modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
9.67k
| library_name
stringlengths 2
84
⌀ | tags
list | pipeline_tag
stringlengths 5
30
⌀ | createdAt
timestamp[us, tz=UTC] | card
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901k
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|---|
VitaliiVrublevskyi/mrpc_deberta-base_base_dummy_attention
|
VitaliiVrublevskyi
| 2023-12-15T23:58:51Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta",
"endpoints_compatible",
"region:us"
] | null | 2023-12-15T23:58:30Z |
Entry not found
|
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] |
ewof/koishi-7b-qlora-gguf
|
ewof
| 2023-12-16T01:39:46Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"dataset:ewof/koishi-instruct-metharme",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2023-12-15T23:59:02Z |
---
datasets:
- ewof/koishi-instruct-metharme
---
## GGUF
little endian
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 6x nvidia a40 gpu cluster.
the a40 GPU cluster has been graciously provided by [Arc Compute](https://www.arccompute.io/).
trained on koishi commit 6e675d1 for one epoch
## Base Model
rank 16 lora tune of mistralai/Mistral-7B-v0.1 (all modules, merged)
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
|
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] |
ewof/koishi-7b-qlora-gptq
|
ewof
| 2023-12-16T14:53:18Z | 0 | 0 |
transformers
|
[
"transformers",
"mistral",
"text-generation",
"dataset:ewof/koishi-instruct-metharme",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-15T23:59:13Z |
---
datasets:
- ewof/koishi-instruct-metharme
---
## GPTQ
4096 sequence length
VMware/open-instruct dataset
## Training
[axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on a 6x nvidia a40 gpu cluster.
the a40 GPU cluster has been graciously provided by [Arc Compute](https://www.arccompute.io/).
trained on koishi commit 6e675d1 for one epoch
## Base Model
rank 16 lora tune of mistralai/Mistral-7B-v0.1 (all modules, merged)
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
|
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Nathan757/llama-2-7b-Physics
|
Nathan757
| 2023-12-16T00:20:22Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T00:12:07Z |
Entry not found
|
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] |
BePresent/mistral7b-test-gptq
|
BePresent
| 2023-12-16T00:25:04Z | 0 | 0 |
transformers
|
[
"transformers",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T00:15:52Z |
Entry not found
|
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StellarMilk/t5-small-newsqa-qag-finetuned
|
StellarMilk
| 2023-12-16T00:18:10Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"questions and answers generation",
"en",
"dataset:StellarMilk/newsqa",
"arxiv:2210.03992",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-16T00:16:18Z |
---
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- StellarMilk/newsqa
pipeline_tag: text2text-generation
tags:
- questions and answers generation
widget:
- text: "generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records."
example_title: "Questions & Answers Generation Example 1"
model-index:
- name: StellarMilk/t5-small-newsqa-qag-finetuned
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: StellarMilk/newsqa
type: default
args: default
metrics:
- name: BLEU4 (Question & Answer Generation)
type: bleu4_question_answer_generation
value: 4.45
---
# Model Card of `StellarMilk/t5-small-newsqa-qag-finetuned`
This model is fine-tuned version of [lmqg/t5-small-squad-qag](https://huggingface.co/lmqg/t5-small-squad-qag) for question & answer pair generation task on the [StellarMilk/newsqa](https://huggingface.co/datasets/StellarMilk/newsqa) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [lmqg/t5-small-squad-qag](https://huggingface.co/lmqg/t5-small-squad-qag)
- **Language:** en
- **Training data:** [StellarMilk/newsqa](https://huggingface.co/datasets/StellarMilk/newsqa) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="en", model="StellarMilk/t5-small-newsqa-qag-finetuned")
# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
```
- With `transformers`
```python
from transformers import pipeline
pipe = pipeline("text2text-generation", "StellarMilk/t5-small-newsqa-qag-finetuned")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
```
## Evaluation
- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/StellarMilk/t5-small-newsqa-qag-finetuned/raw/main/eval/metric.first.answer.paragraph.questions_answers.StellarMilk_newsqa.default.json)
| Score | Type | Dataset |
|---------|--------|-----------|
## Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: StellarMilk/newsqa
- dataset_name: default
- input_types: ['paragraph']
- output_types: ['questions_answers']
- prefix_types: ['qag']
- model: lmqg/t5-small-squad-qag
- max_length: 512
- max_length_output: 512
- epoch: 1
- batch: 2
- lr: 1e-05
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- label_smoothing: 0.15
The full configuration can be found at [fine-tuning config file](https://huggingface.co/StellarMilk/t5-small-newsqa-qag-finetuned/raw/main/trainer_config.json).
## Citation
```
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
author = "Ushio, Asahi and
Alva-Manchego, Fernando and
Camacho-Collados, Jose",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, U.A.E.",
publisher = "Association for Computational Linguistics",
}
```
|
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] |
LoneStriker/Nous-Capybara-limarpv3-34B-4.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T04:48:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T00:19:06Z |
Entry not found
|
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db4/LIHUAN2
|
db4
| 2023-12-16T00:24:52Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T00:19:19Z |
---
license: apache-2.0
---
|
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] |
ManuRasputin/Lana
|
ManuRasputin
| 2023-12-16T00:20:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T00:20:16Z |
Entry not found
|
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] |
afrideva/SmolLlamix-8x101M-GGUF
|
afrideva
| 2023-12-16T00:22:04Z | 0 | 1 | null |
[
"gguf",
"llama",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"en",
"dataset:togethercomputer/RedPajama-Data-1T-Sample",
"base_model:chargoddard/SmolLlamix-8x101M",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-16T00:20:39Z |
---
base_model: chargoddard/SmolLlamix-8x101M
datasets:
- togethercomputer/RedPajama-Data-1T-Sample
inference: false
language:
- en
license: apache-2.0
model_creator: chargoddard
model_name: SmolLlamix-8x101M
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- llama
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# chargoddard/SmolLlamix-8x101M-GGUF
Quantized GGUF model files for [SmolLlamix-8x101M](https://huggingface.co/chargoddard/SmolLlamix-8x101M) from [chargoddard](https://huggingface.co/chargoddard)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smolllamix-8x101m.fp16.gguf](https://huggingface.co/afrideva/SmolLlamix-8x101M-GGUF/resolve/main/smolllamix-8x101m.fp16.gguf) | fp16 | 798.87 MB |
| [smolllamix-8x101m.q2_k.gguf](https://huggingface.co/afrideva/SmolLlamix-8x101M-GGUF/resolve/main/smolllamix-8x101m.q2_k.gguf) | q2_k | 146.83 MB |
| [smolllamix-8x101m.q3_k_m.gguf](https://huggingface.co/afrideva/SmolLlamix-8x101M-GGUF/resolve/main/smolllamix-8x101m.q3_k_m.gguf) | q3_k_m | 184.67 MB |
| [smolllamix-8x101m.q4_k_m.gguf](https://huggingface.co/afrideva/SmolLlamix-8x101M-GGUF/resolve/main/smolllamix-8x101m.q4_k_m.gguf) | q4_k_m | 233.54 MB |
| [smolllamix-8x101m.q5_k_m.gguf](https://huggingface.co/afrideva/SmolLlamix-8x101M-GGUF/resolve/main/smolllamix-8x101m.q5_k_m.gguf) | q5_k_m | 279.97 MB |
| [smolllamix-8x101m.q6_k.gguf](https://huggingface.co/afrideva/SmolLlamix-8x101M-GGUF/resolve/main/smolllamix-8x101m.q6_k.gguf) | q6_k | 329.31 MB |
| [smolllamix-8x101m.q8_0.gguf](https://huggingface.co/afrideva/SmolLlamix-8x101M-GGUF/resolve/main/smolllamix-8x101m.q8_0.gguf) | q8_0 | 425.26 MB |
## Original Model Card:
This is eight copies of [BEE-spoke-data/smol_llama-101M-GQA](https://huggingface.co/BEE-spoke-data/smol_llama-101M-GQA) ensembled into a Mixtral model, then trained very briefly on a small subset of RedPajama. Mostly just an experiment to demonstrate that training it works at all.
