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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
wolferobert3/mistral_factcheck-test | wolferobert3 | 2023-11-29T23:06:01Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"region:us"
] | 2023-11-29T23:06:01Z | 2023-11-29T22:44:42.000Z | null | null | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.1
---
# Model Card for Model ID
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## Model Details
### Model Description
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## Training Details
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#### Preprocessing [optional]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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 0.6.2
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Picoloamg/numspk_model | Picoloamg | 2023-11-30T00:05:18Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:05:18Z | 2023-11-29T22:45:47.000Z | null | null | Entry not found | null | transformers | audio-classification | null | null | null | null | null | null | null | null | null | Picoloamg/numspk_model | [
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GoatMilk98/635-a2-bert | GoatMilk98 | 2023-11-29T22:49:21Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:49:21Z | 2023-11-29T22:48:40.000Z | null | null | Entry not found | null | transformers | question-answering | null | null | null | null | null | null | null | null | null | GoatMilk98/635-a2-bert | [
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Vise/visemodel | Vise | 2023-11-29T22:50:15Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:50:15Z | 2023-11-29T22:49:25.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Vise/visemodel | [
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Realgon/roberta_sst5_padding80model | Realgon | 2023-11-30T00:20:17Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:20:17Z | 2023-11-29T22:49:26.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst5_padding80model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# roberta_sst5_padding80model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5247
- Accuracy: 0.5624
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2916 | 1.0 | 534 | 1.3748 | 0.4484 |
| 1.0328 | 2.0 | 1068 | 1.0661 | 0.5317 |
| 0.8673 | 3.0 | 1602 | 1.1531 | 0.5195 |
| 0.7557 | 4.0 | 2136 | 1.1215 | 0.5692 |
| 0.6258 | 5.0 | 2670 | 1.2796 | 0.5367 |
| 0.528 | 6.0 | 3204 | 1.4486 | 0.5412 |
| 0.413 | 7.0 | 3738 | 1.5099 | 0.5511 |
| 0.3434 | 8.0 | 4272 | 1.6205 | 0.5552 |
| 0.2961 | 9.0 | 4806 | 1.7024 | 0.5394 |
| 0.2508 | 10.0 | 5340 | 2.1916 | 0.5398 |
| 0.2382 | 11.0 | 5874 | 2.5628 | 0.5357 |
| 0.1928 | 12.0 | 6408 | 2.6311 | 0.5534 |
| 0.1934 | 13.0 | 6942 | 2.9223 | 0.5489 |
| 0.157 | 14.0 | 7476 | 3.1200 | 0.5493 |
| 0.1235 | 15.0 | 8010 | 3.1288 | 0.5498 |
| 0.0967 | 16.0 | 8544 | 3.2570 | 0.5620 |
| 0.0911 | 17.0 | 9078 | 3.3236 | 0.5606 |
| 0.0698 | 18.0 | 9612 | 3.4772 | 0.5575 |
| 0.0595 | 19.0 | 10146 | 3.4699 | 0.5656 |
| 0.0579 | 20.0 | 10680 | 3.5247 | 0.5624 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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ashahopkins/GymbroChatbot | ashahopkins | 2023-11-29T22:52:42Z | 0 | 0 | null | [
"keras",
"region:us"
] | 2023-11-29T22:52:42Z | 2023-11-29T22:49:58.000Z | null | null | Entry not found | null | keras | null | null | null | null | null | null | null | null | null | null | ashahopkins/GymbroChatbot | [
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mochxxtea/MochxxteaRVC | mochxxtea | 2023-11-29T22:52:17Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T22:52:17Z | 2023-11-29T22:50:34.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | mochxxtea/MochxxteaRVC | [
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LavenderGirl99/SoyeonofGIDLE | LavenderGirl99 | 2023-11-29T23:03:19Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:03:19Z | 2023-11-29T22:51:30.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LavenderGirl99/SoyeonofGIDLE | [
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Xzh0u/results | Xzh0u | 2023-11-29T22:54:48Z | 0 | 0 | null | [
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jordandavis/bishop_ebonized_wood_credenza | jordandavis | 2023-11-29T22:57:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T22:57:02Z | 2023-11-29T22:57:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jordandavis/bishop_ebonized_wood_credenza | [
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matt-le-kat/mistral | matt-le-kat | 2023-11-29T22:58:19Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T22:58:19Z | 2023-11-29T22:58:19.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | matt-le-kat/mistral | [
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BlahBlah314/whisper-large-presc-test3 | BlahBlah314 | 2023-11-29T22:58:56Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:openai/whisper-large-v3",
"license:apache-2.0",
"region:us"
] | 2023-11-29T22:58:56Z | 2023-11-29T22:58:54.000Z | null | null | ---
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-presc-test3
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-large-presc-test3
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1383
- Wer: 13.1472
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 1.7015 | 0.52 | 50 | 1.8507 | 41.0880 |
| 1.6787 | 1.03 | 100 | 1.7603 | 39.1076 |
| 1.6409 | 1.55 | 150 | 1.6623 | 33.0709 |
| 1.64 | 2.06 | 200 | 1.5705 | 26.6046 |
| 1.3699 | 2.58 | 250 | 1.4675 | 21.8564 |
| 1.2186 | 3.09 | 300 | 1.3259 | 18.2057 |
| 1.1032 | 3.61 | 350 | 1.2183 | 15.1515 |
| 0.9002 | 4.12 | 400 | 1.1694 | 13.8392 |
| 0.9697 | 4.64 | 450 | 1.1456 | 13.4574 |
| 1.1882 | 5.15 | 500 | 1.1383 | 13.1472 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | BlahBlah314/whisper-large-presc-test3 | [
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jonduea/ppo-LunarLander-v2 | jonduea | 2023-11-29T22:59:21Z | 0 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T22:59:21Z | 2023-11-29T22:59:01.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 260.08 +/- 17.53
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | jonduea/ppo-LunarLander-v2 | [
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] |
Homiebear/MilesProwler | Homiebear | 2023-11-29T23:04:34Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T23:04:34Z | 2023-11-29T23:04:14.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Homiebear/MilesProwler | [
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LoneStriker/deepseek-llm-67b-chat-6.0bpw-h6-exl2 | LoneStriker | 2023-11-30T00:11:05Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:11:05Z | 2023-11-29T23:06:43.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-llm-67b-chat-6.0bpw-h6-exl2 | [
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SauloFerraz/ModelMB | SauloFerraz | 2023-11-29T23:21:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:21:18Z | 2023-11-29T23:08:37.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | SauloFerraz/ModelMB | [
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TheBloke/deepseek-llm-7B-base-GPTQ | TheBloke | 2023-11-29T23:42:58Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:deepseek-ai/deepseek-llm-7b-base",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T23:42:58Z | 2023-11-29T23:14:32.000Z | null | null | ---
base_model: deepseek-ai/deepseek-llm-7b-base
inference: false
license: other
license_link: LICENSE
license_name: deepseek
model_creator: DeepSeek
model_name: Deepseek LLM 7B Base
model_type: deepseek
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Deepseek LLM 7B Base - GPTQ
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base)
<!-- description start -->
# Description
This repo contains GPTQ model files for [DeepSeek's Deepseek LLM 7B Base](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-base)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.84 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.20 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.76 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.90 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.33 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.96 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/deepseek-llm-7B-base-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/deepseek-llm-7B-base-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `deepseek-llm-7B-base-GPTQ`:
```shell
mkdir deepseek-llm-7B-base-GPTQ
huggingface-cli download TheBloke/deepseek-llm-7B-base-GPTQ --local-dir deepseek-llm-7B-base-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir deepseek-llm-7B-base-GPTQ
huggingface-cli download TheBloke/deepseek-llm-7B-base-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir deepseek-llm-7B-base-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir deepseek-llm-7B-base-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-llm-7B-base-GPTQ --local-dir deepseek-llm-7B-base-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/deepseek-llm-7B-base-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/deepseek-llm-7B-base-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/deepseek-llm-7B-base-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `deepseek-llm-7B-base-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/deepseek-llm-7B-base-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/deepseek-llm-7B-base-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: DeepSeek's Deepseek LLM 7B Base
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-7b-base` is a 7B parameter model with Multi-Head Attention trained on 2 trillion tokens from scratch.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Text Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-7b-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
text = "An attention function can be described as mapping a query and a set of key-value pairs to an output, where the query, keys, values, and output are all vectors. The output is"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
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typeof/nano-mistral | typeof | 2023-11-29T23:15:46Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T23:15:46Z | 2023-11-29T23:15:25.000Z | null | null | Invalid username or password. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | typeof/nano-mistral | [
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talkie/silver_retriever_med_dom_speakleash_10 | talkie | 2023-11-29T23:17:46Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:17:46Z | 2023-11-29T23:16:24.000Z | null | null | Entry not found | null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | talkie/silver_retriever_med_dom_speakleash_10 | [
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LavenderGirl99/KaiSaofKDA | LavenderGirl99 | 2023-11-29T23:21:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:21:10Z | 2023-11-29T23:19:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LavenderGirl99/KaiSaofKDA | [
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GoatMilk98/635-a2-albert | GoatMilk98 | 2023-11-29T23:20:38Z | 0 | 0 | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:20:38Z | 2023-11-29T23:20:02.000Z | null | null | Entry not found | null | transformers | question-answering | null | null | null | null | null | null | null | null | null | GoatMilk98/635-a2-albert | [
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DigitalKandra/list_trimmer | DigitalKandra | 2023-11-29T23:20:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T23:20:11Z | 2023-11-29T23:20:11.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | DigitalKandra/list_trimmer | [
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brian-dlee/bert-emotion | brian-dlee | 2023-11-29T23:28:59Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:28:59Z | 2023-11-29T23:20:58.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- precision
- recall
model-index:
- name: bert-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- name: Precision
type: precision
value: 0.7250217383862243
- name: Recall
type: recall
value: 0.7243162915142891
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-emotion
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1060
- Precision: 0.7250
- Recall: 0.7243
- Fscore: 0.7225
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.8399 | 1.0 | 815 | 0.6333 | 0.7495 | 0.7146 | 0.7265 |
| 0.5379 | 2.0 | 1630 | 0.8113 | 0.7660 | 0.7188 | 0.7357 |
| 0.2567 | 3.0 | 2445 | 1.1060 | 0.7250 | 0.7243 | 0.7225 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0
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saludobuenas/test2 | saludobuenas | 2023-11-29T23:22:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:22:22Z | 2023-11-29T23:22:22.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | saludobuenas/test2 | [
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StringCheese/squad-v2-bloom-1b | StringCheese | 2023-11-29T23:22:44Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b7",
"region:us"
] | 2023-11-29T23:22:44Z | 2023-11-29T23:22:42.000Z | null | null | ---
library_name: peft
base_model: bigscience/bloom-1b7
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | StringCheese/squad-v2-bloom-1b | [
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CtrlX/ModelReady-pretrain | CtrlX | 2023-11-30T00:12:38Z | 0 | 0 | null | [
"onnx",
"license:mit",
"region:us"
] | 2023-11-30T00:12:38Z | 2023-11-29T23:22:56.000Z | null | null | ---
license: mit
---
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InductiveGrub/RatBoyGenius | InductiveGrub | 2023-11-29T23:25:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:25:01Z | 2023-11-29T23:23:06.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | InductiveGrub/RatBoyGenius | [
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kerianheYi/CS245-fine-tunedSD150_708 | kerianheYi | 2023-11-29T23:30:38Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dataset:jytjyt05/t_to_m3",
"base_model:stabilityai/stable-diffusion-2-base",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T23:30:38Z | 2023-11-29T23:23:15.000Z | null | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-base
datasets:
- jytjyt05/t_to_m3
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD150_708
This pipeline was finetuned from **stabilityai/stable-diffusion-2-base** on the **jytjyt05/t_to_m3** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['C-Major, Piano, Solo, Ultra High Resolution']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("kerianheYi/CS245-fine-tunedSD150_708", torch_dtype=torch.float16)
prompt = "C-Major, Piano, Solo, Ultra High Resolution"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 1
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | kerianheYi/CS245-fine-tunedSD150_708 | [
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staticai/Dlama | staticai | 2023-11-29T23:23:18Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:23:18Z | 2023-11-29T23:23:18.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | staticai/Dlama | [
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peldrak/maskformer-finetuned-riviera | peldrak | 2023-11-29T23:25:50Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:25:50Z | 2023-11-29T23:25:50.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | peldrak/maskformer-finetuned-riviera | [
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tkcho/cp-commerce-clf-kr-sku-brand-650cb710962d60c4fc850a47c2aca5a5 | tkcho | 2023-11-29T23:26:43Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:26:43Z | 2023-11-29T23:26:04.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/cp-commerce-clf-kr-sku-brand-650cb710962d60c4fc850a47c2aca5a5 | [
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paul-w-qs/handling_charges_v1_model | paul-w-qs | 2023-11-29T23:26:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:26:23Z | 2023-11-29T23:26:23.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | paul-w-qs/handling_charges_v1_model | [
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aryanguls/mistral_conversation_finetuned_test | aryanguls | 2023-11-29T23:26:41Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:alexsherstinsky/Mistral-7B-v0.1-sharded",
"region:us"
] | 2023-11-29T23:26:41Z | 2023-11-29T23:26:27.000Z | null | null | ---
library_name: peft
base_model: alexsherstinsky/Mistral-7B-v0.1-sharded
---
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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.
