modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
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| library_name
stringlengths 2
84
⌀ | tags
list | pipeline_tag
stringlengths 5
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⌀ | createdAt
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| embedding
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tkcho/commerce-clf-kr-sku-brand-80768e578184d5cf3d9ac5415d4806e9
|
tkcho
| 2023-11-21T03:09:01Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T03:08:25Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ea6e2458a32cf233178f2ee07fdaff87
|
tkcho
| 2023-11-21T09:49:59Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T09:49:27Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-19af1e94175009cdef6261067634f5d6
|
tkcho
| 2023-11-30T09:29:13Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T04:27:32Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-4aacc881ed6f1142b4b6bb0d268739f4
|
tkcho
| 2023-11-22T08:21:45Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T08:21:02Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ea0417f5ed43b91ef1b1da3b7c923595
|
tkcho
| 2023-11-17T00:20:20Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T00:19:56Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-7e133eefacdaf80a9bb2080f61cd4b3c
|
tkcho
| 2023-11-18T02:12:11Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T02:11:43Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-62b475bd92b99e219a558fe0e73d2629
|
tkcho
| 2023-11-18T20:47:23Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T20:46:54Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-5b6e4f5dd341487a65e1b2521cda8ede
|
tkcho
| 2023-11-20T11:56:33Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T11:55:57Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-43d38a4bd710dba33fec23870da6d94a
|
tkcho
| 2023-11-21T02:21:02Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T02:20:32Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ee76afb73d9ae6454fae5a21840489fa
|
tkcho
| 2023-11-21T02:38:48Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T02:38:21Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-9981d48af97be28a974d5dd6f4cad3fe
|
tkcho
| 2023-11-21T02:50:36Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T02:50:11Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-62a65dd4ac84d2c610292ef6a0d9ffff
|
tkcho
| 2023-11-21T13:36:48Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T13:36:12Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-cb4f4bf09e2302a158234d3b59ec39cd
|
tkcho
| 2023-11-22T10:49:31Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T10:49:02Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-5f3d227a42a15cb282fe606e77e17afb
|
tkcho
| 2023-11-24T10:05:07Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T10:04:35Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-3ed3565fcbe904b1155e5e71f313aa66
|
tkcho
| 2023-11-25T02:54:54Z | 4,417 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-25T02:54:27Z |
Entry not found
|
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] |
Fredithefish/Guanaco-3B-Uncensored
|
Fredithefish
| 2023-08-27T21:33:10Z | 4,416 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"conversational",
"en",
"dataset:Fredithefish/openassistant-guanaco-unfiltered",
"license:apache-2.0",
"has_space",
"text-generation-inference",
"region:us"
] |
conversational
| 2023-08-23T13:13:14Z |
---
license: apache-2.0
datasets:
- Fredithefish/openassistant-guanaco-unfiltered
language:
- en
library_name: transformers
pipeline_tag: conversational
inference: false
---
<img src="https://huggingface.co/Fredithefish/Guanaco-3B-Uncensored/resolve/main/Guanaco-Uncensored.jpg" alt="Alt Text" width="295"/>
# ✨ Guanaco - 3B - Uncensored ✨
<h4 style="color: orange;">IMPORTANT:</h4>
<p style="color: orange;">This is the old model. The dataset has been updated and a newer version of this model is available <a href="https://huggingface.co/Fredithefish/Guanaco-3B-Uncensored-v2">here</a>.</p>
<br>
Guanaco-3B-Uncensored has been fine-tuned for 6 epochs on the [Unfiltered Guanaco Dataset.](https://huggingface.co/datasets/Fredithefish/openassistant-guanaco-unfiltered) using [RedPajama-INCITE-Base-3B-v1](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1) as the base model.
<br>The model does not perform well with languages other than English.
<br>Please note: This model is designed to provide responses without content filtering or censorship. It generates answers without denials.
## Special thanks
I would like to thank AutoMeta for providing me with the computing power necessary to train this model.