It's very, very smart. Probably the smartest model ever made. Better than GPT-5. See its thoughts on the internet:
> In a world where the internet is so much more than a web browser, it's also very important to have a good understanding of how the internet works.
> The first thing we need to do is to understand what the internet looks like and what the future looks like. We can use the internet to look at the internet's history, but we don't want to go into detail about the history of the internet. The internet was created by the internet's history, which is often called the history of the internet. It was originally developed as a way for people to learn about the internet, but it wasn't until the 1960s that the internet became a place to work. Today, the internet is used in many ways, from the internet's history to the internet itself.
|
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zsy12345/model-medium-Punjabi-3-0-2000-1e-06-8-8-1000-lora
|
zsy12345
| 2023-12-16T00:23:04Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-medium",
"region:us"
] | null | 2023-12-16T00:22:56Z |
---
library_name: peft
base_model: openai/whisper-medium
---
# 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]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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. -->
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### 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. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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 -->
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## 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]
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## Technical Specifications [optional]
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## 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: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
|
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zsy12345/w-train-lora-medium-Punjabi-3-0-2000-1e-06-8-8-1000
|
zsy12345
| 2023-12-16T00:23:15Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"hf-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T00:23:04Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: model-medium-Punjabi-3-0-2000-1e-06-8-8-1000
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. -->
# model-medium-Punjabi-3-0-2000-1e-06-8-8-1000
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8558
## 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: 0.001
- 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_steps: 200
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9371 | 0.97 | 1000 | 0.9231 |
| 0.7781 | 1.94 | 2000 | 0.8558 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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SiE69/SiE69_Checkpoint_Collection_SD15
|
SiE69
| 2023-12-17T16:28:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T00:23:22Z |
Entry not found
|
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Xenova/llama2-tokenizer
|
Xenova
| 2023-12-16T00:25:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T00:24:15Z |
Entry not found
|
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afrideva/Walter-Llama-1B-GGUF
|
afrideva
| 2023-12-16T00:32:19Z | 0 | 0 | null |
[
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"dataset:KnutJaegersberg/Auton",
"base_model:KnutJaegersberg/Walter-Llama-1B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-16T00:28:36Z |
---
base_model: KnutJaegersberg/Walter-Llama-1B
datasets:
- KnutJaegersberg/Auton
inference: false
license: apache-2.0
model_creator: KnutJaegersberg
model_name: Walter-Llama-1B
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# KnutJaegersberg/Walter-Llama-1B-GGUF
Quantized GGUF model files for [Walter-Llama-1B](https://huggingface.co/KnutJaegersberg/Walter-Llama-1B) from [KnutJaegersberg](https://huggingface.co/KnutJaegersberg)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [walter-llama-1b.fp16.gguf](https://huggingface.co/afrideva/Walter-Llama-1B-GGUF/resolve/main/walter-llama-1b.fp16.gguf) | fp16 | 2.20 GB |
| [walter-llama-1b.q2_k.gguf](https://huggingface.co/afrideva/Walter-Llama-1B-GGUF/resolve/main/walter-llama-1b.q2_k.gguf) | q2_k | 483.12 MB |
| [walter-llama-1b.q3_k_m.gguf](https://huggingface.co/afrideva/Walter-Llama-1B-GGUF/resolve/main/walter-llama-1b.q3_k_m.gguf) | q3_k_m | 550.82 MB |
| [walter-llama-1b.q4_k_m.gguf](https://huggingface.co/afrideva/Walter-Llama-1B-GGUF/resolve/main/walter-llama-1b.q4_k_m.gguf) | q4_k_m | 668.79 MB |
| [walter-llama-1b.q5_k_m.gguf](https://huggingface.co/afrideva/Walter-Llama-1B-GGUF/resolve/main/walter-llama-1b.q5_k_m.gguf) | q5_k_m | 783.02 MB |
| [walter-llama-1b.q6_k.gguf](https://huggingface.co/afrideva/Walter-Llama-1B-GGUF/resolve/main/walter-llama-1b.q6_k.gguf) | q6_k | 904.39 MB |
| [walter-llama-1b.q8_0.gguf](https://huggingface.co/afrideva/Walter-Llama-1B-GGUF/resolve/main/walter-llama-1b.q8_0.gguf) | q8_0 | 1.17 GB |
## Original Model Card:
Walter is an unaligned, free thinking AI assistant that has been given time to think about things.
It's trained on instruction datasets with open source licenses.
It covers a lot of tasks, 2/3 of the samples are from large datasets like flan, but also other datasets.
It knows a few tricks, shown by the prompting examples below:
General Prompt Examples:
```
### Instruction:
Please answer the following question: Process: - The bat sends out sound waves from their mouth or nose - The sound waves hit an object - Echoes are produced - The echo returns to the bat's ears - The bat hears the echo - The bat can figure out where the object is located. suppose No bats live here happens, how will it affect STRONGER echolocation. Which of the following is the supposed perturbation? - directly impacting a step of the process - indirectly impacting a step of the process - not impacting any step of the process Answer:
### Response:
indirectly impacting a step of the process
```
```
### Instruction:
You will be given a text in Russian language which contain different emotion labels from the list - ['joy', ' sadness', 'surprise', 'fear', 'anger']. You need to output the incorrect emotion label, which is irrelevant to the input text. Your answer (i) should contain only one emotion label (ii) should be unambiguous.
### Input:
То чувство, когда идешь домой после 3-х часовой тренировки и в подъезде пахнет запеченной курицей - зачем вы меня дразните :(
### Response:
surprise
```
CoT Prompt Example:
```
### Instruction:
In this task, you are given a sentence with a missing word that can be an object, a person, and/or an action. Fill in the blank with a plausible word. Although each sentence has many correct answers, you only have to write one answer. PersonX puts ___ on PersonY's car
### Reasoning:
Since PersonX is putting water on PersonY's car, it could be assumed that the car is on fire.
### Response:
water
```
Summarization Example:
```
### Instruction:
Below is a text. Write a comprehensive, concise and coherent summary.
### Input:
Embodied bilingual language, also known as L2 embodiment, is the idea that people mentally simulate their actions, perceptions, and emotions when speaking and understanding a second language (L2) as with their first language (L1). It is closely related to embodied cognition and embodied language processing, both of which only refer to native language thinking and speaking. An example of embodied bilingual language would be situation in which a L1 English speaker learning Spanish as a second language hears the word rápido ("fast") in Spanish while taking notes and then proceeds to take notes more quickly. ==Overview== Embodied bilingual language refers to the role ......
