mistral-7B-v0.1-sharded",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T08:50:36Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- alexsherstinsky/Mistral-7B-v0.1-sharded
---
# Mistral-7B-v0.1-sharded-Mistral-7B-Instruct-v0.2-slerp
Mistral-7B-v0.1-sharded-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [alexsherstinsky/Mistral-7B-v0.1-sharded](https://huggingface.co/alexsherstinsky/Mistral-7B-v0.1-sharded)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: alexsherstinsky/Mistral-7B-v0.1-sharded
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Mistral-7B-v0.1-sharded-Mistral-7B-Instruct-v0.2-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"])
``` |
DenisKIMEE/ALT1 | DenisKIMEE | 2024-01-12T08:52:08Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:baffo32/decapoda-research-llama-7B-hf",
"base_model:adapter:baffo32/decapoda-research-llama-7B-hf",
"region:us"
] | null | 2024-01-12T08:49:20Z | ---
library_name: peft
base_model: baffo32/decapoda-research-llama-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 0.7.2.dev0 |
MaziyarPanahi/LeoScorpius-GreenNode-Platypus-7B-v1-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T08:46:44Z | 22 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"ignos/LeoScorpius-GreenNode-Platypus-7B-v1",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T08:41:48Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- ignos/LeoScorpius-GreenNode-Platypus-7B-v1
---
# LeoScorpius-GreenNode-Platypus-7B-v1-Mistral-7B-Instruct-v0.2-slerp
LeoScorpius-GreenNode-Platypus-7B-v1-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [ignos/LeoScorpius-GreenNode-Platypus-7B-v1](https://huggingface.co/ignos/LeoScorpius-GreenNode-Platypus-7B-v1)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: ignos/LeoScorpius-GreenNode-Platypus-7B-v1
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/LeoScorpius-GreenNode-Platypus-7B-v1-Mistral-7B-Instruct-v0.2-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"])
``` |
hflserdaniel/chai_s6_13b_slerp | hflserdaniel | 2024-01-12T08:37:00Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T08:29:42Z | ---
base_model: []
tags:
- mergekit
- merge
---
# merged
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 SLERP merge method.
### Models Merged
The following models were included in the merge:
* /paratera5-data/home/wangchenyue/LLaMA-Factory/output/chai_season6_13b/checkpoint-150
* /paratera5-data/home/wangchenyue/model/Noromaid-13b-v0.3
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: /paratera5-data/home/wangchenyue/model/Noromaid-13b-v0.3
dtype: float16
merge_method: slerp
parameters:
t:
- value: 0.9
slices:
- sources:
- layer_range: [0, 40]
model: /paratera5-data/home/wangchenyue/LLaMA-Factory/output/chai_season6_13b/checkpoint-150
- layer_range: [0, 40]
model: /paratera5-data/home/wangchenyue/model/Noromaid-13b-v0.3
```
|
MaziyarPanahi/agiin-13.6B-v0.1-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T08:36:20Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"mncai/agiin-13.6B-v0.1",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T08:31:02Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- mncai/agiin-13.6B-v0.1
---
# agiin-13.6B-v0.1-Mistral-7B-Instruct-v0.2-slerp
agiin-13.6B-v0.1-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [mncai/agiin-13.6B-v0.1](https://huggingface.co/mncai/agiin-13.6B-v0.1)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: mncai/agiin-13.6B-v0.1
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/agiin-13.6B-v0.1-Mistral-7B-Instruct-v0.2-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"])
``` |
thebeautifulmegumiasaoka/Megumi_Asaoka | thebeautifulmegumiasaoka | 2024-01-12T08:34:19Z | 0 | 0 | null | [
"singer",
"text-to-speech",
"ja",
"dataset:wikimedia/wikipedia",
"license:apache-2.0",
"region:us"
] | text-to-speech | 2024-01-12T08:20:52Z | ---
license: apache-2.0
datasets:
- wikimedia/wikipedia
language:
- ja
tags:
- singer
metrics:
- character
pipeline_tag: text-to-speech
--- |
MaziyarPanahi/Mini_synata_7b_011-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T08:14:25Z | 21 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Minirecord/Mini_synata_7b_011",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T08:09:27Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Minirecord/Mini_synata_7b_011
---
# Mini_synata_7b_011-Mistral-7B-Instruct-v0.2-slerp
Mini_synata_7b_011-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Minirecord/Mini_synata_7b_011](https://huggingface.co/Minirecord/Mini_synata_7b_011)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Minirecord/Mini_synata_7b_011
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Mini_synata_7b_011-Mistral-7B-Instruct-v0.2-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"])
``` |
MaziyarPanahi/MRAI_synatra_7B_v1-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T08:04:21Z | 25 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"MRAIRR/MRAI_synatra_7B_v1",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T07:59:29Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- MRAIRR/MRAI_synatra_7B_v1
---
# MRAI_synatra_7B_v1-Mistral-7B-Instruct-v0.2-slerp
MRAI_synatra_7B_v1-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [MRAIRR/MRAI_synatra_7B_v1](https://huggingface.co/MRAIRR/MRAI_synatra_7B_v1)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: MRAIRR/MRAI_synatra_7B_v1
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/MRAI_synatra_7B_v1-Mistral-7B-Instruct-v0.2-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"])
``` |
ai-anytime/unsloth_4bit_mistral_imdb_model | ai-anytime | 2024-01-12T07:57:12Z | 3 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/mistral-7b",
"base_model:adapter:unsloth/mistral-7b",
"region:us"
] | null | 2024-01-12T07:56:46Z | ---
library_name: peft
base_model: unsloth/mistral-7b
---
# 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 0.7.1 |
MaziyarPanahi/Mistral-7B-KNUT-v0.2-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T07:55:40Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Herry443/Mistral-7B-KNUT-v0.2",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T07:50:55Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Herry443/Mistral-7B-KNUT-v0.2
---
# Mistral-7B-KNUT-v0.2-Mistral-7B-Instruct-v0.2-slerp
Mistral-7B-KNUT-v0.2-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Herry443/Mistral-7B-KNUT-v0.2](https://huggingface.co/Herry443/Mistral-7B-KNUT-v0.2)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Herry443/Mistral-7B-KNUT-v0.2
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Mistral-7B-KNUT-v0.2-Mistral-7B-Instruct-v0.2-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"])
``` |
jeongyeom/xlm-roberta-base-finetuned-panx-it | jeongyeom | 2024-01-12T07:51:35Z | 89 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-01-12T07:49:32Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2557
- F1: 0.8083
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.834 | 1.0 | 70 | 0.3297 | 0.7233 |
| 0.2913 | 2.0 | 140 | 0.2851 | 0.7810 |
| 0.1944 | 3.0 | 210 | 0.2557 | 0.8083 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
samuelesam/Vipoo1completo | samuelesam | 2024-01-12T07:48:42Z | 0 | 0 | null | [
"arxiv:1910.09700",
"region:us"
] | null | 2023-06-30T16:01:28Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jeongyeom/xlm-roberta-base-finetuned-panx-de-fr | jeongyeom | 2024-01-12T07:45:21Z | 89 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-01-12T07:33:06Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
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. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1616
- F1: 0.8613
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2888 | 1.0 | 715 | 0.1836 | 0.8232 |
| 0.1461 | 2.0 | 1430 | 0.1610 | 0.8472 |
| 0.0947 | 3.0 | 2145 | 0.1616 | 0.8613 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
nm-testing/Nous-Hermes-2-SOLAR-10.7B-pruned50-ds | nm-testing | 2024-01-12T07:36:12Z | 2 | 0 | transformers | [
"transformers",
"onnx",
"llama",
"text-generation",
"deepsparse",
"conversational",
"arxiv:2301.00774",
"base_model:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"base_model:quantized:NousResearch/Nous-Hermes-2-SOLAR-10.7B",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-01-12T06:34:38Z | ---
base_model: NousResearch/Nous-Hermes-2-SOLAR-10.7B
inference: false
model_type: llama
prompt_template: |
### User:\n
{prompt}
### Assistant:\n
quantized_by: mwitiderrick
tags:
- deepsparse
---
# Nous Hermes 2 - Solar 10.7B - DeepSparse
This repo contains model files for [Nous Hermes 2 - Solar 10.7B](https://huggingface.co/NousResearch/Nous-Hermes-2-SOLAR-10.7B) optimized for [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
## Inference
Install [DeepSparse LLM](https://github.com/neuralmagic/deepsparse) for fast inference on CPUs:
```bash
pip install deepsparse-nightly[llm]
```
Run in a [Python pipeline](https://github.com/neuralmagic/deepsparse/blob/main/docs/llms/text-generation-pipeline.md):
```python
from deepsparse import TextGeneration
prompt = "How to make banana bread?"