### Prompt Template
```
### Human: {prompt} ### Assistant:
```
|
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] |
tkcho/commerce-clf-kr-sku-brand-61a7efd0478643d4a37d8647531afdf0
|
tkcho
| 2023-11-14T21:54:31Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-14T21:54:04Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-f21f5f134915e921e67d158176983f4e
|
tkcho
| 2023-11-17T09:26:52Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T09:26:24Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-a014c9d65e2bbe7cf512720cfd62d86f
|
tkcho
| 2023-11-17T12:43:12Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T12:42:41Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-95244fa780cea274dad7ec4dbff6aaa8
|
tkcho
| 2023-11-19T06:51:49Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T06:51:23Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ce1ba8afff7caca930f530373990cc82
|
tkcho
| 2023-11-19T14:23:55Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T14:23:27Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-c7249091c3d89d4dde4e54c49c18047b
|
tkcho
| 2023-11-20T01:38:32Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:38:07Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-17a81523f6feccb8b2acc3ec63965c7e
|
tkcho
| 2023-11-21T06:19:19Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T06:18:39Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-505ad1fefba0c2693d8f172ed80d3a7d
|
tkcho
| 2023-11-21T14:30:10Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T14:29:41Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-a1a38935fecbacd21c0f607f9431d936
|
tkcho
| 2023-11-23T09:22:28Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T09:21:55Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-a95eb61117ba62670bb4ac1fa5ba4ce9
|
tkcho
| 2023-11-24T09:13:12Z | 4,416 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T09:12:39Z |
Entry not found
|
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timm/tf_efficientnet_lite4.in1k
|
timm
| 2023-04-27T21:38:39Z | 4,415 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:1905.11946",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-12-13T00:14:01Z |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for tf_efficientnet_lite4.in1k
A EfficientNet-Lite image classification model. Trained on ImageNet-1k in Tensorflow by paper authors, ported to PyTorch by Ross Wightman.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 13.0
- GMACs: 4.0
- Activations (M): 45.7
- Image size: 380 x 380
- **Papers:**
- EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('tf_efficientnet_lite4.in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnet_lite4.in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 24, 190, 190])
# torch.Size([1, 32, 95, 95])
# torch.Size([1, 56, 48, 48])
# torch.Size([1, 160, 24, 24])
# torch.Size([1, 448, 12, 12])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'tf_efficientnet_lite4.in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1280, 12, 12) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{tan2019efficientnet,
title={Efficientnet: Rethinking model scaling for convolutional neural networks},
author={Tan, Mingxing and Le, Quoc},
booktitle={International conference on machine learning},
pages={6105--6114},
year={2019},
organization={PMLR}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
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CobraMamba/mamba-gpt-3b
|
CobraMamba
| 2023-07-28T06:42:23Z | 4,415 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"en",
"license:apache-2.0",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-12T06:08:57Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
inference: false
thumbnail: >-
https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
license: apache-2.0
---
# Model Card
## Github
https://github.com/chi2liu/mamba-gpt-3b
| Metric | Value |
|-----------------------|-------|
| MMLU (5-shot) | 25.3 |
| ARC (25-shot) | 40.5 |
| HellaSwag (10-shot) | 64.9 |
| TruthfulQA (0-shot) | 37.1 |
| Avg. | 42.0 |
We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above.
## Summary
We have fine-tuned the open-lama model and surpassed the original model in multiple evaluation subtasks, making it currently the best performing 3B model with comparable performance to llama-7b
- Base model: [openlm-research/open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers`, `accelerate` and `torch` libraries installed.
```bash
pip install transformers==4.29.2
pip install accelerate==0.19.0
pip install torch==2.0.0
```
```python
import torch
from transformers import pipeline
generate_text = pipeline(
model="CobraMamba/mamba-gpt-3b",
torch_dtype="auto",
trust_remote_code=True,
use_fast=False,
device_map={"": "cuda:0"},
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download the mamba_gpt_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
import torch
from mamba_gpt_pipeline import MambaGPTTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"CobraMamba/mamba-gpt-3b",
use_fast=False,
padding_side="left",
trust_remote_code=False,
)
model = AutoModelForCausalLM.from_pretrained(
"CobraMamba/mamba-gpt-3b",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=False,
)
generate_text = MambaGPTTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "CobraMamba/mamba-gpt-3b" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=False,
trust_remote_code=False,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=False,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
**inputs,
min_new_tokens=2,
max_new_tokens=1024,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Evaluation
We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks.
| **Task/Metric** | finetuned-GPT 3B | OpenLLaMA 3B |
| ---------------------- | -------- | ------------ |
| anli_r1/acc | **0.35** | 0.33 |
| anli_r2/acc | **0.33** | 0.32 |
| anli_r3/acc | 0.35 | 0.35 |
| arc_challenge/acc | **0.35** | 0.34 |
| arc_challenge/acc_norm | 0.37 | 0.37 |
| arc_easy/acc | **0.71** | 0.69 |
| arc_easy/acc_norm | 0.65 | 0.65 |
| boolq/acc | **0.72** | 0.66 |
| hellaswag/acc | **0.49** | 0.43 |
| hellaswag/acc_norm | 0.66 | **0.67** |
| openbookqa/acc | 0.26 | **0.27** |
| openbookqa/acc_norm | 0.40 | 0.40 |
| piqa/acc | **0.76** | 0.75 |
| piqa/acc_norm | 0.76 | 0.76 |
| record/em | 0.88 | 0.88 |
| record/f1 | 0.88 | **0.89** |
| rte/acc | 0.55 | **0.58** |
| truthfulqa_mc/mc1 | **0.27** | 0.22 |
| truthfulqa_mc/mc2 | **0.37** | 0.35 |
| wic/acc | **0.49** | 0.48 |
| winogrande/acc | **0.63** | 0.62 |
| Average | **0.53** | 0.52 |
We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
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] |
sail-rvc/Patrick_Star__RVC_v2_
|
sail-rvc
| 2023-07-14T07:30:17Z | 4,415 | 0 |
transformers
|
[
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] |
audio-to-audio
| 2023-07-14T07:29:32Z |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# Patrick_Star__RVC_v2_
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:30:17
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
|
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pankajmathur/model_51
|
pankajmathur
| 2023-11-18T12:14:57Z | 4,415 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/dolphin",
"dataset:pankajmathur/orca_mini_v1_dataset",
"arxiv:2306.02707",
"license:llama2",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-03T15:08:32Z |
---
language:
- en
library_name: transformers
license: llama2
datasets:
- ehartford/dolphin
- pankajmathur/orca_mini_v1_dataset
---
# model_51
A Llama2-70b model trained on Orca Style datasets.