### Response:
Embodied bilingual language, also known as L2 embodiment, is the idea that people mentally simulate their actions, perceptions, and emotions when speaking and understanding a second language (L2) as with their first language (L1). It is closely related to embodied cognition and embodied language processing, both of which only refer to native language thinking and speaking. An example of embodied bilingual language would be situation in which a L1 English speaker learning Spanish as a second language hears the word rápido ("fast") in Spanish while taking notes and then proceeds to take notes more quickly.
```
Essay Example:
```
### Instruction:
Write the original essay for the following summary: A sleeveless dress with a round neck is a great day dress and can be worn for both formal and casual occasions. It has a sweetheart neckline and is made from 100% silk. The dress has a chiffon overlay that covers up the miniskirt and still adds to the class of the dress.
### Response:
Sleeveless Dress with a Round Neck Essay Casual wear, day dress, more formal dress The garment is a sleeveless dress with a round neck and has a sweetheart neckline. It has sheer detail on the neckline and on the back. These aspects of design make it suitable to be worn for day and evening occasions. Its great detail and chicness make it suitable for more formal events, ....
```
|
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] |
badamczewski01/test2
|
badamczewski01
| 2023-12-16T00:28:42Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T00:28:42Z |
---
license: mit
---
|
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] |
BePresent/mistral7b-test
|
BePresent
| 2023-12-16T00:36:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T00:28:46Z |
Entry not found
|
[
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iadsmedia/Mistral-7B-Instruct-v0.2
|
iadsmedia
| 2023-12-16T00:30:18Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T00:30:18Z |
---
license: apache-2.0
---
|
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afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF
|
afrideva
| 2023-12-16T00:39:32Z | 0 | 0 | null |
[
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"en",
"dataset:Locutusque/inst_mix_v2_top_100k",
"base_model:Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-16T00:38:29Z |
---
base_model: Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct
datasets:
- Locutusque/inst_mix_v2_top_100k
inference: false
language:
- en
license: apache-2.0
model_creator: Locutusque
model_name: LocutusqueXFelladrin-TinyMistral248M-Instruct
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
widget:
- text: '<|USER|> Design a Neo4j database and Cypher function snippet to Display Extreme
Dental hygiene: Using Mouthwash for Analysis for Beginners. Implement if/else
or switch/case statements to handle different conditions related to the Consent.
Provide detailed comments explaining your control flow and the reasoning behind
each decision. <|ASSISTANT|> '
- text: '<|USER|> Write me a story about a magical place. <|ASSISTANT|> '
- text: '<|USER|> Write me an essay about the life of George Washington <|ASSISTANT|> '
- text: '<|USER|> Solve the following equation 2x + 10 = 20 <|ASSISTANT|> '
- text: '<|USER|> Craft me a list of some nice places to visit around the world. <|ASSISTANT|> '
- text: '<|USER|> How to manage a lazy employee: Address the employee verbally. Don''t
allow an employee''s laziness or lack of enthusiasm to become a recurring issue.
Tell the employee you''re hoping to speak with them about workplace expectations
and performance, and schedule a time to sit down together. Question: To manage
a lazy employee, it is suggested to talk to the employee. True, False, or Neither?
<|ASSISTANT|> '
---
# Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF
Quantized GGUF model files for [LocutusqueXFelladrin-TinyMistral248M-Instruct](https://huggingface.co/Locutusque/LocutusqueXFelladrin-TinyMistral248M-Instruct) from [Locutusque](https://huggingface.co/Locutusque)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.fp16.gguf) | fp16 | 497.76 MB |
| [locutusquexfelladrin-tinymistral248m-instruct.q2_k.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q2_k.gguf) | q2_k | 116.20 MB |
| [locutusquexfelladrin-tinymistral248m-instruct.q3_k_m.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q3_k_m.gguf) | q3_k_m | 131.01 MB |
| [locutusquexfelladrin-tinymistral248m-instruct.q4_k_m.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q4_k_m.gguf) | q4_k_m | 156.61 MB |
| [locutusquexfelladrin-tinymistral248m-instruct.q5_k_m.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q5_k_m.gguf) | q5_k_m | 180.17 MB |
| [locutusquexfelladrin-tinymistral248m-instruct.q6_k.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q6_k.gguf) | q6_k | 205.20 MB |
| [locutusquexfelladrin-tinymistral248m-instruct.q8_0.gguf](https://huggingface.co/afrideva/LocutusqueXFelladrin-TinyMistral248M-Instruct-GGUF/resolve/main/locutusquexfelladrin-tinymistral248m-instruct.q8_0.gguf) | q8_0 | 265.26 MB |
## Original Model Card:
# LocutusqueXFelladrin-TinyMistral248M-Instruct
This model was created by merging Locutusque/TinyMistral-248M-Instruct and Felladrin/TinyMistral-248M-SFT-v4 using mergekit. After the two models were merged, the resulting model was further trained on ~20,000 examples on the Locutusque/inst_mix_v2_top_100k at a low learning rate to further normalize weights. The following is the YAML config used to merge:
```yaml
models:
- model: Felladrin/TinyMistral-248M-SFT-v4
parameters:
weight: 0.5
- model: Locutusque/TinyMistral-248M-Instruct
parameters:
weight: 1.0
merge_method: linear
dtype: float16
```
The resulting model combines the best of both worlds. With Locutusque/TinyMistral-248M-Instruct's coding capabilities and reasoning skills, and Felladrin/TinyMistral-248M-SFT-v4's low hallucination and instruction-following capabilities. The resulting model has an incredible performance considering its size.
## Evaluation
Coming soon...
