modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-31 06:26:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-31 06:26:13
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
peyschen/ppo-LunarLander-v2
|
peyschen
| 2023-11-23T10:11:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T10:11:05Z |
---
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: 269.13 +/- 19.11
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
...
```
|
feynman-integrals-nn/topbox-3layers
|
feynman-integrals-nn
| 2023-11-23T10:11:15Z | 5 | 0 |
transformers
|
[
"transformers",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2023-11-10T09:36:14Z |
---
license: cc-by-4.0
---
# topbox-3layers
* [model](https://huggingface.co/feynman-integrals-nn/topbox-3layers)
* [data](https://huggingface.co/datasets/feynman-integrals-nn/topbox/tree/8acb41221c829845af218756baa7985c97927a50)
* [source](https://gitlab.com/feynman-integrals-nn/feynman-integrals-nn/-/tree/main/topbox)
|
enaitzb/a2c-PandaReachDense-v3
|
enaitzb
| 2023-11-23T10:10:05Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T10:04:23Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.13
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
mtc/LeoLM-leo-mistral-hessianai-7b-xnli-absinth-qlora-4bit
|
mtc
| 2023-11-23T10:09:37Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-11-23T10:09:09Z |
---
library_name: peft
---
## 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 0.5.0
|
owanr/SChem5Labels-google-t5-v1_1-xl-inter-frequency-model-cross-ent
|
owanr
| 2023-11-23T10:08:26Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:google/t5-v1_1-xl",
"base_model:finetune:google/t5-v1_1-xl",
"license:apache-2.0",
"region:us"
] | null | 2023-11-23T03:40:41Z |
---
license: apache-2.0
base_model: google/t5-v1_1-xl
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-google-t5-v1_1-xl-inter-frequency-model-cross-ent
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. -->
# SChem5Labels-google-t5-v1_1-xl-inter-frequency-model-cross-ent
This model is a fine-tuned version of [google/t5-v1_1-xl](https://huggingface.co/google/t5-v1_1-xl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.8828
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 11.4711 | 1.0 | 99 | 8.8828 |
| 11.0023 | 2.0 | 198 | 8.8828 |
| 10.7984 | 3.0 | 297 | 8.8828 |
| 11.1695 | 4.0 | 396 | 8.8828 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
TheBloke/Noromaid-20B-v0.1.1-GPTQ
|
TheBloke
| 2023-11-23T09:56:48Z | 27 | 8 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:NeverSleep/Noromaid-20b-v0.1.1",
"base_model:quantized:NeverSleep/Noromaid-20b-v0.1.1",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-11-23T08:47:36Z |
---
base_model: NeverSleep/Noromaid-20b-v0.1.1
inference: false
license: cc-by-nc-4.0
model_creator: IkariDev and Undi
model_name: Noromaid 20B v0.1.1
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{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>
<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 -->
# Noromaid 20B v0.1.1 - GPTQ
- Model creator: [IkariDev and Undi](https://huggingface.co/NeverSleep)
- Original model: [Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- description start -->
# Description
This repo contains GPTQ model files for [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
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/Noromaid-20B-v0.1.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF)
* [IkariDev and Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
<!-- licensing 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/Noromaid-20B-v0.1.1-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.52 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.89 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/Noromaid-20B-v0.1.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 12.04 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.41 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 20.35 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 9.51 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 20.80 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher 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/Noromaid-20B-v0.1.1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Noromaid-20B-v0.1.1-GPTQ:gptq-4bit-128g-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 `Noromaid-20B-v0.1.1-GPTQ`:
```shell
mkdir Noromaid-20B-v0.1.1-GPTQ
huggingface-cli download TheBloke/Noromaid-20B-v0.1.1-GPTQ --local-dir Noromaid-20B-v0.1.1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Noromaid-20B-v0.1.1-GPTQ
huggingface-cli download TheBloke/Noromaid-20B-v0.1.1-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Noromaid-20B-v0.1.1-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 Noromaid-20B-v0.1.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Noromaid-20B-v0.1.1-GPTQ --local-dir Noromaid-20B-v0.1.1-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-128g-actorder_True https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-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/Noromaid-20B-v0.1.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Noromaid-20B-v0.1.1-GPTQ:gptq-4bit-128g-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: `Noromaid-20B-v0.1.1-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/Noromaid-20B-v0.1.1-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{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/Noromaid-20B-v0.1.1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{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: IkariDev and Undi's Noromaid 20B v0.1.1