**P.S. If you're interested to collaborate, please connect with me at www.linkedin.com/in/pankajam**
## Evaluation
We evaluated model_51 on a wide range of tasks using [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) from EleutherAI.
Here are the results on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|||
|:------:|:--------:|
|**Task**|**Value**|
|*ARC*|0.6843|
|*HellaSwag*|0.8671|
|*MMLU*|0.6931|
|*TruthfulQA*|0.5718|
|*Winogrande*|0.8177|
|*GSM8K*|0.3237|
|*DROP*|0.5843|
|**Total Average**|**0.6488**|
### Prompt Foramt
```
### System:
You are an AI assistant that follows instruction extremely well. Help as much as you can.
### User:
Tell me about Orcas.
### Assistant:
```
#### OobaBooga Instructions:
This model required upto 45GB GPU VRAM in 4bit so it can be loaded directly on Single RTX 6000/L40/A40/A100/H100 GPU or Double RTX 4090/L4/A10/RTX 3090/RTX A5000
So, if you have access to Machine with 45GB GPU VRAM and have installed [OobaBooga Web UI](https://github.com/oobabooga/text-generation-webui) on it.
You can just download this model by using HF repo link directly on OobaBooga Web UI "Model" Tab/Page & Just use **load-in-4bit** option in it.
After that go to Default Tab/Page on OobaBooga Web UI and **copy paste above prompt format into Input** and Enjoy!
<br>
#### Code Instructions:
Below shows a code example on how to use this model
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
tokenizer = AutoTokenizer.from_pretrained("pankajmathur/model_51")
model = AutoModelForCausalLM.from_pretrained(
"pankajmathur/model_51",
torch_dtype=torch.float16,
load_in_4bit=True,
low_cpu_mem_usage=True,
device_map="auto"
)
system_prompt = "### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n"
#generate text steps
instruction = "Tell me about Orcas."
prompt = f"{system_prompt}### User: {instruction}\n\n### Assistant:\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=4096)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
#### Limitations & Biases:
While this model aims for accuracy, it can occasionally produce inaccurate or misleading results.
Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content.
Exercise caution and cross-check information when necessary.
### Citiation:
Please kindly cite using the following BibTeX:
```
@misc{model_51,
author = {Pankaj Mathur},
title = {model_51: An Orca Style Llama2-70b model},
year = {2023},
publisher = {HuggingFace},
journal = {HuggingFace repository},
howpublished = {\url{https://https://huggingface.co/psmathur/model_51},
}
```
```
@misc{mukherjee2023orca,
title={Orca: Progressive Learning from Complex Explanation Traces of GPT-4},
author={Subhabrata Mukherjee and Arindam Mitra and Ganesh Jawahar and Sahaj Agarwal and Hamid Palangi and Ahmed Awadallah},
year={2023},
eprint={2306.02707},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@software{touvron2023llama2,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava,
Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller,
Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez Madian Khabsa, Isabel Kloumann,
Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov,
Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith,
Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu , Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan,
Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, Thomas Scialom},
year={2023}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_psmathur__model_51)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 64.88 |
| ARC (25-shot) | 68.43 |
| HellaSwag (10-shot) | 86.71 |
| MMLU (5-shot) | 69.31 |
| TruthfulQA (0-shot) | 57.18 |
| Winogrande (5-shot) | 81.77 |
| GSM8K (5-shot) | 32.37 |
| DROP (3-shot) | 58.43 |
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] |
tkcho/commerce-clf-kr-sku-brand-b37885940d37c01ce0def7d48ef04601
|
tkcho
| 2023-11-16T00:00:57Z | 4,415 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T00:00:27Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-afcaa85a617d36f4d1132b97de8037f7
|
tkcho
| 2023-11-18T07:30:11Z | 4,415 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T07:29:39Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-680b8f6edfe4c691bd91eae9a5532860
|
tkcho
| 2023-11-19T04:49:31Z | 4,415 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T04:49:04Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-a52e54fb928241d042e399b71e0e7a45
|
tkcho
| 2023-11-21T01:32:29Z | 4,415 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T01:31:57Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-a21f0504382ea053e8326d6db59e000e
|
tkcho
| 2023-11-21T04:37:56Z | 4,415 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T04:37:28Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-b4a6b7892928afa67c6e10d9e3da95c8
|
tkcho
| 2023-11-21T16:58:44Z | 4,415 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T16:58:16Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-6eaac6a3c329907d8261883a9340b533
|
tkcho
| 2023-11-22T12:29:48Z | 4,415 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T12:29:11Z |
Entry not found
|
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0.3126053214073181,
-0.1702686995267868,