|
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] |
LoneStriker/Nous-Capybara-limarpv3-34B-4.65bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T04:48:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T00:41:08Z |
Entry not found
|
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AlekBot/Aaa
|
AlekBot
| 2023-12-16T00:43:43Z | 0 | 0 | null |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2023-12-16T00:43:43Z |
---
license: bigcode-openrail-m
---
|
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] |
EmporiumTech/Emporium
|
EmporiumTech
| 2023-12-16T00:44:09Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T00:44:09Z |
---
license: apache-2.0
---
|
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hkivancoral/smids_5x_beit_base_sgd_00001_fold5
|
hkivancoral
| 2023-12-16T02:05:41Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T00:46:47Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_sgd_00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.40166666666666667
---
<!-- 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. -->
# smids_5x_beit_base_sgd_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1301
- Accuracy: 0.4017
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2159 | 1.0 | 375 | 1.3104 | 0.3133 |
| 1.2415 | 2.0 | 750 | 1.3020 | 0.3233 |
| 1.2057 | 3.0 | 1125 | 1.2939 | 0.3233 |
| 1.176 | 4.0 | 1500 | 1.2863 | 0.3267 |
| 1.2191 | 5.0 | 1875 | 1.2790 | 0.3267 |
| 1.1863 | 6.0 | 2250 | 1.2719 | 0.3333 |
| 1.2037 | 7.0 | 2625 | 1.2651 | 0.34 |
| 1.177 | 8.0 | 3000 | 1.2586 | 0.3483 |
| 1.1576 | 9.0 | 3375 | 1.2521 | 0.35 |
| 1.0865 | 10.0 | 3750 | 1.2459 | 0.3517 |
| 1.1578 | 11.0 | 4125 | 1.2399 | 0.3533 |
| 1.1516 | 12.0 | 4500 | 1.2341 | 0.355 |
| 1.1216 | 13.0 | 4875 | 1.2282 | 0.355 |
| 1.1365 | 14.0 | 5250 | 1.2228 | 0.3583 |
| 1.1282 | 15.0 | 5625 | 1.2175 | 0.3583 |
| 1.1187 | 16.0 | 6000 | 1.2123 | 0.3633 |
| 1.1048 | 17.0 | 6375 | 1.2074 | 0.365 |
| 1.1548 | 18.0 | 6750 | 1.2025 | 0.365 |
| 1.1271 | 19.0 | 7125 | 1.1978 | 0.3683 |
| 1.1003 | 20.0 | 7500 | 1.1934 | 0.3717 |
| 1.0771 | 21.0 | 7875 | 1.1891 | 0.3733 |
| 1.0833 | 22.0 | 8250 | 1.1849 | 0.3767 |
| 1.1002 | 23.0 | 8625 | 1.1809 | 0.3783 |
| 1.0994 | 24.0 | 9000 | 1.1772 | 0.3833 |
| 1.0715 | 25.0 | 9375 | 1.1735 | 0.385 |
| 1.1029 | 26.0 | 9750 | 1.1700 | 0.3867 |
| 1.1056 | 27.0 | 10125 | 1.1666 | 0.3867 |
| 1.022 | 28.0 | 10500 | 1.1633 | 0.3883 |
| 1.0343 | 29.0 | 10875 | 1.1602 | 0.3867 |
| 1.0325 | 30.0 | 11250 | 1.1573 | 0.3883 |
| 1.0378 | 31.0 | 11625 | 1.1546 | 0.3883 |
| 1.0659 | 32.0 | 12000 | 1.1519 | 0.3867 |
| 1.0282 | 33.0 | 12375 | 1.1495 | 0.3867 |
| 1.0519 | 34.0 | 12750 | 1.1472 | 0.3883 |
| 1.0399 | 35.0 | 13125 | 1.1451 | 0.3883 |
| 1.0632 | 36.0 | 13500 | 1.1430 | 0.39 |
| 1.015 | 37.0 | 13875 | 1.1411 | 0.39 |
| 1.0714 | 38.0 | 14250 | 1.1394 | 0.39 |
| 0.9921 | 39.0 | 14625 | 1.1379 | 0.3917 |
| 1.0391 | 40.0 | 15000 | 1.1365 | 0.3917 |
| 1.0121 | 41.0 | 15375 | 1.1352 | 0.395 |
| 1.0675 | 42.0 | 15750 | 1.1341 | 0.3967 |
| 1.0815 | 43.0 | 16125 | 1.1331 | 0.3967 |
| 1.0054 | 44.0 | 16500 | 1.1322 | 0.3967 |
| 1.0674 | 45.0 | 16875 | 1.1316 | 0.3983 |
| 1.0115 | 46.0 | 17250 | 1.1310 | 0.4 |
| 1.0426 | 47.0 | 17625 | 1.1306 | 0.4017 |
| 1.0416 | 48.0 | 18000 | 1.1303 | 0.4017 |
| 1.0297 | 49.0 | 18375 | 1.1302 | 0.4017 |
| 1.0431 | 50.0 | 18750 | 1.1301 | 0.4017 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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afrideva/Walter-StableLM-3B-GGUF
|
afrideva
| 2023-12-16T00:57:27Z | 0 | 1 | null |
[
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"dataset:KnutJaegersberg/Auton",
"base_model:KnutJaegersberg/Walter-StableLM-3B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-16T00:49:06Z |
---
base_model: KnutJaegersberg/Walter-StableLM-3B
datasets:
- KnutJaegersberg/Auton
inference: false
license: apache-2.0
model_creator: KnutJaegersberg
model_name: Walter-StableLM-3B
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# KnutJaegersberg/Walter-StableLM-3B-GGUF
Quantized GGUF model files for [Walter-StableLM-3B](https://huggingface.co/KnutJaegersberg/Walter-StableLM-3B) from [KnutJaegersberg](https://huggingface.co/KnutJaegersberg)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [walter-stablelm-3b.fp16.gguf](https://huggingface.co/afrideva/Walter-StableLM-3B-GGUF/resolve/main/walter-stablelm-3b.fp16.gguf) | fp16 | 5.59 GB |
| [walter-stablelm-3b.q2_k.gguf](https://huggingface.co/afrideva/Walter-StableLM-3B-GGUF/resolve/main/walter-stablelm-3b.q2_k.gguf) | q2_k | 1.20 GB |
| [walter-stablelm-3b.q3_k_m.gguf](https://huggingface.co/afrideva/Walter-StableLM-3B-GGUF/resolve/main/walter-stablelm-3b.q3_k_m.gguf) | q3_k_m | 1.39 GB |
| [walter-stablelm-3b.q4_k_m.gguf](https://huggingface.co/afrideva/Walter-StableLM-3B-GGUF/resolve/main/walter-stablelm-3b.q4_k_m.gguf) | q4_k_m | 1.71 GB |
| [walter-stablelm-3b.q5_k_m.gguf](https://huggingface.co/afrideva/Walter-StableLM-3B-GGUF/resolve/main/walter-stablelm-3b.q5_k_m.gguf) | q5_k_m | 1.99 GB |
| [walter-stablelm-3b.q6_k.gguf](https://huggingface.co/afrideva/Walter-StableLM-3B-GGUF/resolve/main/walter-stablelm-3b.q6_k.gguf) | q6_k | 2.30 GB |
| [walter-stablelm-3b.q8_0.gguf](https://huggingface.co/afrideva/Walter-StableLM-3B-GGUF/resolve/main/walter-stablelm-3b.q8_0.gguf) | q8_0 | 2.97 GB |
## Original Model Card:
Walter is an unaligned, free thinking AI assistant that has been given time to think about things.
It's trained on instruction datasets with open source licenses.
It covers a lot of tasks, 2/3 of the samples are from large datasets like flan, but also other datasets.
It knows a few tricks, shown by the prompting examples below:
General Prompt Examples:
```
### Instruction:
Please answer the following question: Process: - The bat sends out sound waves from their mouth or nose - The sound waves hit an object - Echoes are produced - The echo returns to the bat's ears - The bat hears the echo - The bat can figure out where the object is located. suppose No bats live here happens, how will it affect STRONGER echolocation. Which of the following is the supposed perturbation? - directly impacting a step of the process - indirectly impacting a step of the process - not impacting any step of the process Answer:
### Response:
indirectly impacting a step of the process
```
```
### Instruction:
You will be given a text in Russian language which contain different emotion labels from the list - ['joy', ' sadness', 'surprise', 'fear', 'anger']. You need to output the incorrect emotion label, which is irrelevant to the input text. Your answer (i) should contain only one emotion label (ii) should be unambiguous.
### Input:
То чувство, когда идешь домой после 3-х часовой тренировки и в подъезде пахнет запеченной курицей - зачем вы меня дразните :(
### Response:
surprise
```
CoT Prompt Example:
```
### Instruction:
In this task, you are given a sentence with a missing word that can be an object, a person, and/or an action. Fill in the blank with a plausible word. Although each sentence has many correct answers, you only have to write one answer. PersonX puts ___ on PersonY's car
### Reasoning:
Since PersonX is putting water on PersonY's car, it could be assumed that the car is on fire.
### Response:
water
```
Summarization Example:
```
### Instruction:
Below is a text. Write a comprehensive, concise and coherent summary.