---
# Disclaimer:
## This is a ***TEST*** version, don't expect everything to work!!!
You may use our custom **prompting format**(scroll down to download them!), or simple alpaca. **(Choose which fits best for you!)**
---
# This model is a collab between [IkariDev](https://huggingface.co/IkariDev) and [Undi](https://huggingface.co/Undi95)!
Tired of the same merges everytime? Here it is, the Noromaid-20b-v0.1.1 model. Suitable for RP, ERP and general stuff.
[Recommended settings - No settings yet(Please suggest some over in the Community tab!)]
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains fp16 files of Noromaid-20b-v0.1.1.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom format, or Alpaca
### Custom format:
UPDATED!! SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
OLD SillyTavern config files: [Context](https://files.catbox.moe/x85uy1.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Training data used:
- [no_robots dataset](https://huggingface.co/Undi95/Llama2-13B-no_robots-alpaca-lora) let the model have more human behavior, enhances the output.
- [Aesir Private RP dataset] New data from a new and never used before dataset, add fresh data, no LimaRP spam, this is 100% new. Thanks to the [MinvervaAI Team](https://huggingface.co/MinervaAI) and, in particular, [Gryphe](https://huggingface.co/Gryphe) for letting us use it!
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
|
baskotayunisha/my_model
|
baskotayunisha
| 2023-11-23T09:56:17Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-23T09:45:53Z |
---
tags:
- generated_from_trainer
model-index:
- name: my_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_model
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
y1m1ng/test_dome
|
y1m1ng
| 2023-11-23T09:54:32Z | 15 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"arxiv:2207.12598",
"arxiv:2112.10752",
"arxiv:2103.00020",
"arxiv:2205.11487",
"arxiv:1910.09700",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-09-16T18:31:05Z |
---
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: true
extra_gated_prompt: |-
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. CompVis claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
---
# Stable Diffusion v1-5 Model Card
Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
For more information about how Stable Diffusion functions, please have a look at [🤗's Stable Diffusion blog](https://huggingface.co/blog/stable_diffusion).
The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2)
checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
You can use this both with the [🧨Diffusers library](https://github.com/huggingface/diffusers) and the [RunwayML GitHub repository](https://github.com/runwayml/stable-diffusion).
### Diffusers
```py
from diffusers import StableDiffusionPipeline
import torch
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
For more detailed instructions, use-cases and examples in JAX follow the instructions [here](https://github.com/huggingface/diffusers#text-to-image-generation-with-stable-diffusion)
### Original GitHub Repository
1. Download the weights
- [v1-5-pruned-emaonly.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt) - 4.27GB, ema-only weight. uses less VRAM - suitable for inference
- [v1-5-pruned.ckpt](https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned.ckpt) - 7.7GB, ema+non-ema weights. uses more VRAM - suitable for fine-tuning
2. Follow instructions [here](https://github.com/runwayml/stable-diffusion).
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based.
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487).
- **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and
tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material
and is not fit for product use without additional safety mechanisms and
considerations.
- No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data.
The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images.
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are primarily limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
### Safety Module
The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers.
This checker works by checking model outputs against known hard-coded NSFW concepts.
The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter.
Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images.
The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-2B (en) and subsets thereof (see next section)
**Training Procedure**
Stable Diffusion v1-5 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through a ViT-L/14 text-encoder.
- The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet.
Currently six Stable Diffusion checkpoints are provided, which were trained as follows.
- [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en).
194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`).
- [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`.
515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en,
filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)).
- [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2` - 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2` - 225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) Resumed from `stable-diffusion-v1-2` - 595,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598).