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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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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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#### Summary
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## Environmental Impact
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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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## Technical Specifications [optional]
### Model Architecture and Objective
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.2
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Rigosaenz/goodman | Rigosaenz | 2023-11-29T23:29:34Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T23:29:34Z | 2023-11-29T23:27:46.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Rigosaenz/goodman | [
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jordandavis/curvo_camel_velvet_sofa_pin_id | jordandavis | 2023-11-30T00:59:42Z | 0 | 0 | null | [
"diffusers",
"if",
"if-diffusers",
"inpaint",
"lora",
"base_model:runwayml/stable-diffusion-inpainting",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-30T00:59:42Z | 2023-11-29T23:28:16.000Z | null | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-inpainting
instance_prompt: 209135976437775112 sofa
tags:
- if
- if-diffusers
- inpaint
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - jordandavis/curvo_camel_velvet_sofa_pin_id
These are LoRA adaption weights for runwayml/stable-diffusion-inpainting. The weights were trained on 209135976437775112 sofa using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
| null | diffusers | null | null | null | null | null | null | null | null | null | null | jordandavis/curvo_camel_velvet_sofa_pin_id | [
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beom115/temp | beom115 | 2023-11-30T01:26:35Z | 0 | 0 | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:26:35Z | 2023-11-29T23:29:08.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | beom115/temp | [
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raulgdp/roberta-large-bne-capitel-ner | raulgdp | 2023-11-29T23:29:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:29:13Z | 2023-11-29T23:29:13.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | raulgdp/roberta-large-bne-capitel-ner | [
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LoneStriker/deepseek-coder-33b-instruct-6.0bpw-h6-exl2 | LoneStriker | 2023-11-29T23:31:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:31:26Z | 2023-11-29T23:31:26.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-coder-33b-instruct-6.0bpw-h6-exl2 | [
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LoneStriker/deepseek-coder-33b-instruct-8.0bpw-h8-exl2 | LoneStriker | 2023-11-29T23:31:44Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:31:44Z | 2023-11-29T23:31:44.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-coder-33b-instruct-8.0bpw-h8-exl2 | [
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fiastros/videomae-base-finetuned-ucf101-subset | fiastros | 2023-11-29T23:32:17Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:32:17Z | 2023-11-29T23:32:17.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | fiastros/videomae-base-finetuned-ucf101-subset | [
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Angelinis/bigbird-roberta-base-finetuned-enem | Angelinis | 2023-11-30T00:01:43Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"big_bird",
"multiple-choice",
"generated_from_trainer",
"base_model:google/bigbird-roberta-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:01:43Z | 2023-11-29T23:33:12.000Z | null | null | ---
license: apache-2.0
base_model: google/bigbird-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bigbird-roberta-base-finetuned-enem
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. -->
# bigbird-roberta-base-finetuned-enem
This model is a fine-tuned version of [google/bigbird-roberta-base](https://huggingface.co/google/bigbird-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6094
- Accuracy: 0.2053
## 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: 2
- eval_batch_size: 2
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6199 | 1.0 | 933 | 1.6094 | 0.2063 |
| 1.623 | 2.0 | 1866 | 1.6094 | 0.2079 |
| 1.6214 | 3.0 | 2799 | 1.6094 | 0.2053 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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bhagya20/llama2-7b-text2SQL-pretrained | bhagya20 | 2023-11-29T23:42:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:42:24Z | 2023-11-29T23:42:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | bhagya20/llama2-7b-text2SQL-pretrained | [
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GAI-LLM/llama-2-koen-13b-dpo-v2 | GAI-LLM | 2023-11-30T00:56:58Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:56:58Z | 2023-11-29T23:42:30.000Z | null | null | ---
license: cc-by-nc-4.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | GAI-LLM/llama-2-koen-13b-dpo-v2 | [
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yeseok/gpt2-finetuned-wikitext2 | yeseok | 2023-11-29T23:42:40Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:42:40Z | 2023-11-29T23:42:40.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | yeseok/gpt2-finetuned-wikitext2 | [
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athosathosorganization/fishfishfishmodel | athosathosorganization | 2023-11-29T23:44:58Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T23:44:58Z | 2023-11-29T23:42:50.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | athosathosorganization/fishfishfishmodel | [
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TheBloke/deepseek-llm-7B-chat-GPTQ | TheBloke | 2023-11-30T00:06:51Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:deepseek-ai/deepseek-llm-7b-chat",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-30T00:06:51Z | 2023-11-29T23:43:07.000Z | null | null | ---
base_model: deepseek-ai/deepseek-llm-7b-chat
inference: false
license: other
license_link: LICENSE
license_name: deepseek
model_creator: DeepSeek
model_name: Deepseek LLM 7B Chat
model_type: deepseek
prompt_template: 'User: {prompt}
Assistant:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<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 style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Deepseek LLM 7B Chat - GPTQ
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat)
<!-- description start -->
# Description
This repo contains GPTQ model files for [DeepSeek's Deepseek LLM 7B Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: DeepSeek-LLM
```
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.84 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 5.20 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.76 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.90 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.33 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.96 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/deepseek-llm-7B-chat-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/deepseek-llm-7B-chat-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `deepseek-llm-7B-chat-GPTQ`:
```shell
mkdir deepseek-llm-7B-chat-GPTQ
huggingface-cli download TheBloke/deepseek-llm-7B-chat-GPTQ --local-dir deepseek-llm-7B-chat-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir deepseek-llm-7B-chat-GPTQ
huggingface-cli download TheBloke/deepseek-llm-7B-chat-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir deepseek-llm-7B-chat-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir deepseek-llm-7B-chat-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-llm-7B-chat-GPTQ --local-dir deepseek-llm-7B-chat-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/deepseek-llm-7B-chat-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/deepseek-llm-7B-chat-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/deepseek-llm-7B-chat-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `deepseek-llm-7B-chat-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/deepseek-llm-7B-chat-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''User: {prompt}
Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/deepseek-llm-7B-chat-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''User: {prompt}
Assistant:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: DeepSeek's Deepseek LLM 7B Chat
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 7 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-7b-chat` is a 7B parameter model initialized from `deepseek-llm-7b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-7b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
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Medilora/medilora-mistral-7b | Medilora | 2023-11-30T01:13:12Z | 0 | 0 | null | [
"peft",
"safetensors",
"en",
"dataset:Medilora/mimic_iii_diagnosis_anonymous",
"dataset:Medilora/us_medical_license_exam_textbooks_en",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | 2023-11-30T01:13:12Z | 2023-11-29T23:43:11.000Z | null | null | ---
license: mit
datasets:
- Medilora/mimic_iii_diagnosis_anonymous
- Medilora/us_medical_license_exam_textbooks_en
language:
- en
library_name: peft
---
# Model Card for Medilora/medilora-mistral-7b
Medilora-Mistral-7b is a QLoRA completion retraining of the model teknium/OpenHermes-2.5-Mistral-7B.
After training on two of the open corpus, we merge the adapter with the base model. We plan on conducting further instruction tuning.
## Model Details
- **Developed by:** The MediLora team at the University of California San Diego
- **Funded by:** sheer will
- **Model type:** Llama2
- **Language(s) (NLP):** En
- **License:** MIT License
- **Finetuned from model:** teknium/OpenHermes-2.5-Mistral-7B
## Uses
This language model, while finetuned on medical datasets, is not a substitute for professional medical advice, diagnosis, or treatment. It is primarily intended for research and development purposes in the field of medical language processing. Its applications can range from aiding in medical literature analysis to supporting the development of healthcare-related chatbots. However, it should not be used as a standalone tool for making clinical decisions.
## Direct Use
This model is designed for direct application in non-critical tasks such as preliminary data analysis, hypothesis generation, and educational purposes. It can assist researchers and developers in understanding medical language patterns and generating insights from medical texts. However, it is not intended for direct clinical use or to provide medical guidance without expert supervision and validation.
## Downstream Use [optional]
When integrated into larger systems or applications, this model can enhance functionalities like automated medical record analysis, preliminary diagnostic assistance, and information retrieval from medical databases. It is crucial that any downstream application involving this model undergoes rigorous testing and validation by medical professionals. The model should be used as a supportive tool rather than a definitive source of medical information.
## Out-of-Scope Use
This model is not designed for and should not be used in scenarios where medical decision-making, diagnosis, or treatment is involved. It is not a replacement for clinical judgment or expertise. Use in critical healthcare applications, patient care management, or any scenario requiring medical licensure is strictly out of scope. Any misuse, particularly in sensitive medical contexts, could lead to inaccurate or harmful outcomes.
## Bias, Risks, and Limitations
We did not incorporate bias-reduction techniques on the original datasets.
The model is not instruction-tuned, therefore it should not be treated or deployed as a chatbot.
### Recommendations
Given the Medilora/medilora-mistral-7b model's focus on medical diagnosis and its training on specialized datasets, it is paramount to emphasize the importance of ongoing validation against real-world clinical outcomes. The model, despite its sophistication, should not replace clinical judgment but rather augment it, considering its training on a limited language scope (English only) and specific medical contexts. Users must be aware of potential biases inherent in the training data, especially regarding diagnoses and treatment recommendations that may vary across different populations and healthcare systems. Continuous monitoring and updating of the model are recommended to ensure its alignment with the latest medical research and practices, thus minimizing risks associated with outdated or incorrect medical advice.
## How 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. -->
- [Medilora/mimic_iii_diagnosis_anonymous](https://huggingface.co/datasets/Medilora/mimic_iii_diagnosis_anonymous)
- This is an anonymized diagnosis notes extracted from the MIMIC_III dataset.
- [Medilora/us_medical_license_exam_textbooks_en](https://huggingface.co/datasets/Medilora/us_medical_license_exam_textbooks_en)
- This is the cleaned raw text corpus of the 18 textbooks contained in the USMLE Exam dataset.
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### Training Procedure
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We employ 4-bit QLoRA finetune on base model with the two datasets. We use the awesome [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) package to fire up training runs.
#### 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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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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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]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | null | peft | null | null | null | null | null | null | null | null | null | null | Medilora/medilora-mistral-7b | [
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kwang123/roberta-base-nli | kwang123 | 2023-11-30T00:35:34Z | 0 | 0 | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"zero-shot-classification",
"en",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:35:34Z | 2023-11-29T23:44:29.000Z | null | null | ---
language:
- en
metrics:
- f1
pipeline_tag: zero-shot-classification
---
# roberta-base-nli
This model was fine-tuned on the LT-EDI-ACL-2022 shared task 4 depression detection datset. | null | transformers | zero-shot-classification | null | null | null | null | null | null | null | null | null | kwang123/roberta-base-nli | [
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-naan-1e-3 | kanishka | 2023-11-30T01:28:45Z | 0 | 0 | null | [
"transformers",
"safetensors",
"opt",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:28:45Z | 2023-11-29T23:45:33.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-naan-1e-3 | [
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AhmedEladl/t5-base-finetuned-swag | AhmedEladl | 2023-11-29T23:50:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:50:13Z | 2023-11-29T23:50:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AhmedEladl/t5-base-finetuned-swag | [
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patrejoss/bertin-gpt-j-6B-es-finetuned-sarcasm-spanish-1000 | patrejoss | 2023-11-29T23:52:56Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:bertin-project/bertin-gpt-j-6B",
"region:us"
] | 2023-11-29T23:52:56Z | 2023-11-29T23:52:15.000Z | null | null | ---
library_name: peft
base_model: bertin-project/bertin-gpt-j-6B
---
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### Framework versions
- PEFT 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | patrejoss/bertin-gpt-j-6B-es-finetuned-sarcasm-spanish-1000 | [
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onizukal/SMIDS_10x_beit_base_sgd_lr00001_fold2 | onizukal | 2023-11-30T01:25:06Z | 0 | 0 | null | [
"transformers",
"safetensors",
"beit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:25:06Z | 2023-11-29T23:52:50.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | onizukal/SMIDS_10x_beit_base_sgd_lr00001_fold2 | [
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LoneStriker/deepseek-llm-67b-chat-2.65bpw-h6-exl2 | LoneStriker | 2023-11-30T00:11:17Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:11:17Z | 2023-11-29T23:53:44.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/deepseek-llm-67b-chat-2.65bpw-h6-exl2 | [
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jerryfr/finetuning-sentiment-model-distillBert | jerryfr | 2023-11-30T00:26:24Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:26:24Z | 2023-11-29T23:55:27.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: finetuning-sentiment-model-distillBert
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. -->
# finetuning-sentiment-model-distillBert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1693
- Accuracy: 0.9381
- F1: 0.9389
- Precision: 0.9293
- Recall: 0.9488
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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jerawincel/xlnet-base-cased-finetuned | jerawincel | 2023-11-30T01:21:51Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"xlnet",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:21:51Z | 2023-11-29T23:56:34.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | jerawincel/xlnet-base-cased-finetuned | [
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harshaan3497/mistral_b_finance_finetuned_test_harshaan_investo | harshaan3497 | 2023-11-29T23:56:59Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:sanchit-gandhi/Mistral-7B-Instruct-v0.1",
"region:us"
] | 2023-11-29T23:56:59Z | 2023-11-29T23:56:49.000Z | null | null | ---
library_name: peft
base_model: sanchit-gandhi/Mistral-7B-Instruct-v0.1
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training Details
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | harshaan3497/mistral_b_finance_finetuned_test_harshaan_investo | [
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kazandaev/WizardCoder-Python-34B-function-calling-sql | kazandaev | 2023-11-30T00:14:08Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:14:08Z | 2023-11-29T23:57:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | kazandaev/WizardCoder-Python-34B-function-calling-sql | [
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sainfo921/twotestt | sainfo921 | 2023-11-30T00:10:37Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:10:37Z | 2023-11-29T23:57:28.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sainfo921/twotestt | [
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xixiyyds/test | xixiyyds | 2023-11-29T23:58:12Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T23:58:12Z | 2023-11-29T23:58:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | xixiyyds/test | [
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belyakoff/LaBSE-fine-tuned-mcx | belyakoff | 2023-11-30T00:01:19Z | 0 | 0 | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:01:19Z | 2023-11-30T00:00:24.000Z | null | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 81 with parameters:
```
{'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 150,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Dense({'in_features': 768, 'out_features': 768, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
(3): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | null | sentence-transformers | sentence-similarity | null | null | null | null | null | null | null | null | null | belyakoff/LaBSE-fine-tuned-mcx | [
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WandneyLC/XXX | WandneyLC | 2023-11-30T00:01:03Z | 0 | 0 | null | [
"license:llama2",
"region:us"
] | 2023-11-30T00:01:03Z | 2023-11-30T00:01:03.000Z | null | null | ---
license: llama2
---
| null | null | null | null | null | null | null | null | null | null | null | null | WandneyLC/XXX | [
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raulgdp/roberta-base-bne-capitel-ner | raulgdp | 2023-11-30T00:18:39Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2002",
"base_model:BSC-LT/roberta-base-bne-capitel-ner",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:18:39Z | 2023-11-30T00:02:38.000Z | null | null | ---
license: apache-2.0
base_model: BSC-LT/roberta-base-bne-capitel-ner
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-base-bne-capitel-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2002
type: conll2002
config: es
split: validation
args: es
metrics:
- name: Precision
type: precision
value: 0.8712310133756518
- name: Recall
type: recall
value: 0.8830422794117647
- name: F1
type: f1
value: 0.8770968846285518
- name: Accuracy
type: accuracy
value: 0.978961189654646
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-capitel-ner
This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1255
- Precision: 0.8712
- Recall: 0.8830
- F1: 0.8771
- Accuracy: 0.9790
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0135 | 1.0 | 1041 | 0.1233 | 0.8615 | 0.8803 | 0.8708 | 0.9783 |
| 0.0111 | 2.0 | 2082 | 0.1099 | 0.8709 | 0.8853 | 0.8781 | 0.9799 |
| 0.0061 | 3.0 | 3123 | 0.1203 | 0.8569 | 0.8739 | 0.8653 | 0.9781 |
| 0.0035 | 4.0 | 4164 | 0.1255 | 0.8712 | 0.8830 | 0.8771 | 0.9790 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.14.1
| null | transformers | token-classification | null | null | null | null | null | null | null | null | null | raulgdp/roberta-base-bne-capitel-ner | [
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kerianheYi/CS245-fine-tunedSD1000_708 | kerianheYi | 2023-11-30T00:29:55Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dataset:jytjyt05/t_to_m3",
"base_model:stabilityai/stable-diffusion-2-base",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-30T00:29:55Z | 2023-11-30T00:02:53.000Z | null | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-base
datasets:
- jytjyt05/t_to_m3
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - kerianheYi/CS245-fine-tunedSD1000_708
This pipeline was finetuned from **stabilityai/stable-diffusion-2-base** on the **jytjyt05/t_to_m3** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['C-Major, Piano, Solo, Ultra High Resolution']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("kerianheYi/CS245-fine-tunedSD1000_708", torch_dtype=torch.float16)
prompt = "C-Major, Piano, Solo, Ultra High Resolution"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 6
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | kerianheYi/CS245-fine-tunedSD1000_708 | [
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TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ | TheBloke | 2023-11-30T00:51:42Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"base_model:harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-30T00:51:42Z | 2023-11-30T00:06:58.000Z | null | null | ---
base_model: harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k
datasets:
- WizardLM/WizardLM_evol_instruct_V2_196k
inference: false
language:
- en
library_name: transformers
license: apache-2.0
model_creator: L
model_name: Open Llama 3B V2 Wizard Evol Instuct V2 196K
model_type: llama
prompt_template: '### HUMAN:
{prompt}
### RESPONSE:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Open Llama 3B V2 Wizard Evol Instuct V2 196K - GPTQ
- Model creator: [L](https://huggingface.co/harborwater)
- Original model: [Open Llama 3B V2 Wizard Evol Instuct V2 196K](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k)
<!-- description start -->
# Description
This repo contains GPTQ model files for [L's Open Llama 3B V2 Wizard Evol Instuct V2 196K](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GGUF)
* [L's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/harborwater/open-llama-3b-v2-wizard-evol-instuct-v2-196k)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Human-Response
```
### HUMAN:
{prompt}
### RESPONSE:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 2.09 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 2.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.64 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.71 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 3.94 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 2048 | 2.15 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ`:
```shell
mkdir open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ
huggingface-cli download TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ --local-dir open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ
huggingface-cli download TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ --local-dir open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''### HUMAN:
{prompt}
### RESPONSE:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''### HUMAN:
{prompt}
### RESPONSE:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: L's Open Llama 3B V2 Wizard Evol Instuct V2 196K
Trained on 1 epoch of the WizardLM_evol_instruct_v2_196k dataset
Link to [GGUF](https://huggingface.co/maddes8cht/harborwater-open-llama-3b-v2-wizard-evol-instuct-v2-196k-gguf) formats.