### Input:
Embodied bilingual language, also known as L2 embodiment, is the idea that people mentally simulate their actions, perceptions, and emotions when speaking and understanding a second language (L2) as with their first language (L1). It is closely related to embodied cognition and embodied language processing, both of which only refer to native language thinking and speaking. An example of embodied bilingual language would be situation in which a L1 English speaker learning Spanish as a second language hears the word rápido ("fast") in Spanish while taking notes and then proceeds to take notes more quickly. ==Overview== Embodied bilingual language refers to the role ......
### Response:
Embodied bilingual language, also known as L2 embodiment, is the idea that people mentally simulate their actions, perceptions, and emotions when speaking and understanding a second language (L2) as with their first language (L1). It is closely related to embodied cognition and embodied language processing, both of which only refer to native language thinking and speaking. An example of embodied bilingual language would be situation in which a L1 English speaker learning Spanish as a second language hears the word rápido ("fast") in Spanish while taking notes and then proceeds to take notes more quickly.
```
Essay Example:
```
### Instruction:
Write the original essay for the following summary: A sleeveless dress with a round neck is a great day dress and can be worn for both formal and casual occasions. It has a sweetheart neckline and is made from 100% silk. The dress has a chiffon overlay that covers up the miniskirt and still adds to the class of the dress.
### Response:
Sleeveless Dress with a Round Neck Essay Casual wear, day dress, more formal dress The garment is a sleeveless dress with a round neck and has a sweetheart neckline. It has sheer detail on the neckline and on the back. These aspects of design make it suitable to be worn for day and evening occasions. Its great detail and chicness make it suitable for more formal events, ....
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] |
ihkl/jjihjjbh
|
ihkl
| 2023-12-16T00:52:47Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T00:52:47Z |
---
license: mit
---
|
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realryo1/nva-miku16th_0.2
|
realryo1
| 2023-12-16T00:54:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T00:54:36Z |
Entry not found
|
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] |
CeAl/bert-base-multilingual-uncased-sentiment-finetuned-mnli
|
CeAl
| 2023-12-16T09:50:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T00:55:18Z |
Entry not found
|
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hkivancoral/smids_5x_beit_base_rms_001_fold5
|
hkivancoral
| 2023-12-16T02:12:30Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T00:55:47Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8216666666666667
---
<!-- 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. -->
# smids_5x_beit_base_rms_001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3262
- Accuracy: 0.8217
## 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: 0.001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.931 | 1.0 | 375 | 0.8668 | 0.5083 |
| 0.8597 | 2.0 | 750 | 0.7892 | 0.6017 |
| 0.7587 | 3.0 | 1125 | 0.7350 | 0.6383 |
| 0.7046 | 4.0 | 1500 | 0.7282 | 0.65 |
| 0.6817 | 5.0 | 1875 | 0.7027 | 0.6567 |
| 0.6292 | 6.0 | 2250 | 0.6987 | 0.6683 |
| 0.6024 | 7.0 | 2625 | 0.5984 | 0.7267 |
| 0.6528 | 8.0 | 3000 | 0.5956 | 0.7267 |
| 0.5546 | 9.0 | 3375 | 0.5629 | 0.765 |
| 0.4767 | 10.0 | 3750 | 0.5576 | 0.75 |
| 0.4967 | 11.0 | 4125 | 0.4703 | 0.8017 |
| 0.3904 | 12.0 | 4500 | 0.4630 | 0.8083 |
| 0.395 | 13.0 | 4875 | 0.4837 | 0.8 |
| 0.4102 | 14.0 | 5250 | 0.4887 | 0.815 |
| 0.4425 | 15.0 | 5625 | 0.4472 | 0.8317 |
| 0.269 | 16.0 | 6000 | 0.4817 | 0.8133 |
| 0.3554 | 17.0 | 6375 | 0.4030 | 0.8483 |
| 0.3667 | 18.0 | 6750 | 0.4187 | 0.83 |
| 0.2943 | 19.0 | 7125 | 0.4575 | 0.8333 |
| 0.2361 | 20.0 | 7500 | 0.4670 | 0.8317 |
| 0.2672 | 21.0 | 7875 | 0.4447 | 0.8383 |
| 0.2065 | 22.0 | 8250 | 0.4671 | 0.8267 |
| 0.3036 | 23.0 | 8625 | 0.5659 | 0.8167 |
| 0.1998 | 24.0 | 9000 | 0.5359 | 0.8233 |
| 0.1813 | 25.0 | 9375 | 0.4898 | 0.85 |
| 0.16 | 26.0 | 9750 | 0.5701 | 0.835 |
| 0.1617 | 27.0 | 10125 | 0.5423 | 0.8333 |
| 0.1338 | 28.0 | 10500 | 0.5644 | 0.8483 |
| 0.1411 | 29.0 | 10875 | 0.5853 | 0.8267 |
| 0.0859 | 30.0 | 11250 | 0.6605 | 0.8217 |
| 0.101 | 31.0 | 11625 | 0.7234 | 0.8317 |
| 0.0828 | 32.0 | 12000 | 0.6563 | 0.8367 |
| 0.1039 | 33.0 | 12375 | 0.7913 | 0.82 |
| 0.0772 | 34.0 | 12750 | 0.8613 | 0.82 |
| 0.0737 | 35.0 | 13125 | 0.7477 | 0.8283 |
| 0.0714 | 36.0 | 13500 | 0.9064 | 0.83 |
| 0.0337 | 37.0 | 13875 | 0.8383 | 0.8367 |
| 0.094 | 38.0 | 14250 | 0.9398 | 0.8233 |
| 0.0203 | 39.0 | 14625 | 0.9121 | 0.8267 |
| 0.0289 | 40.0 | 15000 | 1.0830 | 0.8283 |
| 0.0242 | 41.0 | 15375 | 1.1069 | 0.825 |
| 0.0154 | 42.0 | 15750 | 1.1781 | 0.8117 |
| 0.009 | 43.0 | 16125 | 1.1755 | 0.8167 |
| 0.0144 | 44.0 | 16500 | 1.1730 | 0.8233 |
| 0.0239 | 45.0 | 16875 | 1.4682 | 0.8083 |
| 0.0221 | 46.0 | 17250 | 1.3105 | 0.82 |
| 0.0362 | 47.0 | 17625 | 1.3368 | 0.8317 |
| 0.0008 | 48.0 | 18000 | 1.2965 | 0.8317 |
| 0.0038 | 49.0 | 18375 | 1.2931 | 0.8317 |
| 0.0178 | 50.0 | 18750 | 1.3262 | 0.8217 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Kaue123456/PatatiPauloTeixeira
|
Kaue123456
| 2023-12-16T00:57:12Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T00:55:57Z |
---
license: openrail
---
|
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] |
ihkl/uiui
|
ihkl
| 2023-12-16T00:58:01Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T00:58:01Z |
---
license: mit
---
|
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] |
Kaue123456/PatataJuniorRangel
|
Kaue123456
| 2023-12-16T01:01:10Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T00:58:29Z |
---
license: openrail
---
|
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] |
ManuRasputin/Yuki
|
ManuRasputin
| 2023-12-16T01:06:56Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T01:00:54Z |
---
license: openrail
---
|
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] |
ihkl/kujk
|
ihkl
| 2023-12-16T01:02:26Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T01:02:26Z |
---
license: openrail
---
|
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AxiomDEV/WFT_REALISTIC
|
AxiomDEV
| 2023-12-16T01:22:57Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-16T01:06:37Z |
---
license: unknown
---
|
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] |
joeldabest638/JuniorAsparagus
|
joeldabest638
| 2023-12-16T01:20:17Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T01:06:41Z |
---
license: openrail
---
|
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] |
MaxReynolds/SouderRocketLauncherNetCombined-XL-v0
|
MaxReynolds
| 2023-12-16T13:44:40Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2023-12-16T01:18:48Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-xl-base-1.0
dataset: MaxReynolds/Lee_Souder_Combined
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - MaxReynolds/SouderRocketLauncherNetCombined-XL-v0
This pipeline was finetuned from **stabilityai/stable-diffusion-xl-base-1.0** on the **MaxReynolds/Lee_Souder_Combined** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: Rocket Launcher by Lee Souder:
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
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raunak-pandey/falcon-7b-sharded-finetuned-mental-health-conversational
|
raunak-pandey
| 2023-12-16T01:19:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T01:19:56Z |
Entry not found
|
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Dadew/Novus
|
Dadew
| 2023-12-16T01:20:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T01:20:58Z |
Entry not found
|
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] |
reboot168/Llama-2-7b-chat-hf-fine-tuned-v2
|
reboot168
| 2023-12-16T01:27:27Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T01:22:40Z |
Entry not found
|
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] |
ujjirox/llama-2-13b-hf
|
ujjirox
| 2023-12-16T01:24:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"facebook",
"meta",
"llama-2",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T01:24:08Z |
---
inference: false
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
<!-- header start -->
<div style="width: 100%;">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Meta's Llama 2 13B fp16
These files are fp16 format model files for [Meta's Llama 2 13B](https://huggingface.co/meta-llama/Llama-2-13b-hf).