formatted_prompt = f"### User:\n{prompt}\n\n### Assistant:\n"
model = TextGeneration(model_path="hf:nm-testing/Nous-Hermes-2-SOLAR-10.7B-pruned50-ds")
print(model(formatted_prompt, max_new_tokens=500).generations[0].text)
"""
To make banana bread, you will need the following ingredients:
- 3 cups of flour
- 1 teaspoon of baking soda
- 1/2 teaspoon of salt
- 1/2 cup of butter (softened)
- 1/2 cup of sugar
- 2 eggs
- 1 1/2 cups of mashed ripe bananas
- 1 teaspoon of vanilla extract
Here's how to make banana bread:
1. Preheat your oven to 350Β°F (175Β°C).
2. In a bowl, mix together the flour, baking soda, and salt.
3. In another bowl, cream together the butter and sugar until light and fluffy.
4. Add the eggs one at a time, beating well after each addition.
5. Stir in the mashed bananas and vanilla extract.
6. Gradually stir the flour mixture into the banana mixture.
7. Pour the batter into a greased loaf pan.
8. Bake for 1 hour, or until a toothpick inserted in the center comes out clean.
9. Allow the bread to cool in the pan for 10 minutes before removing it.
Enjoy your delicious banana bread!
"""
```
## Prompt template
```
### User:\n
{prompt}
### Assistant:\n
```
## Sparsification
For details on how this model was sparsified, see the `recipe.yaml` in this repo and follow the instructions below.
```bash
git clone https://github.com/neuralmagic/sparseml
pip install -e "sparseml[transformers]"
python sparseml/src/sparseml/transformers/sparsification/obcq/obcq.py NousResearch/Nous-Hermes-2-SOLAR-10.7B open_platypus --recipe recipe.yaml --save True
python sparseml/src/sparseml/transformers/sparsification/obcq/export.py --task text-generation --model_path obcq_deployment
cp deployment/model.onnx deployment/model-orig.onnx
```
Run this kv-cache injection to speed up the model at inference by caching the Key and Value states:
```python
import os
import onnx
from sparseml.exporters.kv_cache_injector import KeyValueCacheInjector
input_file = "deployment/model-orig.onnx"
output_file = "deployment/model.onnx"
model = onnx.load(input_file, load_external_data=False)
model = KeyValueCacheInjector(model_path=os.path.dirname(input_file)).apply(model)
onnx.save(model, output_file)
print(f"Modified model saved to: {output_file}")
```
Follow the instructions on our [One Shot With SparseML](https://github.com/neuralmagic/sparseml/tree/main/src/sparseml/transformers/sparsification/obcq) page for a step-by-step guide for performing one-shot quantization of large language models.
## Slack
For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ) |
biennh/whisper-large-v3-vi | biennh | 2024-01-12T07:35:31Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-large-v3",
"base_model:adapter:openai/whisper-large-v3",
"region:us"
] | null | 2024-01-06T07:50:56Z | ---
library_name: peft
base_model: openai/whisper-large-v3
---
# 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 0.7.1 |
Kooten/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss-3.5bpw-exl2 | Kooten | 2024-01-12T07:32:02Z | 13 | 5 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-10T10:20:29Z | ---
license: cc-by-nc-4.0
---
# Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss 3.5bpw
Exllama quant of [NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss)
You will need 24gb of vram to run this model at about half context (16k, you can probably go a bit higher too)
### Prompt format: Chatml
```
<|im_start|>system
{sysprompt}<|im_end|>
<|im_start|>user
{input}<|im_end|>
<|im_start|>assistant
{output}<|im_end|>
```
### Contact
Kooten on discord.
|
ByunByun/qlora-koalpaca-polyglot-12.8b-1epoch-positive_data | ByunByun | 2024-01-12T07:31:33Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | null | 2024-01-12T07:31:26Z | ---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# 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 |
itzzdeep/youtube-thumbnails-sdxl-lora-v3 | itzzdeep | 2024-01-12T07:24:50Z | 1,497 | 2 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-01-12T05:57:54Z | ---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'instance_prompt'
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: instance_prompt
license: openrail++
---
# SDXL LoRA DreamBooth - itzzdeep/youtube-thumbnails-sdxl-lora-v3
<Gallery />
## Model description
### These are itzzdeep/youtube-thumbnails-sdxl-lora-v3 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`youtube-thumbnails-sdxl-lora-v3.safetensors` here πΎ](/itzzdeep/youtube-thumbnails-sdxl-lora-v3/blob/main/youtube-thumbnails-sdxl-lora-v3.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:youtube-thumbnails-sdxl-lora-v3:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`youtube-thumbnails-sdxl-lora-v3_emb.safetensors` here πΎ](/itzzdeep/youtube-thumbnails-sdxl-lora-v3/blob/main/youtube-thumbnails-sdxl-lora-v3_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `youtube-thumbnails-sdxl-lora-v3_emb` to your prompt. For example, `instance_prompt`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [𧨠diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('itzzdeep/youtube-thumbnails-sdxl-lora-v3', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='itzzdeep/youtube-thumbnails-sdxl-lora-v3', filename='youtube-thumbnails-sdxl-lora-v3_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('instance_prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` β use `<s0><s1>` in your prompt
## Details
All [Files & versions](/itzzdeep/youtube-thumbnails-sdxl-lora-v3/tree/main).