Prompt template:
```
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
```
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__open-llama-3b-v2-wizard-evol-instuct-v2-196k)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 36.33 |
| ARC (25-shot) | 41.81 |
| HellaSwag (10-shot) | 73.01 |
| MMLU (5-shot) | 26.36 |
| TruthfulQA (0-shot) | 38.99 |
| Winogrande (5-shot) | 66.69 |
| GSM8K (5-shot) | 1.9 |
| DROP (3-shot) | 5.57 |
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/open-llama-3b-v2-wizard-evol-instuct-v2-196k-GPTQ | [
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sam1120/safety-utcustom-terrain-final | sam1120 | 2023-11-30T01:22:04Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:22:04Z | 2023-11-30T00:07:07.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | sam1120/safety-utcustom-terrain-final | [
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hope04302/hate-speech-detection | hope04302 | 2023-11-30T00:29:49Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"license:unknown",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:29:49Z | 2023-11-30T00:07:09.000Z | null | null | ---
license: unknown
---
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | hope04302/hate-speech-detection | [
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cuzaop/ghhdtc | cuzaop | 2023-11-30T00:09:44Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:09:44Z | 2023-11-30T00:07:30.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | cuzaop/ghhdtc | [
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nikolaybi73/RawResnet | nikolaybi73 | 2023-11-30T00:23:26Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-30T00:23:26Z | 2023-11-30T00:10:04.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | nikolaybi73/RawResnet | [
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chris200931/opt1.3_finetuned | chris200931 | 2023-11-30T00:10:58Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-30T00:10:58Z | 2023-11-30T00:10:58.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | chris200931/opt1.3_finetuned | [
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staticai/ZroLLama | staticai | 2023-11-30T00:57:58Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:57:58Z | 2023-11-30T00:11:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | staticai/ZroLLama | [
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sam1120/safety-utcustom-nn-segformer-b0 | sam1120 | 2023-11-30T01:25:59Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:25:59Z | 2023-11-30T00:11:40.000Z | null | null | ---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: safety-utcustom-nn-segformer-b0
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. -->
# safety-utcustom-nn-segformer-b0
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0456
- Mean Iou: 0.8485
- Mean Accuracy: 0.9086
- Overall Accuracy: 0.9900
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.8221
- Accuracy Unsafe: 0.9951
- Iou Unlabeled: nan
- Iou Safe: 0.7073
- Iou Unsafe: 0.9898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.0559 | 1.82 | 20 | 1.0691 | 0.1934 | 0.5133 | 0.4932 | nan | 0.5347 | 0.4919 | 0.0 | 0.0929 | 0.4874 |
| 1.0417 | 3.64 | 40 | 1.0439 | 0.2164 | 0.6062 | 0.5353 | nan | 0.6816 | 0.5309 | 0.0 | 0.1208 | 0.5283 |
| 1.0109 | 5.45 | 60 | 1.0079 | 0.2693 | 0.7359 | 0.6599 | nan | 0.8167 | 0.6552 | 0.0 | 0.1542 | 0.6536 |
| 0.9724 | 7.27 | 80 | 0.9554 | 0.3126 | 0.8237 | 0.7701 | nan | 0.8806 | 0.7667 | 0.0 | 0.1725 | 0.7653 |
| 0.9121 | 9.09 | 100 | 0.8591 | 0.3531 | 0.8632 | 0.8500 | nan | 0.8773 | 0.8492 | 0.0 | 0.2122 | 0.8470 |
| 0.8462 | 10.91 | 120 | 0.7528 | 0.4072 | 0.8881 | 0.9223 | nan | 0.8518 | 0.9244 | 0.0 | 0.3009 | 0.9206 |
| 0.6805 | 12.73 | 140 | 0.6370 | 0.4496 | 0.8735 | 0.9581 | nan | 0.7836 | 0.9634 | 0.0 | 0.3915 | 0.9575 |
| 0.6533 | 14.55 | 160 | 0.5403 | 0.4557 | 0.8104 | 0.9698 | nan | 0.6410 | 0.9797 | 0.0 | 0.3976 | 0.9694 |
| 0.5564 | 16.36 | 180 | 0.5166 | 0.4560 | 0.8152 | 0.9701 | nan | 0.6506 | 0.9798 | 0.0 | 0.3984 | 0.9696 |
| 0.5994 | 18.18 | 200 | 0.4487 | 0.4647 | 0.7708 | 0.9773 | nan | 0.5515 | 0.9902 | 0.0 | 0.4171 | 0.9770 |
| 0.6758 | 20.0 | 220 | 0.4325 | 0.4677 | 0.7902 | 0.9767 | nan | 0.5921 | 0.9883 | 0.0 | 0.4269 | 0.9763 |
| 0.5728 | 21.82 | 240 | 0.4328 | 0.4658 | 0.8001 | 0.9754 | nan | 0.6140 | 0.9863 | 0.0 | 0.4223 | 0.9750 |
| 0.4287 | 23.64 | 260 | 0.3758 | 0.7160 | 0.7669 | 0.9808 | nan | 0.5397 | 0.9941 | nan | 0.4515 | 0.9805 |
| 0.3864 | 25.45 | 280 | 0.3679 | 0.7085 | 0.7783 | 0.9788 | nan | 0.5654 | 0.9913 | nan | 0.4385 | 0.9784 |
| 0.4265 | 27.27 | 300 | 0.3636 | 0.7161 | 0.8022 | 0.9784 | nan | 0.6151 | 0.9893 | nan | 0.4543 | 0.9780 |
| 0.3761 | 29.09 | 320 | 0.3118 | 0.7000 | 0.7272 | 0.9814 | nan | 0.4571 | 0.9972 | nan | 0.4189 | 0.9812 |
| 0.3085 | 30.91 | 340 | 0.3163 | 0.7117 | 0.7884 | 0.9786 | nan | 0.5863 | 0.9904 | nan | 0.4452 | 0.9782 |
| 0.3151 | 32.73 | 360 | 0.2911 | 0.7266 | 0.7896 | 0.9808 | nan | 0.5865 | 0.9927 | nan | 0.4726 | 0.9805 |
| 0.2536 | 34.55 | 380 | 0.2502 | 0.7390 | 0.7945 | 0.9823 | nan | 0.5951 | 0.9940 | nan | 0.4961 | 0.9820 |
| 0.2852 | 36.36 | 400 | 0.2530 | 0.7287 | 0.7773 | 0.9819 | nan | 0.5600 | 0.9947 | nan | 0.4757 | 0.9816 |
| 0.2426 | 38.18 | 420 | 0.2187 | 0.7554 | 0.8104 | 0.9836 | nan | 0.6265 | 0.9943 | nan | 0.5275 | 0.9833 |
| 0.2466 | 40.0 | 440 | 0.2046 | 0.7521 | 0.8253 | 0.9824 | nan | 0.6585 | 0.9921 | nan | 0.5221 | 0.9820 |
| 0.1746 | 41.82 | 460 | 0.1946 | 0.7659 | 0.8364 | 0.9836 | nan | 0.6800 | 0.9928 | nan | 0.5486 | 0.9833 |
| 0.1426 | 43.64 | 480 | 0.1569 | 0.7855 | 0.8347 | 0.9860 | nan | 0.6740 | 0.9954 | nan | 0.5853 | 0.9857 |
| 0.1364 | 45.45 | 500 | 0.1521 | 0.7851 | 0.8443 | 0.9856 | nan | 0.6943 | 0.9944 | nan | 0.5849 | 0.9853 |
| 0.1361 | 47.27 | 520 | 0.1224 | 0.8047 | 0.8624 | 0.9870 | nan | 0.7301 | 0.9948 | nan | 0.6226 | 0.9868 |
| 0.1098 | 49.09 | 540 | 0.1064 | 0.8093 | 0.8461 | 0.9881 | nan | 0.6953 | 0.9969 | nan | 0.6308 | 0.9878 |
| 0.0985 | 50.91 | 560 | 0.1047 | 0.8038 | 0.8586 | 0.9871 | nan | 0.7220 | 0.9951 | nan | 0.6207 | 0.9868 |
| 0.0838 | 52.73 | 580 | 0.1020 | 0.8033 | 0.8664 | 0.9868 | nan | 0.7386 | 0.9942 | nan | 0.6201 | 0.9865 |
| 0.0758 | 54.55 | 600 | 0.0852 | 0.8048 | 0.8286 | 0.9883 | nan | 0.6589 | 0.9982 | nan | 0.6216 | 0.9880 |
| 0.0671 | 56.36 | 620 | 0.0938 | 0.8171 | 0.8932 | 0.9873 | nan | 0.7933 | 0.9932 | nan | 0.6472 | 0.9870 |
| 0.0622 | 58.18 | 640 | 0.0782 | 0.8244 | 0.8788 | 0.9885 | nan | 0.7623 | 0.9954 | nan | 0.6605 | 0.9883 |
| 0.0576 | 60.0 | 660 | 0.0665 | 0.8202 | 0.8497 | 0.9890 | nan | 0.7016 | 0.9977 | nan | 0.6517 | 0.9888 |
| 0.055 | 61.82 | 680 | 0.0757 | 0.8182 | 0.8886 | 0.9876 | nan | 0.7835 | 0.9938 | nan | 0.6490 | 0.9873 |
| 0.0491 | 63.64 | 700 | 0.0698 | 0.8233 | 0.8855 | 0.9882 | nan | 0.7764 | 0.9946 | nan | 0.6586 | 0.9879 |
| 0.0457 | 65.45 | 720 | 0.0656 | 0.8153 | 0.8665 | 0.9880 | nan | 0.7374 | 0.9956 | nan | 0.6429 | 0.9877 |
| 0.0604 | 67.27 | 740 | 0.0581 | 0.8218 | 0.8608 | 0.9888 | nan | 0.7247 | 0.9968 | nan | 0.6550 | 0.9886 |
| 0.0489 | 69.09 | 760 | 0.0569 | 0.8206 | 0.8485 | 0.9891 | nan | 0.6991 | 0.9978 | nan | 0.6524 | 0.9889 |
| 0.0384 | 70.91 | 780 | 0.0568 | 0.8223 | 0.8626 | 0.9888 | nan | 0.7286 | 0.9967 | nan | 0.6561 | 0.9886 |
| 0.041 | 72.73 | 800 | 0.0614 | 0.8219 | 0.8734 | 0.9884 | nan | 0.7511 | 0.9956 | nan | 0.6556 | 0.9882 |
| 0.041 | 74.55 | 820 | 0.0527 | 0.8426 | 0.8867 | 0.9900 | nan | 0.7769 | 0.9965 | nan | 0.6954 | 0.9898 |
| 0.0351 | 76.36 | 840 | 0.0556 | 0.8321 | 0.8882 | 0.9890 | nan | 0.7811 | 0.9953 | nan | 0.6754 | 0.9888 |
| 0.0331 | 78.18 | 860 | 0.0508 | 0.8245 | 0.8538 | 0.9893 | nan | 0.7099 | 0.9977 | nan | 0.6600 | 0.9891 |
| 0.0466 | 80.0 | 880 | 0.0516 | 0.8293 | 0.8719 | 0.9892 | nan | 0.7472 | 0.9965 | nan | 0.6696 | 0.9890 |
| 0.0378 | 81.82 | 900 | 0.0507 | 0.8342 | 0.8835 | 0.9893 | nan | 0.7710 | 0.9959 | nan | 0.6793 | 0.9891 |
| 0.032 | 83.64 | 920 | 0.0519 | 0.8274 | 0.8719 | 0.9890 | nan | 0.7474 | 0.9963 | nan | 0.6661 | 0.9888 |
| 0.0279 | 85.45 | 940 | 0.0519 | 0.8321 | 0.8851 | 0.9891 | nan | 0.7747 | 0.9956 | nan | 0.6754 | 0.9888 |
| 0.0264 | 87.27 | 960 | 0.0486 | 0.8368 | 0.8840 | 0.9896 | nan | 0.7719 | 0.9961 | nan | 0.6843 | 0.9893 |
| 0.0242 | 89.09 | 980 | 0.0472 | 0.8395 | 0.8884 | 0.9897 | nan | 0.7809 | 0.9960 | nan | 0.6896 | 0.9895 |
| 0.028 | 90.91 | 1000 | 0.0497 | 0.8382 | 0.9042 | 0.9892 | nan | 0.8138 | 0.9945 | nan | 0.6875 | 0.9889 |
| 0.0249 | 92.73 | 1020 | 0.0445 | 0.8434 | 0.8922 | 0.9900 | nan | 0.7883 | 0.9961 | nan | 0.6971 | 0.9897 |
| 0.0234 | 94.55 | 1040 | 0.0482 | 0.8371 | 0.8894 | 0.9895 | nan | 0.7831 | 0.9957 | nan | 0.6850 | 0.9892 |
| 0.0242 | 96.36 | 1060 | 0.0464 | 0.8306 | 0.8713 | 0.9893 | nan | 0.7459 | 0.9967 | nan | 0.6721 | 0.9891 |
| 0.0213 | 98.18 | 1080 | 0.0441 | 0.8409 | 0.8901 | 0.9898 | nan | 0.7842 | 0.9960 | nan | 0.6922 | 0.9896 |
| 0.0239 | 100.0 | 1100 | 0.0422 | 0.8431 | 0.8817 | 0.9902 | nan | 0.7665 | 0.9970 | nan | 0.6962 | 0.9900 |
| 0.0225 | 101.82 | 1120 | 0.0484 | 0.8394 | 0.8950 | 0.9895 | nan | 0.7946 | 0.9954 | nan | 0.6895 | 0.9893 |
| 0.0204 | 103.64 | 1140 | 0.0474 | 0.8401 | 0.8999 | 0.9895 | nan | 0.8047 | 0.9950 | nan | 0.6911 | 0.9892 |
| 0.022 | 105.45 | 1160 | 0.0487 | 0.8383 | 0.8971 | 0.9894 | nan | 0.7992 | 0.9951 | nan | 0.6875 | 0.9891 |
| 0.0213 | 107.27 | 1180 | 0.0432 | 0.8404 | 0.8766 | 0.9901 | nan | 0.7560 | 0.9972 | nan | 0.6910 | 0.9899 |
| 0.0207 | 109.09 | 1200 | 0.0494 | 0.8356 | 0.8832 | 0.9895 | nan | 0.7703 | 0.9961 | nan | 0.6820 | 0.9892 |
| 0.0188 | 110.91 | 1220 | 0.0452 | 0.8446 | 0.9006 | 0.9899 | nan | 0.8057 | 0.9954 | nan | 0.6996 | 0.9896 |
| 0.0181 | 112.73 | 1240 | 0.0440 | 0.8419 | 0.8909 | 0.9899 | nan | 0.7857 | 0.9960 | nan | 0.6941 | 0.9896 |
| 0.0184 | 114.55 | 1260 | 0.0443 | 0.8405 | 0.8905 | 0.9897 | nan | 0.7850 | 0.9959 | nan | 0.6914 | 0.9895 |
| 0.0162 | 116.36 | 1280 | 0.0453 | 0.8420 | 0.8979 | 0.9897 | nan | 0.8003 | 0.9954 | nan | 0.6945 | 0.9894 |
| 0.019 | 118.18 | 1300 | 0.0436 | 0.8408 | 0.8928 | 0.9897 | nan | 0.7900 | 0.9957 | nan | 0.6922 | 0.9895 |
| 0.0165 | 120.0 | 1320 | 0.0403 | 0.8534 | 0.9095 | 0.9905 | nan | 0.8234 | 0.9955 | nan | 0.7165 | 0.9902 |