- [`stable-diffusion-inpainting`](https://huggingface.co/runwayml/stable-diffusion-inpainting) Resumed from `stable-diffusion-v1-5` - then 440,000 steps of inpainting training at resolution 512x512 on “laion-aesthetics v2 5+” and 10% dropping of the text-conditioning. For inpainting, the UNet has 5 additional input channels (4 for the encoded masked-image and 1 for the mask itself) whose weights were zero-initialized after restoring the non-inpainting checkpoint. During training, we generate synthetic masks and in 25% mask everything.
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 2
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 PNDM/PLMS sampling
steps show the relative improvements of the checkpoints:
Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 150000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq.
## Citation
```bibtex
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
```
*This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*
|
LittlePrincess/my-pet-dog-xzg
|
LittlePrincess
| 2023-11-23T09:46:23Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-23T09:41:28Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-xzg Dreambooth model trained by LittlePrincess following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: NPR-220
Sample pictures of this concept:
|
Yntec/ChildrenStoriesAnime
|
Yntec
| 2023-11-23T09:45:36Z | 147 | 7 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"Zovya",
"Children Books",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-20T13:56:44Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
- Zovya
- Children Books
---
# Children's Stories Anime
This version of this model by Zovya has the Waifu 1.4 VAE baked in for better saturation.
Original page:
https://civitai.com/models/64544?modelVersionId=69167
|
florentgbelidji/leoLM-13b-sft-lora-oa
|
florentgbelidji
| 2023-11-23T09:44:51Z | 20 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"custom_code",
"base_model:LeoLM/leo-hessianai-13b",
"base_model:quantized:LeoLM/leo-hessianai-13b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-11-22T19:29:30Z |
---
base_model: LeoLM/leo-hessianai-13b
tags:
- generated_from_trainer
model-index:
- name: leoLM-13b-sft-lora-oa
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. -->
# leoLM-13b-sft-lora-oa
This model is a fine-tuned version of [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-13b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.14.1
|
alejoa/bert-sst2
|
alejoa
| 2023-11-23T09:43:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T00:44:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: sst2
split: validation
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.9139908256880734
---
<!-- 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-sst2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3616
- Accuracy: 0.9140
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2447 | 1.0 | 8419 | 0.3422 | 0.9117 |
| 0.1435 | 2.0 | 16838 | 0.3616 | 0.9140 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
mogaio/TinyLlama-con-emp-lora-ada
|
mogaio
| 2023-11-23T09:42:37Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2023-11-23T09:39:35Z |
---
license: apache-2.0
base_model: PY007/TinyLlama-1.1B-Chat-v0.3
tags:
- generated_from_trainer
model-index:
- name: TinyLlama-con-emp-lora-ada
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. -->
# TinyLlama-con-emp-lora-ada
This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Systran/faster-whisper-large-v3
|
Systran
| 2023-11-23T09:41:12Z | 708,585 | 325 |
ctranslate2
|
[
"ctranslate2",
"audio",
"automatic-speech-recognition",
"en",
"zh",
"de",
"es",
"ru",
"ko",
"fr",
"ja",
"pt",
"tr",
"pl",
"ca",
"nl",
"ar",
"sv",
"it",
"id",
"hi",
"fi",
"vi",
"he",
"uk",
"el",
"ms",
"cs",
"ro",
"da",
"hu",
"ta",
"no",
"th",
"ur",
"hr",
"bg",
"lt",
"la",
"mi",
"ml",
"cy",
"sk",
"te",
"fa",
"lv",
"bn",
"sr",
"az",
"sl",
"kn",
"et",
"mk",
"br",
"eu",
"is",
"hy",
"ne",
"mn",
"bs",
"kk",
"sq",
"sw",
"gl",
"mr",
"pa",
"si",
"km",
"sn",
"yo",
"so",
"af",
"oc",
"ka",
"be",
"tg",
"sd",
"gu",
"am",
"yi",
"lo",
"uz",
"fo",
"ht",
"ps",
"tk",
"nn",
"mt",
"sa",
"lb",
"my",
"bo",
"tl",
"mg",
"as",
"tt",
"haw",
"ln",
"ha",
"ba",
"jw",
"su",
"yue",
"license:mit",
"region:us"
] |
automatic-speech-recognition
| 2023-11-23T09:34:20Z |
---
language:
- en
- zh
- de
- es
- ru
- ko
- fr
- ja
- pt
- tr
- pl
- ca
- nl
- ar
- sv
- it
- id
- hi
- fi
- vi
- he
- uk
- el
- ms
- cs
- ro
- da
- hu
- ta
- 'no'
- th
- ur
- hr
- bg
- lt
- la
- mi
- ml
- cy
- sk
- te
- fa
- lv
- bn
- sr
- az
- sl
- kn
- et
- mk
- br
- eu
- is
- hy
- ne
- mn
- bs
- kk
- sq
- sw
- gl
- mr
- pa
- si
- km
- sn
- yo
- so
- af
- oc
- ka
- be
- tg
- sd
- gu
- am
- yi
- lo
- uz
- fo
- ht
- ps
- tk
- nn
- mt
- sa
- lb
- my
- bo
- tl
- mg
- as
- tt
- haw
- ln
- ha
- ba
- jw
- su
- yue
tags:
- audio
- automatic-speech-recognition
license: mit
library_name: ctranslate2
---
# Whisper large-v3 model for CTranslate2
This repository contains the conversion of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format.