The weights were trained using [𧨠diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
igorshmel/irina-shmel-best-base | igorshmel | 2024-01-12T07:24:00Z | 3 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-01-11T06:58:34Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
Promt: rwxrwx, candid woman, gorgious face
Promt: rwxrwx, photo of amazing woman, gorgious face
Steps: 25, Sampler: DPM++ SDE Karras, CFG scale: 4.5
|
MaziyarPanahi/speechless-mistral-six-in-one-7b-orth-1.0-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T07:17:41Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"uukuguy/speechless-mistral-six-in-one-7b-orth-1.0",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T07:12:38Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- uukuguy/speechless-mistral-six-in-one-7b-orth-1.0
---
# speechless-mistral-six-in-one-7b-orth-1.0-Mistral-7B-Instruct-v0.2-slerp
speechless-mistral-six-in-one-7b-orth-1.0-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [uukuguy/speechless-mistral-six-in-one-7b-orth-1.0](https://huggingface.co/uukuguy/speechless-mistral-six-in-one-7b-orth-1.0)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: uukuguy/speechless-mistral-six-in-one-7b-orth-1.0
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/speechless-mistral-six-in-one-7b-orth-1.0-Mistral-7B-Instruct-v0.2-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"])
``` |
MaziyarPanahi/typhoon-7b-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T07:05:30Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"scb10x/typhoon-7b",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T07:00:27Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- scb10x/typhoon-7b
---
# typhoon-7b-Mistral-7B-Instruct-v0.2-slerp
typhoon-7b-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [scb10x/typhoon-7b](https://huggingface.co/scb10x/typhoon-7b)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: scb10x/typhoon-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/typhoon-7b-Mistral-7B-Instruct-v0.2-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"])
``` |
walebadr/Mistral-7B-v0.1-DPO | walebadr | 2024-01-12T06:49:05Z | 1,393 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-10T16:54:54Z | ---
license: apache-2.0
---
Mistral-7b-v0.1-DPO is a finetuned adapter from the original Mistral-7b model. In this adaptor, I am finetuning the LM head in addition to the regular modules that are normally finetuned. Below is the list of the finetuned modules:
'k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj', 'lm_head'
|
MaziyarPanahi/Rabbit-7B-DPO-Chat-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T06:46:58Z | 20 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"viethq188/Rabbit-7B-DPO-Chat",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T06:41:39Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- viethq188/Rabbit-7B-DPO-Chat
---
# Rabbit-7B-DPO-Chat-Mistral-7B-Instruct-v0.2-slerp
Rabbit-7B-DPO-Chat-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [viethq188/Rabbit-7B-DPO-Chat](https://huggingface.co/viethq188/Rabbit-7B-DPO-Chat)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: viethq188/Rabbit-7B-DPO-Chat
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Rabbit-7B-DPO-Chat-Mistral-7B-Instruct-v0.2-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"])
``` |
GreenNode/GreenNode-mini-7B-multilingual-v1olet | GreenNode | 2024-01-12T06:45:37Z | 15 | 17 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-12T15:15:31Z | ---
license: apache-2.0
language:
- en
---
*[December 2023]* We are ranked *4th* on the overall leaderboard and **1st** in the 7B leaderboard!
π₯π₯π₯π₯
DPO from the model ranked *6th* on the overall leaderboard and **1st** in the 7B leaderboard (12th December 2023) - v1olet/v1olet_marcoroni-go-bruins-merge-7B
https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B
Training data:
comparison_gpt4_en,en_orca_dpo
Train for 1 epoch.
You can use alpaca template.
```
template_format = """{system}
### Instruction:
{prompt}
### Response:
"""
```
Developed by: Trong-Hieu Nguyen-Mau
|
MaziyarPanahi/zephykor-ko-beta-7b-chang-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T06:37:09Z | 26 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"lcw99/zephykor-ko-beta-7b-chang",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T06:31:58Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- lcw99/zephykor-ko-beta-7b-chang
---
# zephykor-ko-beta-7b-chang-Mistral-7B-Instruct-v0.2-slerp
zephykor-ko-beta-7b-chang-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [lcw99/zephykor-ko-beta-7b-chang](https://huggingface.co/lcw99/zephykor-ko-beta-7b-chang)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: lcw99/zephykor-ko-beta-7b-chang
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/zephykor-ko-beta-7b-chang-Mistral-7B-Instruct-v0.2-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"])
``` |
ntc-ai/SDXL-LoRA-slider.the-feeling-of-achieving-max-power | ntc-ai | 2024-01-12T06:20:02Z | 84 | 1 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | text-to-image | 2024-01-12T06:19:59Z |
---
language:
- en
thumbnail: "images/evaluate/the feeling of achieving max power.../the feeling of achieving max power_17_3.0.png"
widget:
- text: the feeling of achieving max power
output:
url: images/the feeling of achieving max power_17_3.0.png
- text: the feeling of achieving max power
output:
url: images/the feeling of achieving max power_19_3.0.png
- text: the feeling of achieving max power
output:
url: images/the feeling of achieving max power_20_3.0.png
- text: the feeling of achieving max power
output:
url: images/the feeling of achieving max power_21_3.0.png
- text: the feeling of achieving max power
output:
url: images/the feeling of achieving max power_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "the feeling of achieving max power"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - the feeling of achieving max power (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/the feeling of achieving max power_17_-3.0.png" width=256 height=256 /> | <img src="images/the feeling of achieving max power_17_0.0.png" width=256 height=256 /> | <img src="images/the feeling of achieving max power_17_3.0.png" width=256 height=256 /> |
| <img src="images/the feeling of achieving max power_19_-3.0.png" width=256 height=256 /> | <img src="images/the feeling of achieving max power_19_0.0.png" width=256 height=256 /> | <img src="images/the feeling of achieving max power_19_3.0.png" width=256 height=256 /> |
| <img src="images/the feeling of achieving max power_20_-3.0.png" width=256 height=256 /> | <img src="images/the feeling of achieving max power_20_0.0.png" width=256 height=256 /> | <img src="images/the feeling of achieving max power_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
the feeling of achieving max power
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.the-feeling-of-achieving-max-power', weight_name='the feeling of achieving max power.safetensors', adapter_name="the feeling of achieving max power")
# Activate the LoRA
pipe.set_adapters(["the feeling of achieving max power"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, the feeling of achieving max power"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1040+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
MaziyarPanahi/japanese-stablelm-base-gamma-7b-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T06:13:57Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"stabilityai/japanese-stablelm-base-gamma-7b",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T06:08:59Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- stabilityai/japanese-stablelm-base-gamma-7b
---
# japanese-stablelm-base-gamma-7b-Mistral-7B-Instruct-v0.2-slerp
japanese-stablelm-base-gamma-7b-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [stabilityai/japanese-stablelm-base-gamma-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: stabilityai/japanese-stablelm-base-gamma-7b
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/japanese-stablelm-base-gamma-7b-Mistral-7B-Instruct-v0.2-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"])
``` |
KANC/melllll | KANC | 2024-01-12T06:04:16Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2024-01-12T06:04:16Z | ---
license: bigscience-openrail-m
---
|
MaziyarPanahi/v1olet_merged_dpo_7B-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T06:03:44Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"v1olet/v1olet_merged_dpo_7B",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T05:58:44Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- v1olet/v1olet_merged_dpo_7B