| 0.0162 | 121.82 | 1340 | 0.0389 | 0.8529 | 0.9004 | 0.9906 | nan | 0.8045 | 0.9962 | nan | 0.7153 | 0.9904 |
| 0.0162 | 123.64 | 1360 | 0.0427 | 0.8427 | 0.8920 | 0.9899 | nan | 0.7879 | 0.9960 | nan | 0.6958 | 0.9897 |
| 0.0148 | 125.45 | 1380 | 0.0423 | 0.8442 | 0.8933 | 0.9900 | nan | 0.7906 | 0.9960 | nan | 0.6987 | 0.9898 |
| 0.0148 | 127.27 | 1400 | 0.0388 | 0.8511 | 0.9081 | 0.9903 | nan | 0.8209 | 0.9954 | nan | 0.7121 | 0.9900 |
| 0.0155 | 129.09 | 1420 | 0.0448 | 0.8418 | 0.8953 | 0.9897 | nan | 0.7949 | 0.9956 | nan | 0.6941 | 0.9895 |
| 0.0149 | 130.91 | 1440 | 0.0378 | 0.8569 | 0.9047 | 0.9909 | nan | 0.8132 | 0.9962 | nan | 0.7231 | 0.9907 |
| 0.0182 | 132.73 | 1460 | 0.0396 | 0.8487 | 0.8992 | 0.9903 | nan | 0.8024 | 0.9959 | nan | 0.7074 | 0.9900 |
| 0.0146 | 134.55 | 1480 | 0.0409 | 0.8479 | 0.8993 | 0.9902 | nan | 0.8027 | 0.9959 | nan | 0.7059 | 0.9900 |
| 0.0157 | 136.36 | 1500 | 0.0393 | 0.8496 | 0.8974 | 0.9904 | nan | 0.7985 | 0.9962 | nan | 0.7091 | 0.9902 |
| 0.0152 | 138.18 | 1520 | 0.0424 | 0.8482 | 0.9022 | 0.9902 | nan | 0.8088 | 0.9956 | nan | 0.7064 | 0.9899 |
| 0.0142 | 140.0 | 1540 | 0.0407 | 0.8499 | 0.9034 | 0.9903 | nan | 0.8110 | 0.9957 | nan | 0.7098 | 0.9901 |
| 0.0141 | 141.82 | 1560 | 0.0381 | 0.8522 | 0.9028 | 0.9905 | nan | 0.8096 | 0.9960 | nan | 0.7141 | 0.9903 |
| 0.0144 | 143.64 | 1580 | 0.0385 | 0.8548 | 0.8999 | 0.9908 | nan | 0.8033 | 0.9965 | nan | 0.7189 | 0.9906 |
| 0.0158 | 145.45 | 1600 | 0.0426 | 0.8466 | 0.9009 | 0.9900 | nan | 0.8062 | 0.9956 | nan | 0.7035 | 0.9898 |
| 0.0124 | 147.27 | 1620 | 0.0418 | 0.8472 | 0.8954 | 0.9902 | nan | 0.7947 | 0.9961 | nan | 0.7044 | 0.9900 |
| 0.0157 | 149.09 | 1640 | 0.0387 | 0.8519 | 0.9042 | 0.9904 | nan | 0.8126 | 0.9958 | nan | 0.7136 | 0.9902 |
| 0.0119 | 150.91 | 1660 | 0.0435 | 0.8479 | 0.9040 | 0.9901 | nan | 0.8125 | 0.9954 | nan | 0.7060 | 0.9899 |
| 0.0118 | 152.73 | 1680 | 0.0424 | 0.8483 | 0.9040 | 0.9901 | nan | 0.8126 | 0.9955 | nan | 0.7067 | 0.9899 |
| 0.0132 | 154.55 | 1700 | 0.0384 | 0.8559 | 0.8962 | 0.9910 | nan | 0.7955 | 0.9969 | nan | 0.7211 | 0.9908 |
| 0.0127 | 156.36 | 1720 | 0.0426 | 0.8475 | 0.9028 | 0.9901 | nan | 0.8101 | 0.9955 | nan | 0.7052 | 0.9898 |
| 0.0118 | 158.18 | 1740 | 0.0386 | 0.8512 | 0.8992 | 0.9905 | nan | 0.8023 | 0.9962 | nan | 0.7120 | 0.9903 |
| 0.0231 | 160.0 | 1760 | 0.0396 | 0.8509 | 0.9001 | 0.9905 | nan | 0.8041 | 0.9961 | nan | 0.7116 | 0.9902 |
| 0.0124 | 161.82 | 1780 | 0.0375 | 0.8520 | 0.8961 | 0.9906 | nan | 0.7956 | 0.9965 | nan | 0.7136 | 0.9904 |
| 0.0134 | 163.64 | 1800 | 0.0435 | 0.8496 | 0.9033 | 0.9903 | nan | 0.8109 | 0.9957 | nan | 0.7091 | 0.9900 |
| 0.0137 | 165.45 | 1820 | 0.0431 | 0.8490 | 0.9002 | 0.9903 | nan | 0.8044 | 0.9959 | nan | 0.7080 | 0.9901 |
| 0.0129 | 167.27 | 1840 | 0.0429 | 0.8445 | 0.8866 | 0.9902 | nan | 0.7766 | 0.9966 | nan | 0.6989 | 0.9900 |
| 0.0105 | 169.09 | 1860 | 0.0431 | 0.8478 | 0.8967 | 0.9903 | nan | 0.7974 | 0.9961 | nan | 0.7055 | 0.9900 |
| 0.0121 | 170.91 | 1880 | 0.0428 | 0.8518 | 0.9040 | 0.9904 | nan | 0.8122 | 0.9958 | nan | 0.7134 | 0.9902 |
| 0.0099 | 172.73 | 1900 | 0.0413 | 0.8466 | 0.8914 | 0.9903 | nan | 0.7863 | 0.9964 | nan | 0.7032 | 0.9901 |
| 0.0266 | 174.55 | 1920 | 0.0372 | 0.8592 | 0.9108 | 0.9910 | nan | 0.8257 | 0.9959 | nan | 0.7277 | 0.9907 |
| 0.0105 | 176.36 | 1940 | 0.0396 | 0.8533 | 0.9050 | 0.9906 | nan | 0.8142 | 0.9959 | nan | 0.7163 | 0.9903 |
| 0.01 | 178.18 | 1960 | 0.0385 | 0.8549 | 0.9025 | 0.9908 | nan | 0.8087 | 0.9963 | nan | 0.7193 | 0.9905 |
| 0.0101 | 180.0 | 1980 | 0.0408 | 0.8511 | 0.9005 | 0.9905 | nan | 0.8049 | 0.9961 | nan | 0.7119 | 0.9902 |
| 0.0105 | 181.82 | 2000 | 0.0411 | 0.8501 | 0.8992 | 0.9904 | nan | 0.8023 | 0.9961 | nan | 0.7101 | 0.9902 |
| 0.0107 | 183.64 | 2020 | 0.0403 | 0.8526 | 0.9037 | 0.9905 | nan | 0.8115 | 0.9959 | nan | 0.7150 | 0.9903 |
| 0.0127 | 185.45 | 2040 | 0.0410 | 0.8518 | 0.9076 | 0.9904 | nan | 0.8196 | 0.9955 | nan | 0.7135 | 0.9901 |
| 0.0122 | 187.27 | 2060 | 0.0372 | 0.8608 | 0.8989 | 0.9913 | nan | 0.8008 | 0.9971 | nan | 0.7305 | 0.9911 |
| 0.0094 | 189.09 | 2080 | 0.0391 | 0.8530 | 0.8891 | 0.9909 | nan | 0.7810 | 0.9972 | nan | 0.7154 | 0.9907 |
| 0.0102 | 190.91 | 2100 | 0.0386 | 0.8560 | 0.9211 | 0.9904 | nan | 0.8476 | 0.9947 | nan | 0.7218 | 0.9902 |
| 0.0122 | 192.73 | 2120 | 0.0360 | 0.8587 | 0.9035 | 0.9911 | nan | 0.8105 | 0.9965 | nan | 0.7266 | 0.9909 |
| 0.009 | 194.55 | 2140 | 0.0410 | 0.8513 | 0.9036 | 0.9904 | nan | 0.8114 | 0.9958 | nan | 0.7124 | 0.9902 |
| 0.0106 | 196.36 | 2160 | 0.0409 | 0.8518 | 0.9040 | 0.9904 | nan | 0.8123 | 0.9958 | nan | 0.7134 | 0.9902 |
| 0.0095 | 198.18 | 2180 | 0.0412 | 0.8500 | 0.9007 | 0.9904 | nan | 0.8054 | 0.9959 | nan | 0.7099 | 0.9901 |
| 0.0098 | 200.0 | 2200 | 0.0392 | 0.8536 | 0.9055 | 0.9906 | nan | 0.8152 | 0.9959 | nan | 0.7168 | 0.9903 |
| 0.0095 | 201.82 | 2220 | 0.0390 | 0.8537 | 0.9047 | 0.9906 | nan | 0.8135 | 0.9959 | nan | 0.7170 | 0.9904 |
| 0.0091 | 203.64 | 2240 | 0.0411 | 0.8528 | 0.9050 | 0.9905 | nan | 0.8143 | 0.9958 | nan | 0.7154 | 0.9903 |
| 0.0093 | 205.45 | 2260 | 0.0387 | 0.8541 | 0.9018 | 0.9907 | nan | 0.8075 | 0.9962 | nan | 0.7177 | 0.9905 |
| 0.0127 | 207.27 | 2280 | 0.0394 | 0.8540 | 0.9067 | 0.9906 | nan | 0.8175 | 0.9958 | nan | 0.7177 | 0.9904 |
| 0.0088 | 209.09 | 2300 | 0.0421 | 0.8513 | 0.9017 | 0.9905 | nan | 0.8074 | 0.9960 | nan | 0.7124 | 0.9902 |
| 0.0098 | 210.91 | 2320 | 0.0409 | 0.8527 | 0.9024 | 0.9906 | nan | 0.8087 | 0.9961 | nan | 0.7151 | 0.9903 |
| 0.0088 | 212.73 | 2340 | 0.0406 | 0.8526 | 0.9038 | 0.9905 | nan | 0.8116 | 0.9959 | nan | 0.7150 | 0.9903 |
| 0.0085 | 214.55 | 2360 | 0.0402 | 0.8526 | 0.9025 | 0.9906 | nan | 0.8089 | 0.9960 | nan | 0.7149 | 0.9903 |
| 0.0084 | 216.36 | 2380 | 0.0407 | 0.8523 | 0.9014 | 0.9906 | nan | 0.8067 | 0.9961 | nan | 0.7144 | 0.9903 |
| 0.0086 | 218.18 | 2400 | 0.0401 | 0.8531 | 0.9033 | 0.9906 | nan | 0.8106 | 0.9960 | nan | 0.7158 | 0.9903 |
| 0.0076 | 220.0 | 2420 | 0.0431 | 0.8518 | 0.9027 | 0.9905 | nan | 0.8094 | 0.9959 | nan | 0.7134 | 0.9903 |
| 0.0101 | 221.82 | 2440 | 0.0424 | 0.8518 | 0.8997 | 0.9905 | nan | 0.8033 | 0.9962 | nan | 0.7133 | 0.9903 |
| 0.0085 | 223.64 | 2460 | 0.0434 | 0.8524 | 0.9045 | 0.9905 | nan | 0.8132 | 0.9958 | nan | 0.7145 | 0.9903 |
| 0.0078 | 225.45 | 2480 | 0.0424 | 0.8526 | 0.9010 | 0.9906 | nan | 0.8058 | 0.9962 | nan | 0.7148 | 0.9904 |
| 0.0094 | 227.27 | 2500 | 0.0442 | 0.8499 | 0.8957 | 0.9905 | nan | 0.7951 | 0.9964 | nan | 0.7096 | 0.9902 |
| 0.0104 | 229.09 | 2520 | 0.0428 | 0.8525 | 0.9075 | 0.9904 | nan | 0.8194 | 0.9956 | nan | 0.7149 | 0.9902 |
| 0.0096 | 230.91 | 2540 | 0.0433 | 0.8532 | 0.9062 | 0.9905 | nan | 0.8166 | 0.9958 | nan | 0.7162 | 0.9903 |
| 0.0091 | 232.73 | 2560 | 0.0424 | 0.8520 | 0.9053 | 0.9904 | nan | 0.8148 | 0.9957 | nan | 0.7138 | 0.9902 |
| 0.0093 | 234.55 | 2580 | 0.0429 | 0.8528 | 0.9039 | 0.9905 | nan | 0.8119 | 0.9959 | nan | 0.7153 | 0.9903 |
| 0.009 | 236.36 | 2600 | 0.0452 | 0.8514 | 0.9006 | 0.9905 | nan | 0.8050 | 0.9961 | nan | 0.7125 | 0.9903 |
| 0.0076 | 238.18 | 2620 | 0.0431 | 0.8551 | 0.9108 | 0.9906 | nan | 0.8259 | 0.9956 | nan | 0.7199 | 0.9904 |
| 0.0074 | 240.0 | 2640 | 0.0418 | 0.8548 | 0.8983 | 0.9908 | nan | 0.8001 | 0.9966 | nan | 0.7189 | 0.9906 |
| 0.0074 | 241.82 | 2660 | 0.0428 | 0.8557 | 0.9063 | 0.9907 | nan | 0.8166 | 0.9960 | nan | 0.7209 | 0.9905 |
| 0.0077 | 243.64 | 2680 | 0.0425 | 0.8533 | 0.9029 | 0.9906 | nan | 0.8098 | 0.9961 | nan | 0.7161 | 0.9904 |
| 0.0078 | 245.45 | 2700 | 0.0421 | 0.8539 | 0.9028 | 0.9907 | nan | 0.8095 | 0.9961 | nan | 0.7174 | 0.9904 |
| 0.0075 | 247.27 | 2720 | 0.0441 | 0.8520 | 0.8989 | 0.9906 | nan | 0.8015 | 0.9963 | nan | 0.7137 | 0.9904 |
| 0.0083 | 249.09 | 2740 | 0.0419 | 0.8548 | 0.8992 | 0.9908 | nan | 0.8019 | 0.9965 | nan | 0.7189 | 0.9906 |
| 0.0075 | 250.91 | 2760 | 0.0403 | 0.8551 | 0.9014 | 0.9908 | nan | 0.8063 | 0.9964 | nan | 0.7196 | 0.9906 |
| 0.0079 | 252.73 | 2780 | 0.0430 | 0.8552 | 0.9061 | 0.9907 | nan | 0.8162 | 0.9960 | nan | 0.7199 | 0.9905 |
| 0.0088 | 254.55 | 2800 | 0.0425 | 0.8552 | 0.9049 | 0.9907 | nan | 0.8138 | 0.9961 | nan | 0.7200 | 0.9905 |
| 0.009 | 256.36 | 2820 | 0.0448 | 0.8546 | 0.9032 | 0.9907 | nan | 0.8102 | 0.9962 | nan | 0.7187 | 0.9905 |
| 0.0082 | 258.18 | 2840 | 0.0430 | 0.8550 | 0.9027 | 0.9908 | nan | 0.8092 | 0.9962 | nan | 0.7194 | 0.9905 |
| 0.0097 | 260.0 | 2860 | 0.0432 | 0.8564 | 0.9051 | 0.9908 | nan | 0.8140 | 0.9962 | nan | 0.7221 | 0.9906 |
| 0.0073 | 261.82 | 2880 | 0.0437 | 0.8562 | 0.9041 | 0.9908 | nan | 0.8121 | 0.9962 | nan | 0.7217 | 0.9906 |
| 0.0085 | 263.64 | 2900 | 0.0388 | 0.8617 | 0.9149 | 0.9911 | nan | 0.8339 | 0.9958 | nan | 0.7325 | 0.9909 |
| 0.008 | 265.45 | 2920 | 0.0418 | 0.8589 | 0.9105 | 0.9909 | nan | 0.8250 | 0.9959 | nan | 0.7270 | 0.9907 |
| 0.0065 | 267.27 | 2940 | 0.0432 | 0.8561 | 0.9041 | 0.9908 | nan | 0.8120 | 0.9962 | nan | 0.7215 | 0.9906 |
| 0.0069 | 269.09 | 2960 | 0.0436 | 0.8551 | 0.9125 | 0.9905 | nan | 0.8296 | 0.9954 | nan | 0.7199 | 0.9903 |
| 0.0081 | 270.91 | 2980 | 0.0445 | 0.8501 | 0.8979 | 0.9904 | nan | 0.7996 | 0.9962 | nan | 0.7099 | 0.9902 |
| 0.0092 | 272.73 | 3000 | 0.0438 | 0.8546 | 0.9069 | 0.9906 | nan | 0.8180 | 0.9958 | nan | 0.7188 | 0.9904 |
| 0.0082 | 274.55 | 3020 | 0.0544 | 0.8337 | 0.9029 | 0.9888 | nan | 0.8118 | 0.9941 | nan | 0.6789 | 0.9885 |
| 0.0077 | 276.36 | 3040 | 0.0416 | 0.8579 | 0.8917 | 0.9913 | nan | 0.7860 | 0.9975 | nan | 0.7248 | 0.9911 |
| 0.0074 | 278.18 | 3060 | 0.0408 | 0.8580 | 0.9040 | 0.9910 | nan | 0.8117 | 0.9964 | nan | 0.7253 | 0.9908 |
| 0.0092 | 280.0 | 3080 | 0.0413 | 0.8571 | 0.9069 | 0.9909 | nan | 0.8176 | 0.9961 | nan | 0.7235 | 0.9906 |
| 0.0062 | 281.82 | 3100 | 0.0412 | 0.8575 | 0.8982 | 0.9911 | nan | 0.7996 | 0.9969 | nan | 0.7241 | 0.9909 |
| 0.007 | 283.64 | 3120 | 0.0410 | 0.8628 | 0.9009 | 0.9915 | nan | 0.8047 | 0.9971 | nan | 0.7344 | 0.9913 |
| 0.0062 | 285.45 | 3140 | 0.0393 | 0.8635 | 0.9022 | 0.9915 | nan | 0.8074 | 0.9971 | nan | 0.7357 | 0.9913 |
| 0.0066 | 287.27 | 3160 | 0.0504 | 0.8446 | 0.9049 | 0.9898 | nan | 0.8148 | 0.9950 | nan | 0.6996 | 0.9895 |