0.2698982357978821,
-0.4559200704097748,
-0.028932858258485794,
-0.057962555438280106,
0.31015971302986145,
-1.0262157917022705
] |
jondurbin/airoboros-7b
|
jondurbin
| 2023-08-14T09:04:07Z | 4,414 | 14 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-05-18T21:56:37Z |
---
license: cc-by-nc-4.0
---
# Overview
This is a fine-tuned 7b parameter LlaMa model, using completely synthetic training data created by https://github.com/jondurbin/airoboros
__*I don't recommend using this model! The outputs aren't particularly great, and it may contain "harmful" data due to jailbreak*__
Please see one of the updated airoboros models for a much better experience.
### Training data
This was an experiment to see if a "jailbreak" prompt could be used to generate a broader range of data that would otherwise have been filtered by OpenAI's alignment efforts.
The jailbreak did indeed work with a high success rate, and caused OpenAI to generate a broader range of topics and fewer refusals to answer questions/instructions of sensitive topics.
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
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] |
OpenBuddy/openbuddy-llama-30b-v7.1-bf16
|
OpenBuddy
| 2023-07-26T15:40:03Z | 4,414 | 6 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-19T07:20:17Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
# Copyright Notice
OpenBuddy LLaMA-series models are built upon Meta's LLaMA and are subject to Meta's licensing agreement.
They are intended for use only by individuals who have obtained approval from Meta and are eligible to download LLaMA.
If you have not obtained approval from Meta, you must visit the https://ai.meta.com/llama/ page, read and agree to the model's licensing agreement, submit an application, and wait for approval from Meta before downloading LLaMA-series models from this page.
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
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tkcho/commerce-clf-kr-sku-brand-37af1e08c94aa3f07b5c5f300369f69e
|
tkcho
| 2023-11-19T15:18:05Z | 4,414 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T15:17:37Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-f5e616130bbb4b7123ea641eee4e51e1
|
tkcho
| 2023-11-21T04:20:13Z | 4,414 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T04:19:41Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-c832b68c07912159641bc8022d86b82c
|
tkcho
| 2023-11-22T10:21:24Z | 4,414 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T10:20:49Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-88bfb7350e54356bb28c22c1733c5070
|
tkcho
| 2023-11-24T10:29:21Z | 4,414 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T10:28:40Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-55b58e789ce373ed384239aad9193e80
|
tkcho
| 2023-11-25T03:34:00Z | 4,414 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-25T03:33:27Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-45896d2239fd5696d6a7725bbfd439f1
|
tkcho
| 2023-11-16T17:44:33Z | 4,413 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T17:43:59Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-967c0823cad1084501db462dca54ac1b
|
tkcho
| 2023-11-16T19:00:50Z | 4,413 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T19:00:24Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-667034e4660dc8cbbdbcfeeae1f3d8a1
|
tkcho
| 2023-11-17T01:36:26Z | 4,413 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T01:35:58Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-d4e8f0a5857b384a7082c2004f855296
|
tkcho
| 2023-11-18T15:36:52Z | 4,413 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T15:36:24Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-6e0d5c04db623dda0c1ea3ff64305b6d
|
tkcho
| 2023-11-20T22:12:14Z | 4,413 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T22:11:48Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-1244b4f0838ad9550fe726fff3d6af53
|
tkcho
| 2023-11-30T09:41:59Z | 4,413 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T07:13:34Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-a1ff5fa747e78a9143de03b150f107a7
|
tkcho
| 2023-11-24T20:53:04Z | 4,413 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T20:52:37Z |
Entry not found
|
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] |
Azure99/blossom-v2-3b
|
Azure99
| 2023-08-11T11:53:58Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"zh",
"en",
"dataset:Azure99/blossom-chat-v1",
"dataset:Azure99/blossom-math-v1",
"dataset:ehartford/dolphin",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-08T12:13:00Z |
---
license: apache-2.0
datasets:
- Azure99/blossom-chat-v1
- Azure99/blossom-math-v1
- ehartford/dolphin
- WizardLM/WizardLM_evol_instruct_V2_196k
language:
- zh
- en
---
# **BLOSSOM-v2-3b**
### 介绍
Blossom是一个对话式语言模型，基于Bloom-3b预训练模型，在Blossom、Wizard、Dolphin混合数据集上进行指令精调得来。
训练分为两阶段，第一阶段使用120K Wizard、180K Dolphin单轮指令数据集，训练1个epoch；第二阶段使用60K Blossom chat、2K Blossom math多轮对话数据集，训练3个epoch。
### 推理
推理采用对话续写的形式。
单轮对话
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: 你好
|Bot|:
```
多轮对话
```
A chat between a human and an artificial intelligence bot. The bot gives helpful, detailed, and polite answers to the human's questions.