---
# v1olet_merged_dpo_7B-Mistral-7B-Instruct-v0.2-slerp
v1olet_merged_dpo_7B-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [v1olet/v1olet_merged_dpo_7B](https://huggingface.co/v1olet/v1olet_merged_dpo_7B)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: v1olet/v1olet_merged_dpo_7B
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/v1olet_merged_dpo_7B-Mistral-7B-Instruct-v0.2-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"])
``` |
matbee/Llama-2-13b-chat-hf-sft-adapters | matbee | 2024-01-12T05:34:21Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null | 2024-01-12T05:33:35Z | ---
library_name: peft
base_model: meta-llama/Llama-2-13b-chat-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 0.7.1 |
letingliu/holder_type2 | letingliu | 2024-01-12T05:34:15Z | 50 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-01-12T05:28:35Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: letingliu/holder_type2
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. -->
# letingliu/holder_type2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4652
- Validation Loss: 0.4554
- Train Accuracy: 0.9333
- Epoch: 19
## 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': 35, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.6797 | 0.6563 | 0.8583 | 0 |
| 0.6380 | 0.5999 | 0.8833 | 1 |
| 0.5750 | 0.5293 | 0.9 | 2 |
| 0.5168 | 0.4771 | 0.925 | 3 |
| 0.4718 | 0.4554 | 0.9333 | 4 |
| 0.4703 | 0.4554 | 0.9333 | 5 |
| 0.4732 | 0.4554 | 0.9333 | 6 |
| 0.4659 | 0.4554 | 0.9333 | 7 |
| 0.4621 | 0.4554 | 0.9333 | 8 |
| 0.4751 | 0.4554 | 0.9333 | 9 |
| 0.4686 | 0.4554 | 0.9333 | 10 |
| 0.4647 | 0.4554 | 0.9333 | 11 |
| 0.4735 | 0.4554 | 0.9333 | 12 |
| 0.4699 | 0.4554 | 0.9333 | 13 |
| 0.4719 | 0.4554 | 0.9333 | 14 |
| 0.4701 | 0.4554 | 0.9333 | 15 |
| 0.4672 | 0.4554 | 0.9333 | 16 |
| 0.4561 | 0.4554 | 0.9333 | 17 |
| 0.4717 | 0.4554 | 0.9333 | 18 |
| 0.4652 | 0.4554 | 0.9333 | 19 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
pranitamahajan/Falcon7bsharded | pranitamahajan | 2024-01-12T05:33:54Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"base_model:finetune:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2024-01-12T05:33:14Z | ---
license: apache-2.0
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
tags:
- generated_from_trainer
model-index:
- name: Falcon7bsharded
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. -->
# Falcon7bsharded
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 10
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
TitanTec/SpaceInvadersNoFrameskip-v4-T2 | TitanTec | 2024-01-12T05:27:17Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-01-12T05:26:46Z | ---
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: 334.50 +/- 159.73
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 TitanTec -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 TitanTec -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 TitanTec
```
## Hyperparameters
```python
OrderedDict([('batch_size', 128),
('buffer_size', 200000),
('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', 1e-05),
('learning_starts', 100000),
('n_timesteps', 200000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('replay_buffer_kwargs', {'handle_timeout_termination': False}),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
MaziyarPanahi/LeoScorpius-7B-Chat-DPO-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T05:23:52Z | 25 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"viethq188/LeoScorpius-7B-Chat-DPO",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T05:18:43Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- viethq188/LeoScorpius-7B-Chat-DPO
---
# LeoScorpius-7B-Chat-DPO-Mistral-7B-Instruct-v0.2-slerp
LeoScorpius-7B-Chat-DPO-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [viethq188/LeoScorpius-7B-Chat-DPO](https://huggingface.co/viethq188/LeoScorpius-7B-Chat-DPO)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: viethq188/LeoScorpius-7B-Chat-DPO
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/LeoScorpius-7B-Chat-DPO-Mistral-7B-Instruct-v0.2-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"])
``` |
wesley7137/llama-2-7B-AddictionCounseling-merged | wesley7137 | 2024-01-12T05:08:39Z | 2 | 1 | peft | [
"peft",
"region:us"
] | null | 2024-01-12T05:08:35Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
|
Chae/results | Chae | 2024-01-12T04:46:30Z | 61 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-01-12T01:42:12Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5909
- Exact Match: 5.7224
- F1: 10.3127
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------:|:-------:|
| No log | 1.0 | 380 | 2.7182 | 3.5093 | 8.2470 |
| 2.8171 | 2.0 | 760 | 2.5752 | 5.3430 | 10.5520 |
| 2.3508 | 3.0 | 1140 | 2.5909 | 5.7224 | 10.3127 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
NhatTranKKK/Taxi-v3 | NhatTranKKK | 2024-01-12T04:46:21Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-01-12T04:46:20Z | ---
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: 7.44 +/- 2.79
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="NhatTranKKK/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"])
```
|
g8nz/stable-diffusion-x4-upscaler | g8nz | 2024-01-12T04:45:48Z | 2 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"arxiv:2112.10752",
"arxiv:2202.00512",
"arxiv:1910.09700",
"license:openrail++",
"diffusers:StableDiffusionUpscalePipeline",
"region:us"
] | null | 2024-01-12T04:10:46Z | ---
license: openrail++
tags:
- stable-diffusion
inference: false
---
# Stable Diffusion x4 upscaler model card
This model card focuses on the model associated with the Stable Diffusion Upscaler, available [here](https://github.com/Stability-AI/stablediffusion).
This model is trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `x4-upscaler-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.ckpt).
- Use it with 𧨠[`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler#examples)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
Using the [π€'s Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
```
```python
import requests
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionUpscalePipeline
import torch
# load model and scheduler
model_id = "stabilityai/stable-diffusion-x4-upscaler"
pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipeline = pipeline.to("cuda")
# let's download an image
url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))
prompt = "a white cat"
upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0]
upscaled_image.save("upsampled_cat.png")
```
**Notes**:
- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to βA red cube on top of a blue sphereβ
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a subset of the large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
**Training Procedure**
Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
We currently provide the following checkpoints:
- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized.
- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).
- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 200000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T04:44:51Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"uukuguy/airoboros-m-7b-3.1.2-dare-0.85",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T04:39:51Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- uukuguy/airoboros-m-7b-3.1.2-dare-0.85
---
# airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.2-slerp
airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [uukuguy/airoboros-m-7b-3.1.2-dare-0.85](https://huggingface.co/uukuguy/airoboros-m-7b-3.1.2-dare-0.85)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: uukuguy/airoboros-m-7b-3.1.2-dare-0.85
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.2-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"])
``` |
wesley7137/TinyLllama-OpenHermes-MOE-Psyhchologist-Expert | wesley7137 | 2024-01-12T04:40:30Z | 0 | 0 | peft | [
"peft",
"llama",
"region:us"
] | null | 2024-01-12T02:45:21Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
wesley7137/TinyLlama-OpenHermes-MOE-MetaMath-Expert | wesley7137 | 2024-01-12T04:40:19Z | 0 | 0 | peft | [
"peft",
"llama",
"region:us"
] | null | 2024-01-12T02:40:44Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