| 0.0088 | 289.09 | 3180 | 0.0482 | 0.8476 | 0.9130 | 0.9898 | nan | 0.8314 | 0.9946 | nan | 0.7056 | 0.9896 |
| 0.0062 | 290.91 | 3200 | 0.0454 | 0.8521 | 0.9083 | 0.9904 | nan | 0.8212 | 0.9955 | nan | 0.7141 | 0.9901 |
| 0.0067 | 292.73 | 3220 | 0.0453 | 0.8521 | 0.9075 | 0.9904 | nan | 0.8195 | 0.9955 | nan | 0.7141 | 0.9902 |
| 0.0065 | 294.55 | 3240 | 0.0444 | 0.8530 | 0.9032 | 0.9906 | nan | 0.8103 | 0.9960 | nan | 0.7157 | 0.9903 |
| 0.0068 | 296.36 | 3260 | 0.0459 | 0.8536 | 0.9034 | 0.9906 | nan | 0.8108 | 0.9960 | nan | 0.7167 | 0.9904 |
| 0.0063 | 298.18 | 3280 | 0.0426 | 0.8580 | 0.9066 | 0.9909 | nan | 0.8170 | 0.9962 | nan | 0.7254 | 0.9907 |
| 0.0099 | 300.0 | 3300 | 0.0440 | 0.8560 | 0.9058 | 0.9908 | nan | 0.8156 | 0.9961 | nan | 0.7214 | 0.9906 |
| 0.0071 | 301.82 | 3320 | 0.0456 | 0.8485 | 0.9086 | 0.9900 | nan | 0.8221 | 0.9951 | nan | 0.7073 | 0.9898 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| null | transformers | null | null | null | null | null | null | null | null | null | null | sam1120/safety-utcustom-nn-segformer-b0 | [
-0.574354887008667,
-0.6722396016120911,
0.2786654829978943,
0.08709616959095001,
-0.06399935483932495,
0.03035668469965458,
0.025712139904499054,
0.09234792739152908,
0.8266377449035645,
0.3695451319217682,
-0.5713092684745789,
-0.7880774140357971,
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Xzh0u/Llama-2-7b-chat-finetune | Xzh0u | 2023-11-30T00:11:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:11:41Z | 2023-11-30T00:11:41.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Xzh0u/Llama-2-7b-chat-finetune | [
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sam1120/safety-utcustom-nn-segformer-b5 | sam1120 | 2023-11-30T01:24:00Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:24:00Z | 2023-11-30T00:13:21.000Z | null | null | ---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: safety-utcustom-nn-segformer-b5
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. -->
# safety-utcustom-nn-segformer-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0726
- Mean Iou: 0.8461
- Mean Accuracy: 0.9234
- Overall Accuracy: 0.9895
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.8533
- Accuracy Unsafe: 0.9936
- Iou Unlabeled: nan
- Iou Safe: 0.7031
- Iou Unsafe: 0.9892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.0236 | 1.82 | 20 | 1.0342 | 0.1817 | 0.3196 | 0.4839 | nan | 0.1451 | 0.4941 | 0.0 | 0.0560 | 0.4890 |
| 0.9613 | 3.64 | 40 | 0.9378 | 0.2474 | 0.4436 | 0.6346 | nan | 0.2407 | 0.6465 | 0.0 | 0.1033 | 0.6389 |
| 0.8582 | 5.45 | 60 | 0.7904 | 0.3286 | 0.5538 | 0.8298 | nan | 0.2606 | 0.8470 | 0.0 | 0.1516 | 0.8341 |
| 0.7265 | 7.27 | 80 | 0.6110 | 0.3983 | 0.6737 | 0.9216 | nan | 0.4103 | 0.9370 | 0.0 | 0.2715 | 0.9233 |
| 0.5413 | 9.09 | 100 | 0.4794 | 0.4284 | 0.7611 | 0.9423 | nan | 0.5687 | 0.9536 | 0.0 | 0.3427 | 0.9426 |
| 0.3856 | 10.91 | 120 | 0.3320 | 0.4573 | 0.7721 | 0.9690 | nan | 0.5630 | 0.9812 | 0.0 | 0.4030 | 0.9689 |
| 0.2465 | 12.73 | 140 | 0.2433 | 0.4791 | 0.8000 | 0.9775 | nan | 0.6114 | 0.9885 | 0.0 | 0.4601 | 0.9771 |
| 0.1694 | 14.55 | 160 | 0.1717 | 0.5038 | 0.8278 | 0.9827 | nan | 0.6632 | 0.9923 | 0.0 | 0.5292 | 0.9823 |
| 0.1346 | 16.36 | 180 | 0.1393 | 0.5105 | 0.8435 | 0.9832 | nan | 0.6951 | 0.9919 | 0.0 | 0.5487 | 0.9829 |
| 0.0941 | 18.18 | 200 | 0.1053 | 0.5230 | 0.8797 | 0.9840 | nan | 0.7689 | 0.9905 | 0.0 | 0.5855 | 0.9837 |
| 0.0962 | 20.0 | 220 | 0.0929 | 0.5341 | 0.8912 | 0.9856 | nan | 0.7910 | 0.9915 | 0.0 | 0.6169 | 0.9853 |
| 0.0855 | 21.82 | 240 | 0.0783 | 0.5306 | 0.8649 | 0.9860 | nan | 0.7362 | 0.9935 | 0.0 | 0.6060 | 0.9857 |
| 0.0511 | 23.64 | 260 | 0.0762 | 0.8038 | 0.8653 | 0.9868 | nan | 0.7362 | 0.9944 | nan | 0.6211 | 0.9866 |
| 0.0416 | 25.45 | 280 | 0.0752 | 0.5412 | 0.8786 | 0.9872 | nan | 0.7633 | 0.9940 | 0.0 | 0.6366 | 0.9869 |
| 0.04 | 27.27 | 300 | 0.0569 | 0.5260 | 0.8048 | 0.9876 | nan | 0.6106 | 0.9990 | 0.0 | 0.5906 | 0.9874 |
| 0.0488 | 29.09 | 320 | 0.0751 | 0.8011 | 0.8652 | 0.9866 | nan | 0.7363 | 0.9941 | nan | 0.6160 | 0.9863 |
| 0.0389 | 30.91 | 340 | 0.0929 | 0.8107 | 0.8938 | 0.9866 | nan | 0.7952 | 0.9924 | nan | 0.6351 | 0.9863 |
| 0.0273 | 32.73 | 360 | 0.0583 | 0.8203 | 0.8641 | 0.9886 | nan | 0.7320 | 0.9963 | nan | 0.6523 | 0.9883 |
| 0.0209 | 34.55 | 380 | 0.0530 | 0.8301 | 0.8765 | 0.9891 | nan | 0.7568 | 0.9962 | nan | 0.6712 | 0.9889 |
| 0.0305 | 36.36 | 400 | 0.0698 | 0.5524 | 0.8873 | 0.9887 | nan | 0.7796 | 0.9950 | 0.0 | 0.6687 | 0.9884 |
| 0.0256 | 38.18 | 420 | 0.0631 | 0.8348 | 0.8921 | 0.9892 | nan | 0.7890 | 0.9952 | nan | 0.6807 | 0.9889 |
| 0.0315 | 40.0 | 440 | 0.0627 | 0.8323 | 0.8936 | 0.9889 | nan | 0.7925 | 0.9948 | nan | 0.6760 | 0.9886 |
| 0.018 | 41.82 | 460 | 0.0614 | 0.8347 | 0.8934 | 0.9891 | nan | 0.7917 | 0.9951 | nan | 0.6805 | 0.9889 |
| 0.0193 | 43.64 | 480 | 0.0601 | 0.8329 | 0.8893 | 0.9891 | nan | 0.7833 | 0.9953 | nan | 0.6770 | 0.9888 |
| 0.0221 | 45.45 | 500 | 0.0601 | 0.8335 | 0.8940 | 0.9890 | nan | 0.7930 | 0.9949 | nan | 0.6783 | 0.9887 |
| 0.019 | 47.27 | 520 | 0.0588 | 0.5502 | 0.8787 | 0.9886 | nan | 0.7620 | 0.9954 | 0.0 | 0.6621 | 0.9884 |
| 0.0189 | 49.09 | 540 | 0.0712 | 0.8122 | 0.8992 | 0.9866 | nan | 0.8064 | 0.9920 | nan | 0.6380 | 0.9863 |
| 0.0171 | 50.91 | 560 | 0.0538 | 0.8377 | 0.8790 | 0.9898 | nan | 0.7614 | 0.9967 | nan | 0.6858 | 0.9896 |
| 0.0136 | 52.73 | 580 | 0.0628 | 0.8349 | 0.8883 | 0.9893 | nan | 0.7811 | 0.9956 | nan | 0.6808 | 0.9890 |
| 0.012 | 54.55 | 600 | 0.0619 | 0.8372 | 0.8954 | 0.9893 | nan | 0.7956 | 0.9951 | nan | 0.6853 | 0.9890 |
| 0.0129 | 56.36 | 620 | 0.0591 | 0.8374 | 0.8989 | 0.9892 | nan | 0.8029 | 0.9949 | nan | 0.6859 | 0.9890 |
| 0.012 | 58.18 | 640 | 0.0600 | 0.8371 | 0.8952 | 0.9893 | nan | 0.7953 | 0.9952 | nan | 0.6851 | 0.9890 |
| 0.0117 | 60.0 | 660 | 0.0598 | 0.8346 | 0.8935 | 0.9891 | nan | 0.7920 | 0.9951 | nan | 0.6804 | 0.9888 |
| 0.0115 | 61.82 | 680 | 0.0668 | 0.5584 | 0.9096 | 0.9890 | nan | 0.8253 | 0.9939 | 0.0 | 0.6866 | 0.9887 |
| 0.0115 | 63.64 | 700 | 0.0660 | 0.8363 | 0.9009 | 0.9891 | nan | 0.8072 | 0.9946 | nan | 0.6837 | 0.9888 |
| 0.0097 | 65.45 | 720 | 0.0601 | 0.8404 | 0.9021 | 0.9894 | nan | 0.8094 | 0.9949 | nan | 0.6915 | 0.9892 |
| 0.011 | 67.27 | 740 | 0.0633 | 0.8385 | 0.8978 | 0.9894 | nan | 0.8006 | 0.9951 | nan | 0.6879 | 0.9891 |
| 0.0104 | 69.09 | 760 | 0.0648 | 0.8399 | 0.9056 | 0.9893 | nan | 0.8167 | 0.9945 | nan | 0.6908 | 0.9890 |
| 0.0092 | 70.91 | 780 | 0.0669 | 0.8361 | 0.8931 | 0.9893 | nan | 0.7910 | 0.9952 | nan | 0.6833 | 0.9890 |
| 0.0091 | 72.73 | 800 | 0.0663 | 0.8375 | 0.9040 | 0.9891 | nan | 0.8137 | 0.9944 | nan | 0.6861 | 0.9888 |
| 0.009 | 74.55 | 820 | 0.0636 | 0.8396 | 0.9055 | 0.9893 | nan | 0.8165 | 0.9945 | nan | 0.6902 | 0.9890 |
| 0.008 | 76.36 | 840 | 0.0663 | 0.8376 | 0.8931 | 0.9894 | nan | 0.7908 | 0.9954 | nan | 0.6861 | 0.9892 |
| 0.0085 | 78.18 | 860 | 0.0647 | 0.8379 | 0.9049 | 0.9891 | nan | 0.8154 | 0.9944 | nan | 0.6870 | 0.9889 |
| 0.0125 | 80.0 | 880 | 0.0692 | 0.8382 | 0.9077 | 0.9891 | nan | 0.8212 | 0.9941 | nan | 0.6875 | 0.9888 |
| 0.0073 | 81.82 | 900 | 0.0640 | 0.8397 | 0.8946 | 0.9896 | nan | 0.7938 | 0.9955 | nan | 0.6901 | 0.9893 |
| 0.0074 | 83.64 | 920 | 0.0679 | 0.8421 | 0.9121 | 0.9893 | nan | 0.8301 | 0.9941 | nan | 0.6951 | 0.9891 |
| 0.0071 | 85.45 | 940 | 0.0621 | 0.8407 | 0.8986 | 0.9895 | nan | 0.8021 | 0.9952 | nan | 0.6921 | 0.9893 |
| 0.007 | 87.27 | 960 | 0.0670 | 0.8400 | 0.9010 | 0.9894 | nan | 0.8071 | 0.9949 | nan | 0.6909 | 0.9892 |
| 0.0063 | 89.09 | 980 | 0.0677 | 0.8413 | 0.9078 | 0.9894 | nan | 0.8212 | 0.9944 | nan | 0.6935 | 0.9891 |
| 0.0076 | 90.91 | 1000 | 0.0647 | 0.8428 | 0.9092 | 0.9895 | nan | 0.8239 | 0.9945 | nan | 0.6964 | 0.9892 |
| 0.0073 | 92.73 | 1020 | 0.0730 | 0.8400 | 0.9171 | 0.9890 | nan | 0.8407 | 0.9935 | nan | 0.6912 | 0.9887 |
| 0.0066 | 94.55 | 1040 | 0.0702 | 0.8405 | 0.9103 | 0.9892 | nan | 0.8264 | 0.9941 | nan | 0.6920 | 0.9890 |
| 0.0065 | 96.36 | 1060 | 0.0712 | 0.8409 | 0.9122 | 0.9892 | nan | 0.8304 | 0.9940 | nan | 0.6928 | 0.9889 |
| 0.0059 | 98.18 | 1080 | 0.0714 | 0.8396 | 0.9105 | 0.9891 | nan | 0.8270 | 0.9940 | nan | 0.6903 | 0.9889 |
| 0.0078 | 100.0 | 1100 | 0.0648 | 0.8388 | 0.9031 | 0.9893 | nan | 0.8115 | 0.9946 | nan | 0.6887 | 0.9890 |
| 0.0067 | 101.82 | 1120 | 0.0662 | 0.8417 | 0.9095 | 0.9894 | nan | 0.8246 | 0.9943 | nan | 0.6942 | 0.9891 |
| 0.0074 | 103.64 | 1140 | 0.0698 | 0.8413 | 0.9120 | 0.9893 | nan | 0.8299 | 0.9941 | nan | 0.6936 | 0.9890 |
| 0.0065 | 105.45 | 1160 | 0.0690 | 0.8434 | 0.9106 | 0.9895 | nan | 0.8267 | 0.9944 | nan | 0.6976 | 0.9892 |
| 0.0068 | 107.27 | 1180 | 0.0688 | 0.8424 | 0.9077 | 0.9895 | nan | 0.8208 | 0.9946 | nan | 0.6957 | 0.9892 |
| 0.0067 | 109.09 | 1200 | 0.0760 | 0.8411 | 0.9106 | 0.9893 | nan | 0.8269 | 0.9942 | nan | 0.6932 | 0.9890 |
| 0.0061 | 110.91 | 1220 | 0.0754 | 0.8408 | 0.9132 | 0.9892 | nan | 0.8325 | 0.9939 | nan | 0.6928 | 0.9889 |
| 0.0057 | 112.73 | 1240 | 0.0760 | 0.8423 | 0.9150 | 0.9893 | nan | 0.8361 | 0.9939 | nan | 0.6955 | 0.9890 |
| 0.006 | 114.55 | 1260 | 0.0746 | 0.8437 | 0.9145 | 0.9894 | nan | 0.8348 | 0.9941 | nan | 0.6982 | 0.9892 |
| 0.0052 | 116.36 | 1280 | 0.0662 | 0.8467 | 0.9063 | 0.9899 | nan | 0.8174 | 0.9951 | nan | 0.7037 | 0.9897 |
| 0.0065 | 118.18 | 1300 | 0.0713 | 0.8418 | 0.9067 | 0.9895 | nan | 0.8188 | 0.9946 | nan | 0.6945 | 0.9892 |
| 0.0069 | 120.0 | 1320 | 0.0696 | 0.8437 | 0.9167 | 0.9894 | nan | 0.8396 | 0.9939 | nan | 0.6983 | 0.9891 |
| 0.005 | 121.82 | 1340 | 0.0641 | 0.8458 | 0.9100 | 0.9898 | nan | 0.8253 | 0.9947 | nan | 0.7022 | 0.9895 |
| 0.0054 | 123.64 | 1360 | 0.0648 | 0.8493 | 0.9108 | 0.9901 | nan | 0.8266 | 0.9950 | nan | 0.7087 | 0.9898 |
| 0.0052 | 125.45 | 1380 | 0.0690 | 0.8468 | 0.9151 | 0.9897 | nan | 0.8359 | 0.9944 | nan | 0.7042 | 0.9895 |
| 0.0052 | 127.27 | 1400 | 0.0654 | 0.8481 | 0.9105 | 0.9900 | nan | 0.8260 | 0.9949 | nan | 0.7066 | 0.9897 |
| 0.0051 | 129.09 | 1420 | 0.0680 | 0.8477 | 0.9173 | 0.9897 | nan | 0.8404 | 0.9943 | nan | 0.7059 | 0.9895 |
| 0.0046 | 130.91 | 1440 | 0.0694 | 0.8458 | 0.9083 | 0.9898 | nan | 0.8218 | 0.9949 | nan | 0.7020 | 0.9895 |