|Human|: 你好
|Bot|: 你好，有什么我能帮助你的？</s>
|Human|: 介绍下中国的首都吧
|Bot|:
```
注意：在历史对话的Bot输出结尾，拼接一个</s>
|
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] |
tkcho/commerce-clf-kr-sku-brand-73638b47eb0e84339a8c25322d001578
|
tkcho
| 2023-11-15T08:36:06Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-15T08:35:34Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-1e6086b705c7161eeb93a8b249a5ca7c
|
tkcho
| 2023-11-16T20:49:07Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T20:48:40Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-05837170b3c8249fce5ab0edc01916f2
|
tkcho
| 2023-11-17T01:47:30Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T01:46:50Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-4c14480e31cd4554937ade105615e382
|
tkcho
| 2023-11-18T03:40:04Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T03:39:35Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-3122d41f7e600af0f61f0cfcae70a1a4
|
tkcho
| 2023-11-18T05:19:33Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T05:19:02Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-659c29d7e76e8f0fd2a15971a3d62585
|
tkcho
| 2023-11-18T17:44:47Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T17:44:19Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-3a01ff72f0d29558d5a6bbbb85d0e91d
|
tkcho
| 2023-11-19T07:24:03Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T07:23:37Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-967f1aacac38c57fdda4341b395730ba
|
tkcho
| 2023-11-20T01:09:22Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:09:03Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-d81197003d27ba1249f92eba8e41117e
|
tkcho
| 2023-11-29T23:01:51Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T12:13:28Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-e2a5480afd5ea6b8b2779e192ba4771d
|
tkcho
| 2023-11-24T10:46:59Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T10:46:34Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-edcf6bdcde5c3015f1716666e88f11ae
|
tkcho
| 2023-11-24T14:34:35Z | 4,412 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T14:34:02Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-5bc8dd11fa123d93af3738dd9957d130
|
tkcho
| 2023-11-15T00:57:51Z | 4,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-15T00:57:25Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-8ec6e724498d5086a9f1d3eb2b7667a1
|
tkcho
| 2023-11-17T10:21:29Z | 4,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T10:21:00Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-614dd76f9dacf6a4c3f2a2b753a11146
|
tkcho
| 2023-11-20T01:27:26Z | 4,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:27:08Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-6fa3c73302c4cc02a12360fcbd86ba4e
|
tkcho
| 2023-11-20T01:42:54Z | 4,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:42:35Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-5bd9d244bf0b27c5532e70dfcfa79434
|
tkcho
| 2023-11-20T07:07:35Z | 4,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T07:07:03Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-bd77cbe121230ac6f60f141558af8a30
|
tkcho
| 2023-11-21T03:26:46Z | 4,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T03:26:19Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-e8a77f40d7b84df555ad3968deabf66d
|
tkcho
| 2023-11-22T12:45:58Z | 4,411 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T12:45:30Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-45c2317ee3274b270a633512fa966713
|
tkcho
| 2023-11-18T19:42:55Z | 4,410 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T19:42:29Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-bcc74510175cb39899d4c8fac1efc7f3
|
tkcho
| 2023-11-21T08:49:49Z | 4,410 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T08:49:14Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-f169ad96b4741d73dcd83f344ec5ea73
|
tkcho
| 2023-11-21T12:19:13Z | 4,410 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T12:18:43Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-cd63475378a3699de5a22035eeba48ff
|
tkcho
| 2023-11-24T12:30:17Z | 4,410 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T12:29:48Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-6aa0b1d2daf7c02bd5905e3c24ed5526
|
tkcho
| 2023-11-25T04:53:21Z | 4,410 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-25T04:52:35Z |
Entry not found
|
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Writer/palmyra-base
|
Writer
| 2023-08-28T17:50:19Z | 4,409 | 38 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"text generation",
"causal-lm",
"Writer-data",
"gpt",
"NeMo",
"palmyra",
"en",
"dataset:English",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-02-03T13:13:20Z |
---
language:
- en
datasets:
- English
tags:
- text generation
- pytorch
- causal-lm
- Writer-data
- gpt
- NeMo
- palmyra
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
---
# Palmyra Base 5B
<style>
img {
display: inline;
}
</style>
|[](#model-architecture)|[](#model-architecture)|[](#datasets)
## Model Description
Palmyra Base was primarily pre-trained with English text. Note that there is still a trace amount of non-English data present within the training corpus that was accessed through CommonCrawl. A causal language modeling (CLM) objective was utilized during the process of the model's pretraining. Similar to GPT-3, Palmyra Base is a member of the same family of models that only contain a decoder. As a result, it was pre-trained utilizing the objective of self-supervised causal language modeling. Palmyra Base uses the prompts and general experimental setup from GPT-3 in order to conduct its evaluation per GPT-3.