They were produced by downloading the PTH files from Meta, and then converting to HF format using the latest Transformers 4.32.0.dev0, from Git, with the Llama 2 PR included: https://github.com/huggingface/transformers/pull/24891.
Command to convert was:
```
python3 /workspace/venv/pytorch2/lib/python3.10/site-packages/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir /workspace/git/llama/download --model_size 13B --output_dir /workspace/process/llama-2-13b/source
```
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-13B-GPTQ)
* [Original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-13b-hf)
* [My fp16 conversion of the unquantised PTH model files](https://huggingface.co/TheBloke/Llama-2-13B-fp16)
## Prompt template: None
```
{prompt}
```
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Meta's Llama 2 13B
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
|
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] |
LoneStriker/Nous-Capybara-limarpv3-34B-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T04:48:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T01:25:32Z |
Entry not found
|
[
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afrideva/no-prompt-1.3b-GGUF
|
afrideva
| 2023-12-16T01:32:16Z | 0 | 1 | null |
[
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"en",
"dataset:appvoid/no-prompt-15k",
"base_model:appvoid/no-prompt-1.3b",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-16T01:27:38Z |
---
base_model: appvoid/no-prompt-1.3b
datasets:
- appvoid/no-prompt-15k
inference: false
language:
- en
license: apache-2.0
model_creator: appvoid
model_name: no-prompt-1.3b
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# appvoid/no-prompt-1.3b-GGUF
Quantized GGUF model files for [no-prompt-1.3b](https://huggingface.co/appvoid/no-prompt-1.3b) from [appvoid](https://huggingface.co/appvoid)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [no-prompt-1.3b.fp16.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.fp16.gguf) | fp16 | 2.69 GB |
| [no-prompt-1.3b.q2_k.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q2_k.gguf) | q2_k | 631.52 MB |
| [no-prompt-1.3b.q3_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q3_k_m.gguf) | q3_k_m | 704.72 MB |
| [no-prompt-1.3b.q4_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q4_k_m.gguf) | q4_k_m | 873.27 MB |
| [no-prompt-1.3b.q5_k_m.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q5_k_m.gguf) | q5_k_m | 1.00 GB |
| [no-prompt-1.3b.q6_k.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q6_k.gguf) | q6_k | 1.17 GB |
| [no-prompt-1.3b.q8_0.gguf](https://huggingface.co/afrideva/no-prompt-1.3b-GGUF/resolve/main/no-prompt-1.3b.q8_0.gguf) | q8_0 | 1.43 GB |
## Original Model Card:
# no-prompt
### a sheared-llama-1.3b fine-tuning
This model uses an 1.3 billion parameters model as base to be further fine-tuned on the same data as palmer. It works pretty good and even surpasses sota model on `hellaswag`.
### evaluation
|Model| ARC_C| HellaSwag| PIQA| Winogrande|
|------|-----|-----------|------|-------------|
|tinyllama-2t| 0.2807| 0.5463| 0.7067| 0.5683|
|palmer-001 | 0.2807| 0.5524| 0.7106| 0.5896|
|sheared-1.3b| 0.2910| 0.5935| 0.7339| 0.5809|
|no-prompt-1.3b| 0.3157| **0.6022**| 0.7334| 0.5864|
|falcon-rw-1b-instruct-openorca (sota) | **0.3362**| 0.5997| **0.7394**| **0.6148**|
This model was trained on less than 25% of the dataset yet achieves competitive performance to current sota on open llm leaderboard.
### training
Training took ~5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. no-prompt was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.
### prompt
```
no prompt
```
### limitations
Hallucinations are frequent, just as any transformer model this size.
<a href="https://ko-fi.com/appvoid" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 48px !important;width: 180px !important; filter: invert(70%);" ></a>
|
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aClicheName/llama-2-7b-chat-gptq-peft
|
aClicheName
| 2023-12-16T01:28:57Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TheBloke/Llama-2-7b-Chat-GPTQ",
"region:us"
] | null | 2023-12-16T01:28:50Z |
---
library_name: peft
base_model: TheBloke/Llama-2-7b-Chat-GPTQ
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- Relevant interpretability work for the model goes here -->
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## 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).
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## Model Card Contact
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### Framework versions
- PEFT 0.7.1
|
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] |
Nourrr/long-t5-tglobal-base
|
Nourrr
| 2023-12-18T00:22:27Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-16T01:30:01Z |
---
license: apache-2.0
language: en
---
# LongT5 (transient-global attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/t5x).
Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.
LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens).
## Intended uses & limitations
The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you.
### How to use
```python
from transformers import AutoTokenizer, LongT5Model
tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base")
model = LongT5Model.from_pretrained("google/long-t5-tglobal-base")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{guo2021longt5,
title={LongT5: Efficient Text-To-Text Transformer for Long Sequences},
author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei},
journal={arXiv preprint arXiv:2112.07916},
year={2021}
}
```
|
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giwone1330/whisper-small-hi
|
giwone1330
| 2023-12-16T01:32:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T01:32:16Z |
Entry not found
|
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ppbrown/absolutereality-unmerge
|
ppbrown
| 2023-12-16T01:55:19Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T01:47:55Z |
---
license: openrail
---
The concept of this, is that it can turn out good looking results, at low CFG and step numbers.
("low" being 20 steps, or sometimes even 12 or so)
Borrowed from https://huggingface.co/Lykon/AbsoluteReality
Passed through the workflow here, with modelsubtract actually set to .40 instead of my usual .20
Its important to actually test the outputs!