| 0.0063 | 132.73 | 1460 | 0.0709 | 0.8472 | 0.9190 | 0.9897 | nan | 0.8440 | 0.9940 | nan | 0.7049 | 0.9894 |
| 0.0048 | 134.55 | 1480 | 0.0756 | 0.8454 | 0.9182 | 0.9895 | nan | 0.8425 | 0.9939 | nan | 0.7015 | 0.9892 |
| 0.0061 | 136.36 | 1500 | 0.0679 | 0.8473 | 0.9108 | 0.9899 | nan | 0.8267 | 0.9948 | nan | 0.7051 | 0.9896 |
| 0.0054 | 138.18 | 1520 | 0.0664 | 0.8482 | 0.9129 | 0.9899 | nan | 0.8312 | 0.9947 | nan | 0.7068 | 0.9897 |
| 0.0054 | 140.0 | 1540 | 0.0712 | 0.8464 | 0.9144 | 0.9897 | nan | 0.8344 | 0.9944 | nan | 0.7033 | 0.9894 |
| 0.0051 | 141.82 | 1560 | 0.0709 | 0.8472 | 0.9163 | 0.9897 | nan | 0.8382 | 0.9943 | nan | 0.7050 | 0.9895 |
| 0.0046 | 143.64 | 1580 | 0.0674 | 0.8477 | 0.9060 | 0.9900 | nan | 0.8167 | 0.9953 | nan | 0.7057 | 0.9898 |
| 0.0054 | 145.45 | 1600 | 0.0666 | 0.8476 | 0.9078 | 0.9900 | nan | 0.8206 | 0.9951 | nan | 0.7055 | 0.9897 |
| 0.0043 | 147.27 | 1620 | 0.0737 | 0.8458 | 0.9115 | 0.9897 | nan | 0.8284 | 0.9946 | nan | 0.7021 | 0.9895 |
| 0.0055 | 149.09 | 1640 | 0.0760 | 0.8456 | 0.9163 | 0.9896 | nan | 0.8384 | 0.9941 | nan | 0.7019 | 0.9893 |
| 0.0046 | 150.91 | 1660 | 0.0737 | 0.8475 | 0.9179 | 0.9897 | nan | 0.8417 | 0.9942 | nan | 0.7055 | 0.9895 |
| 0.0042 | 152.73 | 1680 | 0.0739 | 0.8456 | 0.9136 | 0.9896 | nan | 0.8329 | 0.9944 | nan | 0.7018 | 0.9894 |
| 0.0045 | 154.55 | 1700 | 0.0755 | 0.8457 | 0.9121 | 0.9897 | nan | 0.8296 | 0.9945 | nan | 0.7019 | 0.9894 |
| 0.0045 | 156.36 | 1720 | 0.0794 | 0.8449 | 0.9115 | 0.9896 | nan | 0.8286 | 0.9945 | nan | 0.7005 | 0.9894 |
| 0.0045 | 158.18 | 1740 | 0.0791 | 0.8457 | 0.9115 | 0.9897 | nan | 0.8285 | 0.9946 | nan | 0.7020 | 0.9894 |
| 0.0073 | 160.0 | 1760 | 0.0726 | 0.8461 | 0.9234 | 0.9895 | nan | 0.8533 | 0.9936 | nan | 0.7031 | 0.9892 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| null | transformers | null | null | null | null | null | null | null | null | null | null | sam1120/safety-utcustom-nn-segformer-b5 | [
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karawalla/mistral_b_karawalla_new1 | karawalla | 2023-11-30T00:13:30Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | 2023-11-30T00:13:30Z | 2023-11-30T00:13:23.000Z | null | null | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
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## Model Details
### Model Description
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## Training Details
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#### Preprocessing [optional]
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## Environmental Impact
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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:
- quant_method: bitsandbytes
- 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 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | karawalla/mistral_b_karawalla_new1 | [
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toninhodjj/cgplay | toninhodjj | 2023-11-30T00:14:46Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-30T00:14:46Z | 2023-11-30T00:14:11.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | toninhodjj/cgplay | [
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rechido/20231130_online_task1_batch128_epoch100 | rechido | 2023-11-30T01:06:27Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-30T01:06:27Z | 2023-11-30T00:15:56.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231130_online_task1_batch128_epoch100 | [
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rechido/20231130_online_task1_batch128_epoch500 | rechido | 2023-11-30T01:06:18Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-30T01:06:18Z | 2023-11-30T00:16:25.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231130_online_task1_batch128_epoch500 | [
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rechido/20231130_online_task1_batch128_epoch1000 | rechido | 2023-11-30T01:07:10Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-30T01:07:10Z | 2023-11-30T00:16:39.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231130_online_task1_batch128_epoch1000 | [
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rechido/20231130_online_task1_batch128_epoch200 | rechido | 2023-11-30T01:08:49Z | 0 | 0 | null | [
"diffusers",
"diffusers:DDPMPipeline",
"region:us"
] | 2023-11-30T01:08:49Z | 2023-11-30T00:17:10.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | rechido/20231130_online_task1_batch128_epoch200 | [
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simoneprete/llama-2-7b-prova13 | simoneprete | 2023-11-30T00:22:38Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:22:38Z | 2023-11-30T00:17:44.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | simoneprete/llama-2-7b-prova13 | [
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GoatMilk98/635-a2-distillbert | GoatMilk98 | 2023-11-30T00:19:33Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:19:33Z | 2023-11-30T00:18:48.000Z | null | null | Entry not found | null | transformers | question-answering | null | null | null | null | null | null | null | null | null | GoatMilk98/635-a2-distillbert | [
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sam1120/safety-utcustom-nn-depth-segformer-b0 | sam1120 | 2023-11-30T01:26:16Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:26:16Z | 2023-11-30T00:19:03.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | sam1120/safety-utcustom-nn-depth-segformer-b0 | [
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Realgon/roberta_sst5_padding90model | Realgon | 2023-11-30T01:25:31Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:25:31Z | 2023-11-30T00:20:21.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst5_padding90model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_sst5_padding90model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5805
- Accuracy: 0.2308
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5744 | 1.0 | 534 | 1.5924 | 0.2308 |
| 1.5745 | 2.0 | 1068 | 1.5815 | 0.2308 |
| 1.5745 | 3.0 | 1602 | 1.5858 | 0.2864 |
| 1.5684 | 4.0 | 2136 | 1.5801 | 0.2308 |
| 1.5737 | 5.0 | 2670 | 1.5756 | 0.2864 |
| 1.5751 | 6.0 | 3204 | 1.5837 | 0.2308 |
| 1.5716 | 7.0 | 3738 | 1.5780 | 0.2308 |
| 1.5717 | 8.0 | 4272 | 1.5792 | 0.2308 |
| 1.5704 | 9.0 | 4806 | 1.5815 | 0.2308 |
| 1.5662 | 10.0 | 5340 | 1.5783 | 0.2308 |
| 1.5721 | 11.0 | 5874 | 1.5821 | 0.2308 |
| 1.5719 | 12.0 | 6408 | 1.5807 | 0.2308 |
| 1.5682 | 13.0 | 6942 | 1.5834 | 0.2308 |
| 1.5711 | 14.0 | 7476 | 1.5808 | 0.2308 |
| 1.5701 | 15.0 | 8010 | 1.5821 | 0.2308 |
| 1.5691 | 16.0 | 8544 | 1.5822 | 0.2308 |
| 1.5686 | 17.0 | 9078 | 1.5809 | 0.2308 |
| 1.5714 | 18.0 | 9612 | 1.5798 | 0.2308 |
| 1.5699 | 19.0 | 10146 | 1.5809 | 0.2308 |
| 1.5692 | 20.0 | 10680 | 1.5805 | 0.2308 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst5_padding90model | [
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tuln128/t5-large_PREFIX_TUNING_SEQ2SEQ | tuln128 | 2023-11-30T00:22:06Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:t5-large",
"region:us"
] | 2023-11-30T00:22:06Z | 2023-11-30T00:20:32.000Z | null | null | ---
library_name: peft
base_model: t5-large
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.6.2
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sam1120/safety-utcustom-nn-depth-segformer-b5 | sam1120 | 2023-11-30T01:26:51Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"segformer",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:26:51Z | 2023-11-30T00:21:40.000Z | null | null | ---
license: other
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: safety-utcustom-nn-depth-segformer-b5
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. -->
# safety-utcustom-nn-depth-segformer-b5
This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0459
- Mean Iou: 0.8569
- Mean Accuracy: 0.9060
- Overall Accuracy: 0.9909
- Accuracy Unlabeled: nan
- Accuracy Safe: 0.8159
- Accuracy Unsafe: 0.9961
- Iou Unlabeled: nan
- Iou Safe: 0.7231
- Iou Unsafe: 0.9906
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Safe | Accuracy Unsafe | Iou Unlabeled | Iou Safe | Iou Unsafe |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:---------------:|:-------------:|:--------:|:----------:|
| 1.1909 | 1.82 | 20 | 1.0600 | 0.1633 | 0.3415 | 0.4529 | nan | 0.2232 | 0.4598 | 0.0 | 0.0358 | 0.4542 |
| 1.0619 | 3.64 | 40 | 0.8788 | 0.2884 | 0.5014 | 0.7789 | nan | 0.2066 | 0.7962 | 0.0 | 0.0833 | 0.7820 |
| 0.8868 | 5.45 | 60 | 0.6741 | 0.3401 | 0.5704 | 0.8745 | nan | 0.2473 | 0.8934 | 0.0 | 0.1458 | 0.8746 |
| 0.7243 | 7.27 | 80 | 0.5428 | 0.3750 | 0.6916 | 0.9151 | nan | 0.4542 | 0.9290 | 0.0 | 0.2106 | 0.9144 |
| 0.5175 | 9.09 | 100 | 0.4657 | 0.4081 | 0.7242 | 0.9505 | nan | 0.4839 | 0.9646 | 0.0 | 0.2741 | 0.9502 |
| 0.4293 | 10.91 | 120 | 0.3597 | 0.4280 | 0.7185 | 0.9706 | nan | 0.4507 | 0.9862 | 0.0 | 0.3138 | 0.9702 |
| 0.2718 | 12.73 | 140 | 0.2889 | 0.4268 | 0.6908 | 0.9741 | nan | 0.3897 | 0.9918 | 0.0 | 0.3067 | 0.9738 |
| 0.2483 | 14.55 | 160 | 0.2135 | 0.4159 | 0.6591 | 0.9748 | nan | 0.3237 | 0.9944 | 0.0 | 0.2731 | 0.9745 |
| 0.2047 | 16.36 | 180 | 0.2085 | 0.4314 | 0.7161 | 0.9726 | nan | 0.4437 | 0.9886 | 0.0 | 0.3219 | 0.9723 |
| 0.1529 | 18.18 | 200 | 0.1487 | 0.6554 | 0.7019 | 0.9761 | nan | 0.4106 | 0.9932 | nan | 0.3349 | 0.9758 |
| 0.1419 | 20.0 | 220 | 0.1306 | 0.7098 | 0.7572 | 0.9806 | nan | 0.5200 | 0.9945 | nan | 0.4394 | 0.9803 |
| 0.1419 | 21.82 | 240 | 0.1140 | 0.7313 | 0.7838 | 0.9819 | nan | 0.5735 | 0.9942 | nan | 0.4809 | 0.9816 |
| 0.1014 | 23.64 | 260 | 0.0989 | 0.7573 | 0.8164 | 0.9835 | nan | 0.6388 | 0.9939 | nan | 0.5314 | 0.9832 |
| 0.0684 | 25.45 | 280 | 0.0823 | 0.7813 | 0.8386 | 0.9854 | nan | 0.6826 | 0.9945 | nan | 0.5776 | 0.9851 |
| 0.068 | 27.27 | 300 | 0.0791 | 0.7059 | 0.7220 | 0.9827 | nan | 0.4451 | 0.9989 | nan | 0.4294 | 0.9825 |
| 0.0769 | 29.09 | 320 | 0.0768 | 0.7773 | 0.8579 | 0.9841 | nan | 0.7238 | 0.9919 | nan | 0.5708 | 0.9837 |
| 0.0678 | 30.91 | 340 | 0.0656 | 0.7764 | 0.8289 | 0.9852 | nan | 0.6628 | 0.9950 | nan | 0.5678 | 0.9849 |
| 0.0486 | 32.73 | 360 | 0.0608 | 0.7733 | 0.7962 | 0.9864 | nan | 0.5943 | 0.9982 | nan | 0.5606 | 0.9861 |
| 0.0402 | 34.55 | 380 | 0.0592 | 0.8014 | 0.8821 | 0.9860 | nan | 0.7718 | 0.9924 | nan | 0.6171 | 0.9857 |