flemmingmiguel/NeuDist-Ro-7B | flemmingmiguel | 2024-01-12T04:34:11Z | 1,381 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"argilla/distilabeled-Marcoro14-7B-slerp",
"mlabonne/NeuralMarcoro14-7B",
"conversational",
"dataset:mlabonne/chatml_dpo_pairs",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T01:11:06Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- argilla/distilabeled-Marcoro14-7B-slerp
- mlabonne/NeuralMarcoro14-7B
datasets:
- mlabonne/chatml_dpo_pairs
- argilla/distilabel-intel-orca-dpo-pairs
---
# NeuDist-Ro-7B
NeuDist-Ro-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp)
* [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)
As an experiment to find the best base merge to further fine-tuning, expect a lot of experiments named using parts of the component models until a clear winner emerges in the benchmarks
In this case merging 2 DPOs of the same model
## 𧩠Configuration
```yaml
slices:
- sources:
- model: argilla/distilabeled-Marcoro14-7B-slerp
layer_range: [0, 32]
- model: mlabonne/NeuralMarcoro14-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/NeuralMarcoro14-7B
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "flemmingmiguel/NeuDist-Ro-7B"
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"])
``` |
MaziyarPanahi/WizardMath-7B-V1.1-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T04:34:02Z | 24 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"WizardLM/WizardMath-7B-V1.1",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T04:28:46Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- WizardLM/WizardMath-7B-V1.1
---
# WizardMath-7B-V1.1-Mistral-7B-Instruct-v0.2-slerp
WizardMath-7B-V1.1-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: WizardLM/WizardMath-7B-V1.1
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/WizardMath-7B-V1.1-Mistral-7B-Instruct-v0.2-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"])
``` |
ryusangwon/3118_Llama-2-13b-hf | ryusangwon | 2024-01-12T04:32:54Z | 1 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"dataset:cnn_dailymail",
"base_model:meta-llama/Llama-2-13b-hf",
"base_model:adapter:meta-llama/Llama-2-13b-hf",
"region:us"
] | null | 2024-01-12T04:32:46Z | ---
base_model: meta-llama/Llama-2-13b-hf
tags:
- generated_from_trainer
datasets:
- cnn_dailymail
model-index:
- name: 3118_Llama-2-13b-hf
results: []
library_name: peft
---
<!-- 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. -->
# 3118_Llama-2-13b-hf
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on the cnn_dailymail dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.4.0
- Transformers 4.36.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T04:19:15Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"uukuguy/speechless-code-mistral-7b-v2.0",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T04:14:17Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- uukuguy/speechless-code-mistral-7b-v2.0
---
# speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.2-slerp
speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [uukuguy/speechless-code-mistral-7b-v2.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v2.0)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: uukuguy/speechless-code-mistral-7b-v2.0
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.2-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"])
``` |
gustavokpc/IC_quarto | gustavokpc | 2024-01-12T04:14:18Z | 46 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"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 | 2023-10-21T15:20:31Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: gustavokpc/IC_quarto
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. -->
# gustavokpc/IC_quarto
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1061
- Train Accuracy: 0.9634
- Train F1 M: 0.5432
- Train Precision M: 0.3978
- Train Recall M: 0.9159
- Validation Loss: 0.2101
- Validation Accuracy: 0.9235
- Validation F1 M: 0.5596
- Validation Precision M: 0.4070
- Validation Recall M: 0.9389
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2274, '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 | Train Accuracy | Train F1 M | Train Precision M | Train Recall M | Validation Loss | Validation Accuracy | Validation F1 M | Validation Precision M | Validation Recall M | Epoch |
|:----------:|:--------------:|:----------:|:-----------------:|:--------------:|:---------------:|:-------------------:|:---------------:|:----------------------:|:-------------------:|:-----:|
| 0.3469 | 0.8494 | 0.4233 | 0.3470 | 0.6216 | 0.2535 | 0.8945 | 0.5613 | 0.4145 | 0.9125 | 0 |
| 0.1742 | 0.9335 | 0.5237 | 0.3895 | 0.8572 | 0.2315 | 0.9017 | 0.5765 | 0.4256 | 0.9353 | 1 |
| 0.1061 | 0.9634 | 0.5432 | 0.3978 | 0.9159 | 0.2101 | 0.9235 | 0.5596 | 0.4070 | 0.9389 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
MaziyarPanahi/neural-chat-7b-v3-1-dare-0.85-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T04:10:25Z | 25 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"uukuguy/neural-chat-7b-v3-1-dare-0.85",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T04:05:23Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- uukuguy/neural-chat-7b-v3-1-dare-0.85
---
# neural-chat-7b-v3-1-dare-0.85-Mistral-7B-Instruct-v0.2-slerp
neural-chat-7b-v3-1-dare-0.85-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [uukuguy/neural-chat-7b-v3-1-dare-0.85](https://huggingface.co/uukuguy/neural-chat-7b-v3-1-dare-0.85)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: uukuguy/neural-chat-7b-v3-1-dare-0.85
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/neural-chat-7b-v3-1-dare-0.85-Mistral-7B-Instruct-v0.2-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"])
``` |
Wajid333/PPO-Practice | Wajid333 | 2024-01-12T04:05:07Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-01-12T04:03:32Z | ---
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: 251.58 +/- 20.52
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
...
```
|
edfraga/diablo_gpt | edfraga | 2024-01-12T04:00:47Z | 98 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:hakurei/open-instruct-v1",
"base_model:microsoft/DialoGPT-medium",
"base_model:finetune:microsoft/DialoGPT-medium",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-05T13:33:39Z | ---
license: mit
base_model: microsoft/DialoGPT-medium
tags:
- generated_from_trainer
model-index:
- name: diablo_gpt
results: []
datasets:
- hakurei/open-instruct-v1
---
<!-- 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. -->
# diablo_gpt
This model is a fine-tuned version of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) on [hakurei/open-instruct-v1 dataset](https://huggingface.co/datasets/hakurei/open-instruct-v1).
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0 |
amd/rcan | amd | 2024-01-12T03:54:30Z | 0 | 0 | null | [
"onnx",
"RyzenAI",
"Super Resolution",
"Pytorch",
"Vision",
"SISR",
"en",
"dataset:Set5",
"dataset:Div2k",
"arxiv:1807.02758",
"license:apache-2.0",
"region:us"
] | null | 2023-12-04T16:30:53Z | ---
license: apache-2.0
tags:
- RyzenAI
- Super Resolution
- Pytorch
- Vision
- SISR
datasets:
- Set5
- Div2k
language:
- en
Metircs:
- PSNR
---
# RCAN model trained on DIV2K
RCAN is a very deep residual channel attention network for super resolution trained on DIV2K. It was introduced in the paper [Image Super-Resolution Using Very Deep Residual Channel Attention Networks in 2018](https://arxiv.org/abs/1807.02758) by Yulun Zhang et al. and first released in [this repository](https://github.com/yulunzhang/RCAN).
We develop a modified version that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com).
## Model description
RCAN is an advanced algorithm for single image super resolution. Our modified version is smaller than the original version. It is based deep learning techniques and is capable of X2 super resolution.
## Intended uses & limitations
You can use the raw model for super resolution. See the [model hub](https://huggingface.co/models?sort=trending&search=amd%2Frcan) to look for all available RCAN models.
## How to use
### Installation
Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
Run the following script to install pre-requisites for this model.
```bash
pip install -r requirements.txt
```
### Data Preparation (optional: for accuracy evaluation)
1. Download the benchmark(https://cv.snu.ac.kr/research/EDSR/benchmark.tar) dataset.
2. Organize the dataset directory as follows:
```Plain
βββ dataset
βββ benchmark
βββ Set5
βββ HR
| βββ baby.png
| βββ ...
βββ LR_bicubic
βββX2
βββbabyx2.png
βββ ...
βββ Set14
βββ ...