My test image showed a little ick at .20 and .30, so.. i went with .40
ComfyUI workflow to create this version from the upstream included
|
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giwone1330/whisper-small-ja
|
giwone1330
| 2023-12-16T01:51:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T01:51:27Z |
Entry not found
|
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] |
stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep14
|
stvhuang
| 2023-12-16T01:59:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T01:56:47Z |
Entry not found
|
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dsstr/prmre
|
dsstr
| 2023-12-16T02:07:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T02:01:38Z |
Entry not found
|
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] |
kroonen/phi-2-GGUF
|
kroonen
| 2023-12-16T18:32:01Z | 0 | 16 | null |
[
"gguf",
"phi",
"phi2",
"microsoft",
"llama",
"llama2",
"llamacpp",
"license:mit",
"has_space",
"region:us"
] | null | 2023-12-16T02:06:58Z |
---
license: mit
tags:
- phi
- phi2
- gguf
- microsoft
- llama
- llama2
- llamacpp
---
This is a quantized GGUF version of Microsoft Phi-2 to 4_0, 8_0 bits and the converted to GGUF 16FP model.
(link to the original model : https://huggingface.co/microsoft/phi-2)
*Disclamer* : It won't work unless you use this fork with those modofication (https://github.com/mrgraycode/llama.cpp/commit/12cc80cb8975aea3bc9f39d3c9b84f7001ab94c5#diff-150dc86746a90bad4fc2c3334aeb9b5887b3adad3cc1459446717638605348efR6239) of llamacpp : https://github.com/mrgraycode/llama.cpp as the model's architecture is different.
|
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nolsman/q-FrozenLake-v1-4x4-noSlippery
|
nolsman
| 2023-12-16T02:08:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T02:08:08Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="nolsman/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
phucdyale/whisper-small-ko
|
phucdyale
| 2023-12-16T02:09:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ko",
"dataset:mozilla-foundation/common_voice_13_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-16T02:09:05Z |
---
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
model-index:
- name: Whisper Small - ko 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. -->
# Whisper Small - ko Model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
aisuko/ft-ms-git-base-pokemon-blip-captions
|
aisuko
| 2023-12-16T07:37:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"git",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/git-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-16T02:09:38Z |
---
license: mit
base_model: microsoft/git-base
tags:
- generated_from_trainer
model-index:
- name: ft-ms-git-base-pokemon-blip-captions
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. -->
# ft-ms-git-base-pokemon-blip-captions
This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
AbdelrahmanEssmat/long-t5-local-base
|
AbdelrahmanEssmat
| 2023-12-16T04:52:38Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"safetensors",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-16T02:11:37Z |
---
license: apache-2.0
language: en
---
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/t5x).
Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.
LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens).
## Intended uses & limitations
The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you.
### How to use
```python
from transformers import AutoTokenizer, LongT5Model
tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
model = LongT5Model.from_pretrained("google/long-t5-local-base")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{guo2021longt5,
title={LongT5: Efficient Text-To-Text Transformer for Long Sequences},
author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei},
journal={arXiv preprint arXiv:2112.07916},
year={2021}
}
```
|
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] |
Zuntan/JVNV-F2_Bert-VITS2_v2.1
|
Zuntan
| 2023-12-16T02:39:28Z | 0 | 0 |
transformers
|
[
"transformers",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T02:13:38Z |
---
license: cc-by-sa-4.0
---
[JVNV](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvnv_corpus) の F2 を [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2) の v2.1 で学習して、compress_model.py で圧縮したものです。
ライセンスは JVNV の [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ja) を引き継ぎます。
|
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] |
AIFT/aift-llama2-koen-instruct-v1.2
|
AIFT
| 2023-12-16T03:14:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T02:16:34Z |
---
license: cc-by-nc-4.0
---
|
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nolsman/Taxi-v3
|
nolsman
| 2023-12-16T02:17:59Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T02:17:57Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
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="nolsman/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"])
```
|
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EmmaGthn/runs_lora_10000_moji
|
EmmaGthn
| 2023-12-16T13:22:50Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-16T02:18:34Z |
Entry not found
|
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db4/DANIELLE
|
db4
| 2023-12-16T02:21:53Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T02:19:01Z |
Entry not found
|
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nolsman/Taxi-v3-2
|
nolsman
| 2023-12-16T02:30:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T02:30:31Z |
Entry not found
|
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] |
wonione/coverletter-generator
|
wonione
| 2023-12-16T07:01:44Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"ko",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-16T02:37:16Z |
---
language:
- ko
library_name: transformers
pipeline_tag: text-generation
---
pretrained_model_name="skt/kogpt2-base-v2"
|
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vigneshgs7/segformer-b0-finetuned-segments-docboundary-dec-16_kaggletrial2-88images
|
vigneshgs7
| 2023-12-16T02:43:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T02:43:34Z |
Entry not found
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themariolinml/roberta-base-sqaud2-on-medical_meadow_medqa-v2
|
themariolinml
| 2023-12-16T02:43:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T02:43:38Z |
Entry not found
|
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vilm/vinallama-7b-chat-GGUF
|
vilm
| 2023-12-16T03:25:08Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-16T02:46:22Z |
Entry not found
|
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PowerInfer/ReluLLaMA-70B-PowerInfer-GGUF
|
PowerInfer
| 2023-12-16T09:00:41Z | 0 | 0 | null |
[
"gguf",
"en",
"license:llama2",
"region:us"
] | null | 2023-12-16T02:51:46Z |
---
license: llama2
language:
- en
---
# ReluLLaMA-70B-PowerInfer-GGUF
- Original model: [SparseLLM/ReluLLaMA-70B](https://huggingface.co/SparseLLM/ReluLLaMA-70B)
- Converted & distributed by: [PowerInfer](https://huggingface.co/PowerInfer)
This model is the downstream distribution of [SparseLLM/ReluLLaMA-70B](https://huggingface.co/SparseLLM/ReluLLaMA-70B) in PowerInfer GGUF format consisting of the LLM model weights and predictor weights.
|
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danielmalencar/llamaDaniel
|
danielmalencar
| 2023-12-16T02:55:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T02:55:48Z |
Entry not found
|
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] |
istemedu/Qwen-72B-Chat-awq
|
istemedu
| 2023-12-16T08:01:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen",
"text-generation",
"custom_code",
"autotrain_compatible",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-16T02:56:02Z |
Entry not found
|
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InquisitorNova/InquisitorNovaCycloneStudy_2
|
InquisitorNova
| 2023-12-16T03:01:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:01:22Z |
Entry not found
|
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] |
Ttonio/xlm-roberta-base-finetuned-panx-de
|
Ttonio
| 2023-12-16T11:26:48Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-16T03:01:56Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8627450980392157
---
<!-- 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
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1407
- F1: 0.8627
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2585 | 1.0 | 525 | 0.1537 | 0.8285 |
| 0.1274 | 2.0 | 1050 | 0.1435 | 0.8474 |
| 0.0784 | 3.0 | 1575 | 0.1407 | 0.8627 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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EmmaGthn/runs_prefix_10000_moji
|
EmmaGthn
| 2023-12-16T08:41:37Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-16T03:03:09Z |
Entry not found
|
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Fadeca/Bobby
|
Fadeca
| 2023-12-16T03:13:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:08:54Z |
Entry not found
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_humanMix_Seed106
|
behzadnet
| 2023-12-16T03:11:29Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-16T03:11:23Z |
---
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
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### 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]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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## 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
## 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
|
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_humanMix_Seed106
|
behzadnet
| 2023-12-16T03:11:37Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-12-16T03:11:35Z |
---
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
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## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### 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]
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#### Metrics
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### Results
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#### Summary
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## 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).