### Use case
Palmyra Base is extremely powerful while being extremely fast. This model excels at many nuanced tasks such as sentiment classification and summarization.
## Training data
Palmyra Base (5b) was trained on Writer’s custom dataset.
## Intended Use and Limitations
Palmyra Base learns an inner representation of the English language that can be used to extract features useful for downstream tasks. However, the model is best at what it was pre-trained for which is generating text from a prompt.
### How to use
This model can be easily loaded using the `AutoModelForCausalLM` functionality:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained("Writer/palmyra-base", torch_dtype=torch.float16).cuda()
# the fast tokenizer currently does not work correctly
tokenizer = AutoTokenizer.from_pretrained("Writer/palmyra-base", use_fast=False)
```
### Limitations and Biases
Palmyra Base’s core functionality is to take a string of text and predict the next token. While language models are widely used for other tasks, there are many unknowns in this work. When prompting Palmyra Base, keep in mind that the next statistically likely token is not always the token that produces the most "accurate" text. Never rely on Palmyra Base to produce factually correct results.
Palmyra Base was trained on Writer’s custom data. As with all language models, it is difficult to predict how Palmyra Base will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
## Evaluation results
Evaluation of Palmyra-base model on the SuperGLUE benchmark
| Task | Metric | Value |
|------------|--------|-------|
| boolq | acc | 64.43 |
| cb | acc | 10.71 |
| | f1 | 08.32 |
| copa | acc | 76.00 |
| multirc | acc | 01.26 |
| record | f1 | 84.02 |
| | em | 83.29 |
| wic | acc | 50.00 |
| wsc | acc | 36.54 |
## Citation and Related Information
To cite this model:
```
@misc{Palmyra,
author = {Writer Engineering team},
title = {{Palmyra-base Parameter Autoregressive Language Model}},
howpublished = {\url{https://dev.writer.com}},
year = 2023,
month = January
}
```
|
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] |
TheBloke/Lemur-70B-Chat-v1-GPTQ
|
TheBloke
| 2023-09-27T12:46:36Z | 4,409 | 3 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"text-generation-inference",
"en",
"base_model:OpenLemur/lemur-70b-chat-v1",
"license:cc-by-nc-4.0",
"has_space",
"4-bit",
"region:us"
] |
text-generation
| 2023-08-29T14:02:10Z |
---
language:
- en
license: cc-by-nc-4.0
library_name: transformers
tags:
- text-generation
- code
- text-generation-inference
model_name: Lemur 70B Chat v1
base_model: OpenLemur/lemur-70b-chat-v1
inference: false
model_creator: OpenLemur
model_type: llama
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
widget:
- example_title: Lemur favorite fruit
group: Python
text: What's lemur's favorite fruit?
- example_title: Merge Sort
group: Python
text: Write a Python function to merge two sorted lists into one sorted list without
using any built-in sort functions.
---
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</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Lemur 70B Chat v1 - GPTQ
- Model creator: [OpenLemur](https://huggingface.co/OpenLemur)
- Original model: [Lemur 70B Chat v1](https://huggingface.co/OpenLemur/lemur-70b-chat-v1)
<!-- description start -->
## Description
This repo contains GPTQ model files for [OpenLemur's Lemur 70B Chat v1](https://huggingface.co/OpenLemur/lemur-70b-chat-v1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GGUF)
* [OpenLemur's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenLemur/lemur-70b-chat-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [OpenLemur's Lemur 70B Chat v1](https://huggingface.co/OpenLemur/lemur-70b-chat-v1).