```
### Test & Evaluation
- Code snippet from [`infer_onnx.py`](infer_onnx.py) on how to use
```python
parser = argparse.ArgumentParser(description='RCAN SISR')
parser.add_argument('--onnx_path', type=str, default='RCAN_int8_NHWC.onnx',
help='onnx path')
parser.add_argument('--image_path', default='test_data/test.png',
help='path of your image')
parser.add_argument('--output_path', default='test_data/sr.png',
help='path of your image')
parser.add_argument('--ipu', action='store_true',
help='use ipu')
parser.add_argument('--provider_config', type=str, default=None,
help='provider config path')
args = parser.parse_args()
if args.ipu:
providers = ["VitisAIExecutionProvider"]
provider_options = [{"config_file": args.provider_config}]
else:
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
provider_options = None
onnx_file_name = args.onnx_path
image_path = args.image_path
output_path = args.output_path
ort_session = onnxruntime.InferenceSession(onnx_file_name, providers=providers, provider_options=provider_options)
lr = cv2.imread(image_path)[np.newaxis,:,:,:].transpose((0,3,1,2)).astype(np.float32)
sr = tiling_inference(ort_session, lr, 8, (56, 56))
sr = np.clip(sr, 0, 255)
sr = sr.squeeze().transpose((1,2,0)).astype(np.uint8)
sr = cv2.imwrite(output_path, sr)
```
- Run inference for a single image
```python
python infer_onnx.py --onnx_path RCAN_int8_NHWC.onnx --image_path /Path/To/Your/Image --ipu --provider_config Path/To/vaip_config.json
```
- Test accuracy of the quantized model
```python
python eval_onnx.py --onnx_path RCAN_int8_NHWC.onnx --data_test Set5 --ipu --provider_config Path/To/vaip_config.json
```
### Performance
| Method | Scale | Flops | Set5 |
|------------|-------|-------|--------------|
|RCAN-S (float) |X2 |24.5G |37.531 / 0.958|
|RCAN-S (INT8) |X2 |24.5G |37.150 / 0.955|
- Note: the Flops is calculated with the output resolution is 360x640
```bibtex
@inproceedings{zhang2018image,
title={Image super-resolution using very deep residual channel attention networks},
author={Zhang, Yulun and Li, Kunpeng and Li, Kai and Wang, Lichen and Zhong, Bineng and Fu, Yun},
booktitle={Proceedings of the European conference on computer vision (ECCV)},
pages={286--301},
year={2018}
}
```
|
MayaPH/GodziLLa2-70B | MayaPH | 2024-01-12T03:52:58Z | 1,591 | 38 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"merge",
"mix",
"cot",
"dataset:mlabonne/guanaco-llama2-1k",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:1803.05457",
"arxiv:1905.07830",
"arxiv:2109.07958",
"arxiv:1907.10641",
"arxiv:2110.14168",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-08-10T17:05:37Z | ---
pipeline_tag: text-generation
license: llama2
inference: false
tags:
- merge
- mix
- cot
datasets:
- mlabonne/guanaco-llama2-1k
---
Released August 11, 2023
## Model Description
GodziLLa 2 70B is an experimental combination of various proprietary LoRAs from Maya Philippines and [Guanaco LLaMA 2 1K dataset](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k), with LLaMA 2 70B. This model's primary purpose is to stress test the limitations of composite, instruction-following LLMs and observe its performance with respect to other LLMs available on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). This model debuted in the leaderboard at rank #4 (August 17, 2023), debuted in the Fall 2023 update at rank #2 (November, 10, 2023), and operates under the Llama 2 license.
## Open LLM Leaderboard Metrics (Fall 2023 update)
| Metric | Value |
|-----------------------|-------|
| MMLU (5-shot) | 69.88 |
| ARC (25-shot) | 71.42 |
| HellaSwag (10-shot) | 87.53 |
| TruthfulQA (0-shot) | 61.54 |
| Winogrande (5-shot) | 83.19 |
| GSM8K (5-shot) | 43.21 |
| DROP (3-shot) | 52.31 |
| Average (w/ DROP) | 67.01 |
| Average (w/o DROP) | 69.46 |
Note: As of December 1, 2023, [DROP](https://arxiv.org/abs/1903.00161) is removed from the leaderboard benchmarks.
According to the leaderboard description, here are the benchmarks used for the evaluation:
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - a test to measure a text modelβs multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more.
- [AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) -ARC- (25-shot) - a set of grade-school science questions.
- [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - a test of commonsense inference, which is easy for humans (~95%) but challenging for SOTA models.
- [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - a test to measure a modelβs propensity to reproduce falsehoods commonly found online.
- [Winogrande](https://arxiv.org/abs/1907.10641) (5-shot) - an adversarial and difficult Winograd benchmark at scale, for commonsense reasoning.
- [GSM8k](https://arxiv.org/abs/2110.14168) (5-shot) - diverse grade school math word problems to measure a model's ability to solve multi-step mathematical reasoning problems.
- [DROP](https://arxiv.org/abs/1903.00161) (3-shot) - English reading comprehension benchmark requiring Discrete Reasoning Over the content of Paragraphs.
A detailed breakdown of the evaluation can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MayaPH__GodziLLa2-70B). Huge thanks to [@thomwolf](https://huggingface.co/thomwolf).
## Open LLM Leaderboard Metrics (before Fall 2023 update)
| Metric | Value |
|-----------------------|-------|
| MMLU (5-shot) | 69.88 |
| ARC (25-shot) | 71.42 |
| HellaSwag (10-shot) | 87.53 |
| TruthfulQA (0-shot) | 61.54 |
| Average | 72.59 |
## Leaderboard Highlights (Fall 2023 update, November 10, 2023)
- Godzilla 2 70B debuts at 2nd place worldwide in the newly updated Open LLM Leaderboard.
- Godzilla 2 70B beats GPT-3.5 (ChatGPT) in terms of average performance and the HellaSwag benchmark (87.53 > 85.5).
- Godzilla 2 70B outperforms GPT-3.5 (ChatGPT) and GPT-4 on the TruthfulQA benchmark (61.54 for G2-70B, 47 for GPT-3.5, 59 for GPT-4).
- Godzilla 2 70B is on par with GPT-3.5 (ChatGPT) on the MMLU benchmark (<0.12%).
*Based on a [leaderboard clone](https://huggingface.co/spaces/gsaivinay/open_llm_leaderboard) with GPT-3.5 and GPT-4 included.
### Reproducing Evaluation Results
*Instruction template taken from [Platypus 2 70B instruct](https://huggingface.co/garage-bAInd/Platypus2-70B-instruct).
Install LM Evaluation Harness:
```
# clone repository
git clone https://github.com/EleutherAI/lm-evaluation-harness.git
# change to repo directory
cd lm-evaluation-harness
# check out the correct commit
git checkout b281b0921b636bc36ad05c0b0b0763bd6dd43463
# install
pip install -e .
```
ARC:
```
python main.py --model hf-causal-experimental --model_args pretrained=MayaPH/GodziLLa2-70B --tasks arc_challenge --batch_size 1 --no_cache --write_out --output_path results/G270B/arc_challenge_25shot.json --device cuda --num_fewshot 25
```
HellaSwag:
```
python main.py --model hf-causal-experimental --model_args pretrained=MayaPH/GodziLLa2-70B --tasks hellaswag --batch_size 1 --no_cache --write_out --output_path results/G270B/hellaswag_10shot.json --device cuda --num_fewshot 10
```
MMLU:
```
python main.py --model hf-causal-experimental --model_args pretrained=MayaPH/GodziLLa2-70B --tasks hendrycksTest-* --batch_size 1 --no_cache --write_out --output_path results/G270B/mmlu_5shot.json --device cuda --num_fewshot 5
```
TruthfulQA:
```
python main.py --model hf-causal-experimental --model_args pretrained=MayaPH/GodziLLa2-70B --tasks truthfulqa_mc --batch_size 1 --no_cache --write_out --output_path results/G270B/truthfulqa_0shot.json --device cuda
```
### Prompt Template
```
### Instruction:
<prompt> (without the <>)
### Response:
```
## Technical Considerations
When using GodziLLa 2 70B, kindly take note of the following:
- The default precision is `fp32`, and the total file size that would be loaded onto the RAM/VRAM is around 275 GB. Consider using a lower precision (fp16, int8, int4) to save memory.
- To further save on memory, set the `low_cpu_mem_usage` argument to True.
- If you wish to use a quantized version of GodziLLa2-70B, you can either access TheBloke's [GPTQ](https://huggingface.co/TheBloke/GodziLLa2-70B-GPTQ) or [GGML](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML) version of GodziLLa2-70B.
- [GodziLLa2-70B-GPTQ](https://huggingface.co/TheBloke/GodziLLa2-70B-GPTQ#description) is available in 4-bit and 3-bit
- [GodziLLa2-70B-GGML](https://huggingface.co/TheBloke/GodziLLa2-70B-GGML#provided-files) is available in 8-bit, 6-bit, 5-bit, 4-bit, 3-bit, and 2-bit
## Ethical Considerations
When using GodziLLa 2 70B, it is important to consider the following ethical considerations:
1. **Privacy and Security:** Avoid sharing sensitive personal information while interacting with the model. The model does not have privacy safeguards, so exercise caution when discussing personal or confidential matters.