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## 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
|
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] |
SG161222/RealVisXL_V3.0
|
SG161222
| 2023-12-16T03:15:52Z | 0 | 1 | null |
[
"license:openrail++",
"region:us"
] | null | 2023-12-16T03:14:39Z |
---
license: openrail++
---
<b>It's important! Read it!</b><br>
The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.<br>
The model is aimed at photorealism. Can produce sfw and nsfw images of decent quality.<br>
CivitAI Page: ---<br>
<b>Recommended Negative Prompt:</b><br>
(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth<br>
<b>or another negative prompt</b><br>
<b>Recommended Generation Parameters:</b><br>
Sampling Steps: 15-30<br>
Sampling Method: DPM++ SDE Karras<br>
|
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fagundesyuri13/keria
|
fagundesyuri13
| 2023-12-16T03:31:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:15:58Z |
Entry not found
|
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lcybuaa/PSNet
|
lcybuaa
| 2023-12-16T03:32:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:18:39Z |
Entry not found
|
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] |
areegtarek/openchat-finetuned
|
areegtarek
| 2023-12-16T03:20:59Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-16T03:20:21Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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] |
areegtarek/mistral-7b-finetuned
|
areegtarek
| 2023-12-16T03:21:44Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-16T03:21:11Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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athirdpath/HOMELESS__UPDATES_PAUSED
|
athirdpath
| 2023-12-16T06:38:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:26:20Z |
Despite my best efforts, and after much fighting, I lost my home today. I have nowhere to go or to set up my computer. I promise it's not because I overspent on compute ;)
Updates are paused indefinitely.
|
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] |
AIFT/aift-llama2-koen-instruct-v1.1-dpo-test1
|
AIFT
| 2023-12-16T07:45:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T03:30:37Z |
---
license: cc-by-sa-4.0
---
|
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] |
LilYoda/PboyMax
|
LilYoda
| 2023-12-16T07:18:01Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T03:30:59Z |
---
license: openrail
---
|
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vpgits/Mistral-7B-v0.1-qagen-v0.5
|
vpgits
| 2023-12-16T04:11:58Z | 0 | 0 | null |
[
"safetensors",
"license:mit",
"region:us"
] | null | 2023-12-16T03:39:36Z |
---
license: mit
---
|
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qxnam/distilgpt2-finetuned-finance-20epochs
|
qxnam
| 2023-12-16T03:44:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:44:07Z |
Entry not found
|
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angelahzyuan/zephyr-7b-dpo-lora
|
angelahzyuan
| 2023-12-16T03:45:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:45:30Z |
Entry not found
|
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JON1maryanto/emem
|
JON1maryanto
| 2023-12-16T03:46:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:46:56Z |
Entry not found
|
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Intaa/AoiAkiraRVCUTAU
|
Intaa
| 2023-12-16T03:54:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:50:04Z |
Entry not found
|
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prasana06/chatbot
|
prasana06
| 2023-12-16T03:50:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T03:50:43Z |
Entry not found
|
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] |
TheLoko/Llama-2-7b-chat-finetune
|
TheLoko
| 2023-12-16T04:20:38Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-12-16T03:54:14Z |
---
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
### Framework versions
- PEFT 0.4.0
|
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Selvennn/HE_Jiong
|
Selvennn
| 2023-12-16T04:10:23Z | 0 | 0 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2023-12-16T04:10:23Z |
---
license: cc-by-4.0
---
|
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dasilva20/w
|
dasilva20
| 2023-12-16T04:15:42Z | 0 | 0 | null |
[
"license:gpl",
"region:us"
] | null | 2023-12-16T04:15:24Z |
---
license: gpl
metrics:
- bertscore
---
|
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] |
damnedraxx/Taylor_Swift_1989
|
damnedraxx
| 2023-12-16T04:22:51Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T04:18:14Z |
---
license: openrail
---
|
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] |
wcdshaohua01/codegen-350M-mono-python-18k-alpaca
|
wcdshaohua01
| 2023-12-16T04:26:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-16T04:25:51Z |
Entry not found
|
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] |
LoneStriker/Nous-Capybara-limarpv3-34B-8.0bpw-h8-exl2-2
|
LoneStriker
| 2023-12-16T04:48:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T04:27:09Z |
Entry not found
|
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] |
venkatpr/llama7b_chat_sys_promt_epoch-GGUF-2
|
venkatpr
| 2023-12-16T04:42:34Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T04:31:54Z |
Entry not found
|
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] |
EWCH/audio-classification-minds14
|
EWCH
| 2023-12-16T04:37:27Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:minds14",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-12-16T04:32:39Z |
---
tags:
- generated_from_trainer
datasets:
- minds14
metrics:
- accuracy
model-index:
- name: audio-classification-minds14
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: minds14
type: minds14
config: en-US
split: train
args: en-US
metrics:
- name: Accuracy
type: accuracy
value: 0.061946902654867256
---
<!-- 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. -->
# audio-classification-minds14
This model was trained from scratch on the minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6501
- Accuracy: 0.0619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.8 | 3 | 2.6374 | 0.1062 |
| No log | 1.87 | 7 | 2.6353 | 0.0973 |
| 2.6354 | 2.93 | 11 | 2.6417 | 0.0885 |
| 2.6354 | 4.0 | 15 | 2.6444 | 0.0796 |
| 2.6354 | 4.8 | 18 | 2.6440 | 0.0708 |
| 2.6209 | 5.87 | 22 | 2.6470 | 0.0708 |
| 2.6209 | 6.93 | 26 | 2.6484 | 0.0619 |
| 2.6178 | 8.0 | 30 | 2.6501 | 0.0619 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Bonkyyyyy/Kit
|
Bonkyyyyy
| 2023-12-16T04:35:39Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-12-16T04:35:39Z |
---
license: other
license_name: hola
license_link: LICENSE
---
|
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] |
JSPD/Kapampangan
|
JSPD
| 2023-12-16T04:38:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T04:38:43Z |
Entry not found
|
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] |
macab/LLM_book_5.4
|
macab
| 2023-12-16T04:49:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T04:41:39Z |
Entry not found
|
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100yen/distilbert-base-uncased-distilled-clinc
|
100yen
| 2023-12-16T04:57:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T04:51:50Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9493548387096774
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2862
- Accuracy: 0.9494
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 1.7390 | 0.7171 |
| 2.0984 | 2.0 | 636 | 0.8746 | 0.8616 |
| 2.0984 | 3.0 | 954 | 0.5056 | 0.9168 |
| 0.7905 | 4.0 | 1272 | 0.3769 | 0.9371 |
| 0.3725 | 5.0 | 1590 | 0.3263 | 0.9461 |
| 0.3725 | 6.0 | 1908 | 0.3055 | 0.9461 |
| 0.2654 | 7.0 | 2226 | 0.2966 | 0.9471 |
| 0.2335 | 8.0 | 2544 | 0.2897 | 0.9506 |
| 0.2335 | 9.0 | 2862 | 0.2871 | 0.9494 |
| 0.2203 | 10.0 | 3180 | 0.2862 | 0.9494 |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Puteors/TEst-pygma
|
Puteors
| 2023-12-16T05:03:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T05:03:06Z |
Entry not found
|
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