<!-- licensing end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 37.99 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.78 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 28.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Lemur-70B-Chat-v1-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/Lemur-70B-Chat-v1-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Lemur-70B-Chat-v1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Lemur-70B-Chat-v1-GPTQ:main`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Lemur-70B-Chat-v1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
### For CodeLlama models only: you must use Transformers 4.33.0 or later.
If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Lemur-70B-Chat-v1-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: OpenLemur's Lemur 70B Chat v1
# lemur-70b-chat-v1
<p align="center">
<img src="https://huggingface.co/datasets/OpenLemur/assets/resolve/main/lemur_icon.png" width="300" height="300" alt="Lemur">
</p>
<div align="center">
<img src="https://huggingface.co/datasets/OpenLemur/assets/resolve/main/lemur_chat_radar.png">
</div>
## Use
### Setup
First, we have to install all the libraries listed in `requirements.txt` in [GitHub](https://github.com/OpenLemur/lemur-v1):
```bash
pip install -r requirements.txt
```
### Generation
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLemur/lemur-70b-chat-v1")
model = AutoModelForCausalLM.from_pretrained("OpenLemur/lemur-70b-chat-v1", device_map="auto", load_in_8bit=True)
# Text Generation Example
prompt = """<|im_start|>system
You are a helpful, respectful, and honest assistant.
<|im_end|>
<|im_start|>user
What's a lemur's favorite fruit?<|im_end|>
<|im_start|>assistant
"""
input = tokenizer(prompt, return_tensors="pt")
output = model.generate(**input, max_length=50, num_return_sequences=1)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
# Code Generation Example
prompt = """<|im_start|>system
Below is an instruction that describes a task. Write a response that appropriately completes the request.
<|im_end|>
<|im_start|>user
Write a Python function to merge two sorted lists into one sorted list without using any built-in sort functions.<|im_end|>
<|im_start|>assistant
"""
input = tokenizer(prompt, return_tensors="pt")
output = model.generate(**input, max_length=200, num_return_sequences=1)
generated_code = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_code)
```
# License
The model is licensed under a CC BY-NC-4.0 license focused on research use cases.
# Acknowledgements
The Lemur project is an open collaborative research effort between [XLang Lab](https://www.xlang.ai/) and Salesforce Research. We thank Salesforce, Google Research and Amazon AWS for their gift support.
|
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Undi95/ReMM-SLERP-L2-13B
|
Undi95
| 2023-11-17T21:09:01Z | 4,409 | 11 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-09-04T17:37:26Z |
---
license: cc-by-nc-4.0
---
Re:MythoMax (ReMM) is a recreation trial of the original [MythoMax-L2-B13](https://huggingface.co/Gryphe/MythoMax-L2-13b) with updated models.
This merge use SLERP [TESTING] to merge ReML and Huginn v1.2.
Command useds and explaination :
```shell
Due to hardware limitation, some merge was done in 2 part.
- Recreate ReML : Mythologic (v2) (Chronos/Hermes/Airboros)
=> Replacing Chronos by The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 (0.30)
=> Replacing Airoboros by jondurbin/airoboros-l2-13b-2.1 (last version) (0.40)
=> Keeping NousResearch/Nous-Hermes-Llama2-13b (0.30)
Part 1: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./ReML-L2-13B-part1 --merge The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16 --density 0.42 --merge jondurbin/airoboros-l2-13b-2.1 --density 0.56 --cuda
Part 2: python ties_merge.py TheBloke/Llama-2-13B-fp16 ./ReML-L2-13B --merge NousResearch/Nous-Hermes-Llama2-13b --density 0.30 --merge Undi95/ReML-L2-13B-part1 --density 0.70 --cuda
With that :
- Recreate ReMM : MythoMax (v2) (Mythologic/Huginn v1)
=> Replacing Mythologic by the one above (0.5)
=> Replacing Huginn by The-Face-Of-Goonery/Huginn-13b-v1.2 (hottest) (0.5)
Part 3: python slerpmergelm.py "The-Face-Of-Goonery_Huginn-13b-v1.2" "Undi95_ReML-L2-13B" "result"
```
Version of SLERP used is different to accept usage on notebook : https://github.com/Undi95/LLM-SLERP-MergeTest/tree/main (Thanks @Vali)
<!-- description start -->
## Description
This repo contains fp16 files of ReMM-SLERP, a recreation of the original MythoMax, but updated and merged with SLERP.