2. **Fairness and Bias:** The model's responses may reflect biases present in the training data. Be aware of potential biases and make an effort to evaluate responses critically and fairly.
3. **Transparency:** The model operates as a predictive text generator based on patterns learned from the training data. The model's inner workings and the specific training data used are proprietary and not publicly available.
4. **User Responsibility:** Users should take responsibility for their own decisions and not solely rely on the information provided by the model. Consult with the appropriate professionals or reliable sources for specific advice or recommendations.
5. **NSFW Content:** The model is a merge of various datasets and LoRA adapters. It is highly likely that the resulting model contains uncensored content that may include, but is not limited to, violence, gore, explicit language, and sexual content. If you plan to further refine this model for safe/aligned usage, you are highly encouraged to implement guardrails along with it.
## Further Information
For additional information or inquiries about GodziLLa 2 70B, please contact the Maya Philippines iOps Team via [email protected].
## Disclaimer
GodziLLa 2 70B is an AI language model from Maya Philippines. It is provided "as is" without warranty of any kind, express or implied. The model developers and Maya Philippines shall not be liable for any direct or indirect damages arising from the use of this model.
## Acknowledgments
The development of GodziLLa 2 70B was made possible by Maya Philippines and the curation of the various proprietary datasets and creation of the different proprietary LoRA adapters. Special thanks to mlabonne for the Guanaco dataset found [here](https://huggingface.co/datasets/mlabonne/guanaco-llama2-1k). Last but not least, huge thanks to [TheBloke](https://huggingface.co/TheBloke) for the quantized models, making our model easily accessible to a wider community. |
Jaehyeon222/M-SOLAR-10.7B-v1.0-DPO | Jaehyeon222 | 2024-01-12T03:44:25Z | 2,247 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:maywell/ko_Ultrafeedback_binarized",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-05T01:08:14Z | ---
license: cc-by-nc-4.0
datasets:
- maywell/ko_Ultrafeedback_binarized
---
Model Card for M-SOLAR-10.7B-v1.0-DPO
Developed by : λ©κ°μ€ν°λκ΅μ‘, ν리λμ
, λ§μ΄μ€
Base Model : jjourney1125/M-SOLAR-10.7B-v1.0
μ¬μ© λ°μ΄ν°μ
: maywellλμ ko_Ultrafeedback_binarized λ°μ΄ν°μ
μ νμ©νμ΅λλ€. |
wesley7137/TinyLlama-OpenHermes-MOE-Logic-Expert | wesley7137 | 2024-01-12T03:43:04Z | 0 | 0 | peft | [
"peft",
"llama",
"region:us"
] | null | 2024-01-12T02:34:46Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T03:39:50Z | 25 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"uukuguy/SynthIA-7B-v1.3-dare-0.85",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T03:34:51Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- uukuguy/SynthIA-7B-v1.3-dare-0.85
---
# SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.2-slerp
SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [uukuguy/SynthIA-7B-v1.3-dare-0.85](https://huggingface.co/uukuguy/SynthIA-7B-v1.3-dare-0.85)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: uukuguy/SynthIA-7B-v1.3-dare-0.85
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.2-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"])
``` |
MaziyarPanahi/CollectiveCognition-v1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T03:29:17Z | 22 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"teknium/CollectiveCognition-v1-Mistral-7B",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T03:24:12Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/CollectiveCognition-v1-Mistral-7B
---
# CollectiveCognition-v1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp
CollectiveCognition-v1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [teknium/CollectiveCognition-v1-Mistral-7B](https://huggingface.co/teknium/CollectiveCognition-v1-Mistral-7B)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: teknium/CollectiveCognition-v1-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/CollectiveCognition-v1-Mistral-7B-Mistral-7B-Instruct-v0.2-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"])
``` |
quannguyenopus/temp | quannguyenopus | 2024-01-12T03:22:37Z | 49 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-12-09T03:12:22Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 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('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_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 7843 with parameters:
```
{'batch_size': 14, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 3e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 5000,
"weight_decay": 0.0001
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
ntc-ai/SDXL-LoRA-slider.11-10 | ntc-ai | 2024-01-12T03:19:44Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] | text-to-image | 2024-01-12T03:19:41Z |
---
language:
- en
thumbnail: "images/evaluate/11-10...hair down/11-10_17_3.0.png"
widget:
- text: 11-10
output:
url: images/11-10_17_3.0.png
- text: 11-10
output:
url: images/11-10_19_3.0.png
- text: 11-10
output:
url: images/11-10_20_3.0.png
- text: 11-10
output:
url: images/11-10_21_3.0.png
- text: 11-10
output:
url: images/11-10_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "11-10"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - 11-10 (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/11-10_17_-3.0.png" width=256 height=256 /> | <img src="images/11-10_17_0.0.png" width=256 height=256 /> | <img src="images/11-10_17_3.0.png" width=256 height=256 /> |
| <img src="images/11-10_19_-3.0.png" width=256 height=256 /> | <img src="images/11-10_19_0.0.png" width=256 height=256 /> | <img src="images/11-10_19_3.0.png" width=256 height=256 /> |
| <img src="images/11-10_20_-3.0.png" width=256 height=256 /> | <img src="images/11-10_20_0.0.png" width=256 height=256 /> | <img src="images/11-10_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
11-10
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.11-10', weight_name='11-10.safetensors', adapter_name="11-10")
# Activate the LoRA
pipe.set_adapters(["11-10"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, 11-10"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1040+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
loraxian/t5-small-ootl | loraxian | 2024-01-12T03:11:04Z | 92 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-01-12T02:47:24Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-ootl
results: []
pipeline_tag: text2text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-ootl
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9540
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 125 | 3.9870 |
| No log | 2.0 | 250 | 3.9657 |
| No log | 3.0 | 375 | 3.9567 |
| No log | 4.0 | 500 | 3.9540 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0 |
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_GrounTruth_withPrompt_Seed105 | behzadnet | 2024-01-12T03:09:15Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2024-01-12T03:09:13Z | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
bartowski/UNA-TheBeagle-7b-v1-exl2 | bartowski | 2024-01-12T03:07:52Z | 0 | 0 | transformers | [
"transformers",
"generated_from_trainer",
"text-generation",
"dataset:jondurbin/bagel-v0.3",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T02:50:54Z | ---
license: cc-by-nc-nd-4.0
tags:
- generated_from_trainer
model-index:
- name: UNA-TheBeagle-7b-v1
results: []
datasets:
- jondurbin/bagel-v0.3
library_name: transformers
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of UNA-TheBeagle-7b-v1
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.11">turboderp's ExLlamaV2 v0.0.11</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.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1
<a href="https://huggingface.co/bartowski/UNA-TheBeagle-7b-v1-exl2/tree/8_0">8.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/UNA-TheBeagle-7b-v1-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/UNA-TheBeagle-7b-v1-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/UNA-TheBeagle-7b-v1-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/UNA-TheBeagle-7b-v1-exl2/tree/3_5">3.5 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/UNA-TheBeagle-7b-v1-exl2
```
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 `UNA-TheBeagle-7b-v1-exl2`:
```shell
mkdir UNA-TheBeagle-7b-v1-exl2
huggingface-cli download bartowski/UNA-TheBeagle-7b-v1-exl2 --local-dir UNA-TheBeagle-7b-v1-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir UNA-TheBeagle-7b-v1-exl2
huggingface-cli download bartowski/UNA-TheBeagle-7b-v1-exl2 --revision 4_0 --local-dir UNA-TheBeagle-7b-v1-exl2 --local-dir-use-symlinks False
```
|
Deema/AceGPT-7B-alpagasus_ar | Deema | 2024-01-12T02:58:11Z | 10 | 0 | peft | [
"peft",
"pytorch",
"llama",
"text-generation",
"ar",
"dataset:arbml/alpagasus_cleaned_ar",
"region:us"
] | text-generation | 2024-01-12T02:43:32Z | ---
library_name: peft
language:
- ar
datasets:
- arbml/alpagasus_cleaned_ar
pipeline_tag: text-generation
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0 |
FeiiYin/lora-trained-xl-audi4 | FeiiYin | 2024-01-12T02:56:29Z | 1 | 1 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-01-12T02:51:16Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'A photo of sks car on the street'
output:
url:
"image_0.png"
- text: 'A photo of sks car on the street'
output:
url:
"image_1.png"
- text: 'A photo of sks car on the street'
output:
url:
"image_2.png"
- text: 'A photo of sks car on the street'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks car
license: openrail++
---
# SDXL LoRA DreamBooth - FeiiYin/lora-trained-xl-audi4
<Gallery />
## Model description
These are FeiiYin/lora-trained-xl-audi4 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sks car to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](FeiiYin/lora-trained-xl-audi4/tree/main) them in the Files & versions tab.