| 0.0532 | 36.36 | 400 | 0.0535 | 0.8038 | 0.8400 | 0.9877 | nan | 0.6830 | 0.9969 | nan | 0.6201 | 0.9875 |
| 0.0383 | 38.18 | 420 | 0.0519 | 0.7993 | 0.8419 | 0.9872 | nan | 0.6876 | 0.9963 | nan | 0.6117 | 0.9870 |
| 0.0301 | 40.0 | 440 | 0.0490 | 0.8071 | 0.8429 | 0.9880 | nan | 0.6888 | 0.9970 | nan | 0.6266 | 0.9877 |
| 0.0279 | 41.82 | 460 | 0.0445 | 0.8117 | 0.8457 | 0.9883 | nan | 0.6941 | 0.9972 | nan | 0.6353 | 0.9881 |
| 0.0371 | 43.64 | 480 | 0.0548 | 0.8026 | 0.8898 | 0.9858 | nan | 0.7877 | 0.9918 | nan | 0.6197 | 0.9855 |
| 0.0289 | 45.45 | 500 | 0.0398 | 0.8339 | 0.8711 | 0.9897 | nan | 0.7451 | 0.9970 | nan | 0.6784 | 0.9894 |
| 0.0279 | 47.27 | 520 | 0.0426 | 0.8286 | 0.8798 | 0.9889 | nan | 0.7639 | 0.9957 | nan | 0.6685 | 0.9887 |
| 0.0253 | 49.09 | 540 | 0.0582 | 0.7916 | 0.8858 | 0.9847 | nan | 0.7808 | 0.9908 | nan | 0.5988 | 0.9843 |
| 0.0256 | 50.91 | 560 | 0.0423 | 0.8260 | 0.8670 | 0.9890 | nan | 0.7375 | 0.9966 | nan | 0.6633 | 0.9888 |
| 0.0198 | 52.73 | 580 | 0.0479 | 0.8301 | 0.8854 | 0.9889 | nan | 0.7754 | 0.9953 | nan | 0.6716 | 0.9886 |
| 0.0196 | 54.55 | 600 | 0.0603 | 0.8027 | 0.9025 | 0.9854 | nan | 0.8143 | 0.9906 | nan | 0.6204 | 0.9851 |
| 0.0192 | 56.36 | 620 | 0.0436 | 0.8215 | 0.8885 | 0.9879 | nan | 0.7828 | 0.9941 | nan | 0.6554 | 0.9877 |
| 0.0245 | 58.18 | 640 | 0.0348 | 0.8441 | 0.8846 | 0.9902 | nan | 0.7725 | 0.9968 | nan | 0.6983 | 0.9900 |
| 0.0157 | 60.0 | 660 | 0.0364 | 0.8357 | 0.8813 | 0.9895 | nan | 0.7663 | 0.9963 | nan | 0.6821 | 0.9893 |
| 0.0224 | 61.82 | 680 | 0.0365 | 0.8441 | 0.8697 | 0.9906 | nan | 0.7413 | 0.9981 | nan | 0.6979 | 0.9904 |
| 0.0184 | 63.64 | 700 | 0.0395 | 0.8331 | 0.8809 | 0.9893 | nan | 0.7657 | 0.9961 | nan | 0.6771 | 0.9891 |
| 0.0159 | 65.45 | 720 | 0.0416 | 0.8406 | 0.9028 | 0.9894 | nan | 0.8108 | 0.9948 | nan | 0.6920 | 0.9892 |
| 0.018 | 67.27 | 740 | 0.0381 | 0.8467 | 0.9013 | 0.9900 | nan | 0.8070 | 0.9956 | nan | 0.7036 | 0.9898 |
| 0.0184 | 69.09 | 760 | 0.0362 | 0.8407 | 0.8797 | 0.9900 | nan | 0.7626 | 0.9969 | nan | 0.6917 | 0.9898 |
| 0.0152 | 70.91 | 780 | 0.0403 | 0.8334 | 0.9002 | 0.9888 | nan | 0.8061 | 0.9943 | nan | 0.6783 | 0.9885 |
| 0.0157 | 72.73 | 800 | 0.0402 | 0.8308 | 0.8779 | 0.9892 | nan | 0.7597 | 0.9961 | nan | 0.6726 | 0.9889 |
| 0.0135 | 74.55 | 820 | 0.0365 | 0.8455 | 0.8934 | 0.9901 | nan | 0.7907 | 0.9961 | nan | 0.7011 | 0.9899 |
| 0.0123 | 76.36 | 840 | 0.0365 | 0.8486 | 0.8942 | 0.9904 | nan | 0.7920 | 0.9964 | nan | 0.7070 | 0.9902 |
| 0.0163 | 78.18 | 860 | 0.0344 | 0.8438 | 0.8629 | 0.9907 | nan | 0.7270 | 0.9987 | nan | 0.6970 | 0.9905 |
| 0.0197 | 80.0 | 880 | 0.0414 | 0.8433 | 0.8967 | 0.9898 | nan | 0.7977 | 0.9956 | nan | 0.6969 | 0.9896 |
| 0.0124 | 81.82 | 900 | 0.0385 | 0.8426 | 0.8864 | 0.9900 | nan | 0.7764 | 0.9965 | nan | 0.6953 | 0.9898 |
| 0.0126 | 83.64 | 920 | 0.0408 | 0.8452 | 0.9038 | 0.9898 | nan | 0.8123 | 0.9952 | nan | 0.7009 | 0.9896 |
| 0.0109 | 85.45 | 940 | 0.0439 | 0.8422 | 0.8859 | 0.9900 | nan | 0.7754 | 0.9965 | nan | 0.6945 | 0.9898 |
| 0.0104 | 87.27 | 960 | 0.0418 | 0.8469 | 0.9008 | 0.9901 | nan | 0.8060 | 0.9956 | nan | 0.7039 | 0.9898 |
| 0.0108 | 89.09 | 980 | 0.0390 | 0.8444 | 0.8772 | 0.9904 | nan | 0.7568 | 0.9975 | nan | 0.6985 | 0.9902 |
| 0.0125 | 90.91 | 1000 | 0.0384 | 0.8454 | 0.9049 | 0.9898 | nan | 0.8148 | 0.9951 | nan | 0.7012 | 0.9896 |
| 0.0114 | 92.73 | 1020 | 0.0384 | 0.8491 | 0.9027 | 0.9902 | nan | 0.8098 | 0.9957 | nan | 0.7082 | 0.9900 |
| 0.0101 | 94.55 | 1040 | 0.0452 | 0.8459 | 0.8961 | 0.9901 | nan | 0.7963 | 0.9959 | nan | 0.7020 | 0.9899 |
| 0.0099 | 96.36 | 1060 | 0.0459 | 0.8466 | 0.9040 | 0.9900 | nan | 0.8126 | 0.9953 | nan | 0.7034 | 0.9897 |
| 0.0097 | 98.18 | 1080 | 0.0516 | 0.8414 | 0.9006 | 0.9896 | nan | 0.8061 | 0.9951 | nan | 0.6934 | 0.9893 |
| 0.01 | 100.0 | 1100 | 0.0469 | 0.8443 | 0.8924 | 0.9900 | nan | 0.7886 | 0.9961 | nan | 0.6988 | 0.9898 |
| 0.0106 | 101.82 | 1120 | 0.0428 | 0.8512 | 0.9042 | 0.9904 | nan | 0.8126 | 0.9958 | nan | 0.7123 | 0.9902 |
| 0.0102 | 103.64 | 1140 | 0.0472 | 0.8502 | 0.9099 | 0.9902 | nan | 0.8246 | 0.9952 | nan | 0.7104 | 0.9899 |
| 0.009 | 105.45 | 1160 | 0.0469 | 0.8509 | 0.9058 | 0.9903 | nan | 0.8160 | 0.9956 | nan | 0.7117 | 0.9901 |
| 0.0091 | 107.27 | 1180 | 0.0472 | 0.8514 | 0.9024 | 0.9904 | nan | 0.8090 | 0.9959 | nan | 0.7125 | 0.9902 |
| 0.009 | 109.09 | 1200 | 0.0474 | 0.8523 | 0.9062 | 0.9904 | nan | 0.8167 | 0.9957 | nan | 0.7143 | 0.9902 |
| 0.0086 | 110.91 | 1220 | 0.0506 | 0.8471 | 0.9011 | 0.9901 | nan | 0.8066 | 0.9956 | nan | 0.7043 | 0.9898 |
| 0.0081 | 112.73 | 1240 | 0.0462 | 0.8517 | 0.9047 | 0.9904 | nan | 0.8137 | 0.9957 | nan | 0.7132 | 0.9902 |
| 0.0094 | 114.55 | 1260 | 0.0431 | 0.8557 | 0.9070 | 0.9907 | nan | 0.8180 | 0.9959 | nan | 0.7210 | 0.9905 |
| 0.0078 | 116.36 | 1280 | 0.0387 | 0.8558 | 0.8944 | 0.9910 | nan | 0.7918 | 0.9970 | nan | 0.7209 | 0.9908 |
| 0.0097 | 118.18 | 1300 | 0.0389 | 0.8557 | 0.8938 | 0.9910 | nan | 0.7905 | 0.9971 | nan | 0.7206 | 0.9908 |
| 0.0086 | 120.0 | 1320 | 0.0462 | 0.8514 | 0.9034 | 0.9904 | nan | 0.8110 | 0.9958 | nan | 0.7126 | 0.9902 |
| 0.0075 | 121.82 | 1340 | 0.0470 | 0.8524 | 0.9109 | 0.9903 | nan | 0.8265 | 0.9953 | nan | 0.7147 | 0.9901 |
| 0.0075 | 123.64 | 1360 | 0.0490 | 0.8515 | 0.9059 | 0.9904 | nan | 0.8163 | 0.9956 | nan | 0.7129 | 0.9901 |
| 0.0074 | 125.45 | 1380 | 0.0439 | 0.8541 | 0.9040 | 0.9907 | nan | 0.8120 | 0.9960 | nan | 0.7178 | 0.9904 |
| 0.0088 | 127.27 | 1400 | 0.0454 | 0.8517 | 0.9061 | 0.9904 | nan | 0.8165 | 0.9956 | nan | 0.7133 | 0.9902 |
| 0.0073 | 129.09 | 1420 | 0.0392 | 0.8560 | 0.8977 | 0.9910 | nan | 0.7987 | 0.9968 | nan | 0.7213 | 0.9907 |
| 0.0069 | 130.91 | 1440 | 0.0385 | 0.8572 | 0.8966 | 0.9911 | nan | 0.7962 | 0.9970 | nan | 0.7235 | 0.9909 |
| 0.0091 | 132.73 | 1460 | 0.0398 | 0.8577 | 0.9186 | 0.9906 | nan | 0.8421 | 0.9951 | nan | 0.7250 | 0.9904 |
| 0.007 | 134.55 | 1480 | 0.0428 | 0.8552 | 0.9070 | 0.9907 | nan | 0.8180 | 0.9959 | nan | 0.7200 | 0.9905 |
| 0.0082 | 136.36 | 1500 | 0.0374 | 0.8624 | 0.9010 | 0.9914 | nan | 0.8050 | 0.9971 | nan | 0.7336 | 0.9912 |
| 0.0071 | 138.18 | 1520 | 0.0377 | 0.8632 | 0.9019 | 0.9915 | nan | 0.8067 | 0.9971 | nan | 0.7351 | 0.9913 |
| 0.0067 | 140.0 | 1540 | 0.0435 | 0.8575 | 0.9137 | 0.9907 | nan | 0.8319 | 0.9955 | nan | 0.7245 | 0.9905 |
| 0.0073 | 141.82 | 1560 | 0.0408 | 0.8536 | 0.9067 | 0.9905 | nan | 0.8176 | 0.9958 | nan | 0.7169 | 0.9903 |
| 0.0083 | 143.64 | 1580 | 0.0439 | 0.8457 | 0.9044 | 0.9899 | nan | 0.8137 | 0.9952 | nan | 0.7017 | 0.9896 |
| 0.0077 | 145.45 | 1600 | 0.0445 | 0.8523 | 0.9007 | 0.9906 | nan | 0.8052 | 0.9962 | nan | 0.7143 | 0.9903 |
| 0.0069 | 147.27 | 1620 | 0.0436 | 0.8561 | 0.9054 | 0.9908 | nan | 0.8147 | 0.9961 | nan | 0.7216 | 0.9906 |
| 0.0082 | 149.09 | 1640 | 0.0438 | 0.8518 | 0.8977 | 0.9906 | nan | 0.7990 | 0.9964 | nan | 0.7133 | 0.9904 |
| 0.0061 | 150.91 | 1660 | 0.0453 | 0.8532 | 0.9064 | 0.9905 | nan | 0.8170 | 0.9958 | nan | 0.7162 | 0.9903 |
| 0.0058 | 152.73 | 1680 | 0.0441 | 0.8548 | 0.9073 | 0.9906 | nan | 0.8189 | 0.9958 | nan | 0.7192 | 0.9904 |
| 0.0067 | 154.55 | 1700 | 0.0420 | 0.8565 | 0.8998 | 0.9910 | nan | 0.8030 | 0.9966 | nan | 0.7223 | 0.9907 |
| 0.0066 | 156.36 | 1720 | 0.0472 | 0.8545 | 0.9089 | 0.9906 | nan | 0.8222 | 0.9957 | nan | 0.7186 | 0.9903 |
| 0.0063 | 158.18 | 1740 | 0.0429 | 0.8564 | 0.8999 | 0.9910 | nan | 0.8031 | 0.9966 | nan | 0.7222 | 0.9907 |
| 0.0078 | 160.0 | 1760 | 0.0463 | 0.8564 | 0.9134 | 0.9906 | nan | 0.8314 | 0.9954 | nan | 0.7224 | 0.9904 |
| 0.0059 | 161.82 | 1780 | 0.0445 | 0.8581 | 0.9092 | 0.9909 | nan | 0.8225 | 0.9960 | nan | 0.7256 | 0.9907 |
| 0.006 | 163.64 | 1800 | 0.0436 | 0.8581 | 0.9031 | 0.9910 | nan | 0.8097 | 0.9965 | nan | 0.7253 | 0.9908 |
| 0.0071 | 165.45 | 1820 | 0.0431 | 0.8571 | 0.9050 | 0.9909 | nan | 0.8138 | 0.9962 | nan | 0.7236 | 0.9907 |
| 0.0066 | 167.27 | 1840 | 0.0437 | 0.8584 | 0.9030 | 0.9911 | nan | 0.8095 | 0.9965 | nan | 0.7261 | 0.9908 |
| 0.0061 | 169.09 | 1860 | 0.0458 | 0.8565 | 0.9050 | 0.9908 | nan | 0.8138 | 0.9962 | nan | 0.7224 | 0.9906 |
| 0.0059 | 170.91 | 1880 | 0.0463 | 0.8559 | 0.9076 | 0.9907 | nan | 0.8192 | 0.9959 | nan | 0.7213 | 0.9905 |
| 0.0051 | 172.73 | 1900 | 0.0471 | 0.8574 | 0.9090 | 0.9908 | nan | 0.8221 | 0.9959 | nan | 0.7242 | 0.9906 |
| 0.0091 | 174.55 | 1920 | 0.0475 | 0.8577 | 0.9086 | 0.9909 | nan | 0.8212 | 0.9960 | nan | 0.7248 | 0.9906 |
| 0.006 | 176.36 | 1940 | 0.0429 | 0.8494 | 0.9101 | 0.9901 | nan | 0.8251 | 0.9951 | nan | 0.7089 | 0.9898 |
| 0.0051 | 178.18 | 1960 | 0.0387 | 0.8641 | 0.8986 | 0.9916 | nan | 0.7998 | 0.9974 | nan | 0.7368 | 0.9914 |
| 0.0053 | 180.0 | 1980 | 0.0431 | 0.8569 | 0.9005 | 0.9910 | nan | 0.8043 | 0.9966 | nan | 0.7230 | 0.9908 |
| 0.0053 | 181.82 | 2000 | 0.0433 | 0.8583 | 0.9080 | 0.9909 | nan | 0.8199 | 0.9961 | nan | 0.7259 | 0.9907 |
| 0.0057 | 183.64 | 2020 | 0.0434 | 0.8592 | 0.8999 | 0.9912 | nan | 0.8029 | 0.9969 | nan | 0.7275 | 0.9910 |
| 0.0066 | 185.45 | 2040 | 0.0436 | 0.8599 | 0.9016 | 0.9912 | nan | 0.8064 | 0.9968 | nan | 0.7288 | 0.9910 |
| 0.0062 | 187.27 | 2060 | 0.0459 | 0.8569 | 0.9060 | 0.9909 | nan | 0.8159 | 0.9961 | nan | 0.7231 | 0.9906 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
| null | transformers | null | null | null | null | null | null | null | null | null | null | sam1120/safety-utcustom-nn-depth-segformer-b5 | [
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igorvln/dare_gpt2_ddi_byrelation_finetuning_with_rl | igorvln | 2023-11-30T00:57:41Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:57:41Z | 2023-11-30T00:22:26.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | igorvln/dare_gpt2_ddi_byrelation_finetuning_with_rl | [
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Medilora/medilora-qwen-14b | Medilora | 2023-11-30T01:18:30Z | 0 | 0 | null | [
"peft",
"safetensors",
"en",
"dataset:Medilora/mimic_iii_diagnosis_anonymous",
"dataset:Medilora/us_medical_license_exam_textbooks_en",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | 2023-11-30T01:18:30Z | 2023-11-30T00:22:46.000Z | null | null | ---
license: mit
datasets:
- Medilora/mimic_iii_diagnosis_anonymous
- Medilora/us_medical_license_exam_textbooks_en
language:
- en
library_name: peft
---
# Model Card for Medilora/medilora-qwen-14b
Medilora/medilora-qwen-14b is a 4-bit QLoRA completion retraining of the model Qwen/Qwen-14B.
After training on two of the open corpus, we merge the adapter with CausalLM/14B, which is compatible with the HuggingFace software.
We plan on conducting further instruction tuning.
## Model Details
### Model Descriptions
- **Developed by:** The MediLora team at the University of California San Diego
- **Funded by:** sheer will
- **Model type:** Qwen
- **Language(s) (NLP):** En
- **License:** MIT License
- **Finetuned from model:** Qwen/Qwen-14B
## Uses
This language model, while finetuned on medical datasets, is not a substitute for professional medical advice, diagnosis, or treatment. It is primarily intended for research and development purposes in the field of medical language processing. Its applications can range from aiding in medical literature analysis to supporting the development of healthcare-related chatbots. However, it should not be used as a standalone tool for making clinical decisions.
## Direct Use
This model is designed for direct application in non-critical tasks such as preliminary data analysis, hypothesis generation, and educational purposes. It can assist researchers and developers in understanding medical language patterns and generating insights from medical texts. However, it is not intended for direct clinical use or to provide medical guidance without expert supervision and validation.
## Downstream Use [optional]
When integrated into larger systems or applications, this model can enhance functionalities like automated medical record analysis, preliminary diagnostic assistance, and information retrieval from medical databases. It is crucial that any downstream application involving this model undergoes rigorous testing and validation by medical professionals. The model should be used as a supportive tool rather than a definitive source of medical information.
## Out-of-Scope Use
This model is not designed for and should not be used in scenarios where medical decision-making, diagnosis, or treatment is involved. It is not a replacement for clinical judgment or expertise. Use in critical healthcare applications, patient care management, or any scenario requiring medical licensure is strictly out of scope. Any misuse, particularly in sensitive medical contexts, could lead to inaccurate or harmful outcomes.
## Bias, Risks, and Limitations
We did not incorporate bias-reduction techniques on the original datasets.
The model is not instruction-tuned, therefore it should not be treated or deployed as a chatbot.
### Recommendations
Given the model's focus on medical diagnosis and its training on specialized datasets, it is paramount to emphasize the importance of ongoing validation against real-world clinical outcomes. The model, despite its sophistication, should not replace clinical judgment but rather augment it, considering its training on a limited language scope (English only) and specific medical contexts. Users must be aware of potential biases inherent in the training data, especially regarding diagnoses and treatment recommendations that may vary across different populations and healthcare systems. Continuous monitoring and updating of the model are recommended to ensure its alignment with the latest medical research and practices, thus minimizing risks associated with outdated or incorrect medical advice.
## How 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. -->
- [Medilora/mimic_iii_diagnosis_anonymous](https://huggingface.co/datasets/Medilora/mimic_iii_diagnosis_anonymous)
- This is an anonymized diagnosis notes extracted from the MIMIC_III dataset.
- [Medilora/us_medical_license_exam_textbooks_en](https://huggingface.co/datasets/Medilora/us_medical_license_exam_textbooks_en)
- This is the cleaned raw text corpus of the 18 textbooks contained in the USMLE Exam dataset.
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
We employ 4-bit QLoRA finetune on base model with the two datasets. We use the awesome [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) package to fire up training runs.
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| null | peft | null | null | null | null | null | null | null | null | null | null | Medilora/medilora-qwen-14b | [
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hrysto97/ppo-LunarLander-v2.7 | hrysto97 | 2023-11-30T01:12:28Z | 0 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-30T01:12:28Z | 2023-11-30T00:25:33.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 263.85 +/- 71.76
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | hrysto97/ppo-LunarLander-v2.7 | [
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klumdedum/finetuning-sentiment-model-3000-samples | klumdedum | 2023-11-30T00:36:57Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:36:57Z | 2023-11-30T00:26:07.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8692810457516339
---
<!-- 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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3433
- Accuracy: 0.8667
- F1: 0.8693
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Walmart-the-bag/ChatForge-301M-base | Walmart-the-bag | 2023-11-30T00:40:39Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-30T00:40:39Z | 2023-11-30T00:28:39.000Z | null | null | ---
license: apache-2.0
widget:
- text: 'What is the best'
inference:
parameters:
temperature: 0.1
top_p: 1.0
top_k: 80
repetition_penalty: 2.1
---
This model is a base of KoboldAI discord server, with 4x more data. This is the base model which will not do any good, and will have undesirable generation.
### Training:
- train_batch_size: 46
- num_devices: 1
- gradient_accumulation_steps: 1
- total_train_batch_size: 46
- num_epochs: 4
### Inference:
```python
import torch
from transformers import GPT2LMHeadModel, AutoTokenizer
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "Walmart-the-bag/ChatForge-301M-base"
model = GPT2LMHeadModel.from_pretrained(model_name, use_auth_token=True).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
prompt = "What is the best"
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
output = model.generate(
input_ids,
max_length=100,
temperature=0.1,
top_p=1,
top_k=80,
repetition_penalty=2.1,
pad_token_id=tokenizer.eos_token_id
)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
``` | null | null | null | null | null | null | null | null | null | null | null | null | Walmart-the-bag/ChatForge-301M-base | [
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zibajoon/20231129_Bart-lg-samsum_3ep_Summ | zibajoon | 2023-11-30T00:31:44Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:linydub/bart-large-samsum",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:31:44Z | 2023-11-30T00:31:01.000Z | null | null | Temporary Redirect. Redirecting to /zibajoon/20231129_Bart-Lg-samsum_3ep_Summ_Loss_0.81_R1_0.32/resolve/main/README.md | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | zibajoon/20231129_Bart-lg-samsum_3ep_Summ | [
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Masterjp123/XOrcaSlimWin-13B | Masterjp123 | 2023-11-30T01:11:45Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:11:45Z | 2023-11-30T00:31:51.000Z | null | null | ---
license: llama2
language:
- en
library_name: transformers
---
Basically a SLERP merge of all the Following LLms:
```
Xwin-Lm-13B
Xwin-math-13B
XwinCoder-13B
SlimOrca-13B
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Masterjp123/XOrcaSlimWin-13B | [
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s-xiao/speecht5_tts_voxpopuli_es | s-xiao | 2023-11-30T00:33:01Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:33:01Z | 2023-11-30T00:33:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | s-xiao/speecht5_tts_voxpopuli_es | [
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tkcho/cp-commerce-clf-kr-sku-brand-83cc0333f8294bf1648565b8952ff158 | tkcho | 2023-11-30T00:35:33Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:35:33Z | 2023-11-30T00:34:49.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/cp-commerce-clf-kr-sku-brand-83cc0333f8294bf1648565b8952ff158 | [
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FpOliveira/tupi-gpt2-small-multiclass-multilabel | FpOliveira | 2023-11-30T00:36:09Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T00:36:09Z | 2023-11-30T00:35:31.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | FpOliveira/tupi-gpt2-small-multiclass-multilabel | [
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tkcho/cp-commerce-clf-kr-sku-brand-0dcef03099f909940046d8e1ea66744b | tkcho | 2023-11-30T00:36:37Z | 0 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:36:37Z | 2023-11-30T00:36:10.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tkcho/cp-commerce-clf-kr-sku-brand-0dcef03099f909940046d8e1ea66744b | [
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ElmoVT/dqn-SpaceInvadersNoFrameskip-v4 | ElmoVT | 2023-11-30T00:36:59Z | 0 | 0 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-30T00:36:59Z | 2023-11-30T00:36:20.000Z | null | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 530.50 +/- 69.30
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ElmoVT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ElmoVT -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ElmoVT
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | ElmoVT/dqn-SpaceInvadersNoFrameskip-v4 | [
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yeonsikc/unet_conist40k_cozy | yeonsikc | 2023-11-30T00:37:53Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T00:37:53Z | 2023-11-30T00:37:53.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | yeonsikc/unet_conist40k_cozy | [
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] |
Jaehun/polar-leaf-4-epoch-1 | Jaehun | 2023-11-30T00:39:19Z | 0 | 0 | null | [
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:39:19Z | 2023-11-30T00:38:07.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | Jaehun/polar-leaf-4-epoch-1 | [
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