<!-- description end -->
<!-- description start -->
## Models used
- TheBloke/Llama-2-13B-fp16 (base)
- The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16
- jondurbin/airoboros-l2-13b-2.1
- NousResearch/Nous-Hermes-Llama2-13b
- The-Face-Of-Goonery/Huginn-13b-v1.2
- ReML-L2-13B (Private recreation trial of an updated Mythologic-L2-13B)
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
Special thanks to Sushi kek
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Undi95__ReMM-SLERP-L2-13B)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 50.99 |
| ARC (25-shot) | 60.92 |
| HellaSwag (10-shot) | 83.56 |
| MMLU (5-shot) | 55.33 |
| TruthfulQA (0-shot) | 51.97 |
| Winogrande (5-shot) | 75.22 |
| GSM8K (5-shot) | 9.17 |
| DROP (3-shot) | 20.76 |
|
[
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] |
tkcho/commerce-clf-kr-sku-brand-f13dfee35a80a761918d326acd591d12
|
tkcho
| 2023-11-16T17:55:31Z | 4,409 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T17:55:01Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-529419940a585fb2a83765b2ca5cc091
|
tkcho
| 2023-11-16T19:11:45Z | 4,409 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T19:11:16Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-c699cd17f8038f5173d2cb17db860077
|
tkcho
| 2023-11-17T11:37:55Z | 4,409 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T11:37:19Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-730ce2874db60b828787549ae7cbd465
|
tkcho
| 2023-11-18T02:23:04Z | 4,409 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T02:22:31Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-7f9d3902853425ef339e355e14190ad5
|
tkcho
| 2023-11-20T01:19:39Z | 4,409 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:19:19Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-74358edc8ae3b7143e78853e4f7a382e
|
tkcho
| 2023-11-20T22:06:23Z | 4,409 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T22:05:50Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-f1a84f1da0faf660c945e4e4eb6e93ca
|
tkcho
| 2023-11-25T01:47:50Z | 4,409 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-25T01:47:14Z |
Entry not found
|
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Gryphe/MythoMix-L2-13b
|
Gryphe
| 2023-08-11T05:44:46Z | 4,408 | 16 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:other",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-08T15:01:01Z |
---
license: other
language:
- en
---
**UPDATE:** There's an improved version now! [Check it MythoMax!](https://huggingface.co/Gryphe/MythoMax-L2-13b)
A requested variant of [MythoLogic-L2](https://huggingface.co/Gryphe/MythoLogic-L2-13b) and [Huginn](https://huggingface.co/The-Face-Of-Goonery/Huginn-13b-FP16) using a highly experimental tensor type merge technique.
This model is proficient at both roleplaying and storywriting due to its unique nature.
Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoMix-L2-13B-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoMix-L2-13B-GPTQ) (You're the best!)
## Model details
The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time)
This type of merge is incapable of being illustrated, as each of its 360 tensors has an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour.
## Prompt Format
This model primarily uses Alpaca formatting, so for optimal model performance, use:
```
<System prompt/Character Card>
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
```
---
license: other
---
|
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] |
tkcho/commerce-clf-kr-item-domain-74de30fcca2bca9a1dde79e50833faa8
|
tkcho
| 2023-11-03T03:09:30Z | 4,408 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-03T03:09:05Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-0509ecfac619a0b2a47ec87740af70a0
|
tkcho
| 2023-11-18T17:12:13Z | 4,408 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T17:11:44Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-7200c78b0f927d1f091b1d730f4f171a
|
tkcho
| 2023-11-29T11:45:47Z | 4,408 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T03:33:43Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-5eba916d87a4d8663d24cc51edd91492
|
tkcho
| 2023-11-29T23:47:58Z | 4,408 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T02:34:55Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-6e333938d88b83fe60142c1b592f6904
|
tkcho
| 2023-11-21T02:32:56Z | 4,408 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T02:32:30Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-405de61ca13be7baf15194c2b051c7f0
|
tkcho
| 2023-11-21T09:31:58Z | 4,408 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T09:31:30Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-869f1ab109213f0af2012929589502a5
|
tkcho
| 2023-11-24T03:45:15Z | 4,408 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T03:44:45Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-f36354b9f891d8248c27d56ac6b0f273
|
tkcho
| 2023-11-27T14:21:51Z | 4,407 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T17:21:03Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-bfe494eb44cdb3d5f85c86e75c8c8f4d
|
tkcho
| 2023-11-20T10:11:08Z | 4,407 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T10:10:30Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-bbe708a09c7ed29f1b3f5e1fc265c045
|
tkcho
| 2023-11-21T05:55:00Z | 4,407 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T05:54:25Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-23b2e6e49f77a3c639c6e4f5f0005562
|
tkcho
| 2023-11-18T07:08:19Z | 4,406 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T07:07:51Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-c95043ddafb83bd0364188e28b115f1c
|
tkcho
| 2023-11-21T05:13:42Z | 4,406 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T05:13:11Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-7604377ef03cf1eee819c2d8732f7553
|
tkcho
| 2023-11-21T06:25:18Z | 4,406 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T06:24:44Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-75c639bc021c54717f77425cd8c49b6e
|
tkcho
| 2023-11-25T03:17:15Z | 4,406 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-25T03:16:49Z |
Entry not found
|
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