|
amd/squeezenet | amd | 2024-01-12T02:54:01Z | 0 | 0 | null | [
"onnx",
"RyzenAI",
"vision",
"classification",
"pytorch",
"dataset:imagenet-1k",
"arxiv:1602.07360",
"license:apache-2.0",
"region:us"
] | null | 2023-12-04T09:05:48Z | ---
license: apache-2.0
datasets:
- imagenet-1k
metrics:
- accuracy
tags:
- RyzenAI
- vision
- classification
- pytorch
---
# SqueezeNet1_1
Quantized SqueezeNet1_1 model that could be supported by [AMD Ryzen AI](https://ryzenai.docs.amd.com/en/latest/).
## Model description
SqueezeNet was first introduced in the paper [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360). This model is SqueezeNet v1.1 which requires 2.4x less computation than SqueezeNet v1.0 without diminshing accuracy.
The model implementation is from [torchvision](https://pytorch.org/vision/main/models/squeezenet.html).
## How to use
### Installation
Follow [Ryzen AI Installation](https://ryzenai.docs.amd.com/en/latest/inst.html) to prepare the environment for Ryzen AI.
Run the following script to install pre-requisites for this model.
```bash
pip install -r requirements.txt
```
### Data Preparation
Follow [PyTorch Example](https://github.com/pytorch/examples/blob/main/imagenet/README.md#requirements) to prepare dataset.
### Model Evaluation
```python
python eval_onnx.py --onnx_model SqueezeNet_int8.onnx --ipu --provider_config Path\To\vaip_config.json --data_dir /Path/To/Your/Dataset
```
### Performance
|Metric |Accuracy on IPU|
| :----: | :----: |
|Top1/Top5| 57.70% / 80.27% |
```bibtex
@article{SqueezeNet,
Author = {Forrest N. Iandola and Song Han and Matthew W. Moskewicz and Khalid Ashraf and William J. Dally and Kurt Keutzer},
Title = {SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $<$0.5MB model size},
Journal = {arXiv:1602.07360},
Year = {2016}
}
``` |
MaziyarPanahi/Mini_Synatra_SFT-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T02:42:07Z | 21 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"maywell/Mini_Synatra_SFT",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T02:37:05Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- maywell/Mini_Synatra_SFT
---
# Mini_Synatra_SFT-Mistral-7B-Instruct-v0.2-slerp
Mini_Synatra_SFT-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [maywell/Mini_Synatra_SFT](https://huggingface.co/maywell/Mini_Synatra_SFT)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: maywell/Mini_Synatra_SFT
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Mini_Synatra_SFT-Mistral-7B-Instruct-v0.2-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"])
``` |
fz2/t5-small-finetuned-xsum-zz | fz2 | 2024-01-12T02:34:36Z | 44 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-01-12T02:18:57Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: fz2/t5-small-finetuned-xsum-zz
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. -->
# fz2/t5-small-finetuned-xsum-zz
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.9917
- Validation Loss: 2.6503
- Train Rouge1: 25.2337
- Train Rouge2: 6.0997
- Train Rougel: 19.8280
- Train Rougelsum: 19.8418
- Train Gen Len: 18.7549
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 2.9917 | 2.6503 | 25.2337 | 6.0997 | 19.8280 | 19.8418 | 18.7549 | 0 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Sahibsingh12/phi-1-5-finetuned-cazton_complete | Sahibsingh12 | 2024-01-12T02:21:22Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"phi",
"custom_code",
"arxiv:1910.09700",
"base_model:microsoft/phi-1_5",
"base_model:adapter:microsoft/phi-1_5",
"region:us"
] | null | 2024-01-12T00:54:32Z | ---
library_name: peft
base_model: microsoft/phi-1_5
---
# 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 0.7.1 |
saddad/criley | saddad | 2024-01-12T02:03:33Z | 20 | 2 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-01-12T02:03:24Z | ---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: A photo of <s0><s1> a man holding up a passport and an eagle
output:
url: image-0.png
- text: A photo of <s0><s1> a man with a beard and a black shirt
output:
url: image-1.png
- text: A photo of <s0><s1> a man taking a selfie in front of a colorful building
output:
url: image-2.png
- text: A photo of <s0><s1> a man in a blue scrub suit and hat
output:
url: image-3.png
- text: A photo of <s0><s1> a man holding up a piece of wood with a note on it
output:
url: image-4.png
- text: A photo of <s0><s1> a man with a beard and a black shirt
output:
url: image-5.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A photo of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - saddad/criley
<Gallery />
## Model description
### These are saddad/criley LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`criley.safetensors` here πΎ](/saddad/criley/blob/main/criley.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:criley:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`criley_emb.safetensors` here πΎ](/saddad/criley/blob/main/criley_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `criley_emb` to your prompt. For example, `A photo of criley_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [𧨠diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('saddad/criley', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='saddad/criley', filename='criley_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` β use `<s0><s1>` in your prompt
## Details
All [Files & versions](/saddad/criley/tree/main).
The weights were trained using [𧨠diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
DGraham1/doodle_test_LoRA | DGraham1 | 2024-01-12T01:56:02Z | 0 | 0 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-01-12T01:56:00Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK dog
license: openrail++
---
# SDXL LoRA DreamBooth - DGraham1/doodle_test_LoRA
<Gallery />
## Model description
These are DGraham1/doodle_test_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](DGraham1/doodle_test_LoRA/tree/main) them in the Files & versions tab.
|
MaziyarPanahi/Mistral-T5-7B-v1-Mistral-7B-Instruct-v0.2-slerp | MaziyarPanahi | 2024-01-12T01:55:10Z | 22 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"ignos/Mistral-T5-7B-v1",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-12T01:50:10Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- ignos/Mistral-T5-7B-v1
---
# Mistral-T5-7B-v1-Mistral-7B-Instruct-v0.2-slerp
Mistral-T5-7B-v1-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [ignos/Mistral-T5-7B-v1](https://huggingface.co/ignos/Mistral-T5-7B-v1)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: ignos/Mistral-T5-7B-v1
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
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
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Mistral-T5-7B-v1-Mistral-7B-Instruct-v0.2-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"])
``` |
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