This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper).
## Example
```python
from faster_whisper import WhisperModel
model = WhisperModel("large-v3")
segments, info = model.transcribe("audio.mp3")
for segment in segments:
print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text))
```
## Conversion details
The original model was converted with the following command:
```
ct2-transformers-converter --model openai/whisper-large-v3 --output_dir faster-whisper-large-v3 \
--copy_files tokenizer.json preprocessor_config.json --quantization float16
```
Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html).
## More information
**For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-large-v3).**
|
TheBloke/Noromaid-20B-v0.1.1-AWQ
|
TheBloke
| 2023-11-23T09:31:48Z | 17 | 4 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:NeverSleep/Noromaid-20b-v0.1.1",
"base_model:quantized:NeverSleep/Noromaid-20b-v0.1.1",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2023-11-23T08:47:36Z |
---
base_model: NeverSleep/Noromaid-20b-v0.1.1
inference: false
license: cc-by-nc-4.0
model_creator: IkariDev and Undi
model_name: Noromaid 20B v0.1.1
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{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>
<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 -->
# Noromaid 20B v0.1.1 - AWQ
- Model creator: [IkariDev and Undi](https://huggingface.co/NeverSleep)
- Original model: [Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- description start -->
## Description
This repo contains AWQ model files for [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF)
* [IkariDev and Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
<!-- licensing end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-AWQ/tree/main) | 4 | 128 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 10.87 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.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/Noromaid-20B-v0.1.1-AWQ`.
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: `Noromaid-20B-v0.1.1-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/Noromaid-20B-v0.1.1-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/Noromaid-20B-v0.1.1-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
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/Noromaid-20B-v0.1.1-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{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_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/Noromaid-20B-v0.1.1-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.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: IkariDev and Undi's Noromaid 20B v0.1.1
---
# Disclaimer:
## This is a ***TEST*** version, don't expect everything to work!!!
You may use our custom **prompting format**(scroll down to download them!), or simple alpaca. **(Choose which fits best for you!)**
---
# This model is a collab between [IkariDev](https://huggingface.co/IkariDev) and [Undi](https://huggingface.co/Undi95)!
Tired of the same merges everytime? Here it is, the Noromaid-20b-v0.1.1 model. Suitable for RP, ERP and general stuff.
[Recommended settings - No settings yet(Please suggest some over in the Community tab!)]
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains fp16 files of Noromaid-20b-v0.1.1.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom format, or Alpaca
### Custom format:
UPDATED!! SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
OLD SillyTavern config files: [Context](https://files.catbox.moe/x85uy1.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Training data used:
- [no_robots dataset](https://huggingface.co/Undi95/Llama2-13B-no_robots-alpaca-lora) let the model have more human behavior, enhances the output.
- [Aesir Private RP dataset] New data from a new and never used before dataset, add fresh data, no LimaRP spam, this is 100% new. Thanks to the [MinvervaAI Team](https://huggingface.co/MinervaAI) and, in particular, [Gryphe](https://huggingface.co/Gryphe) for letting us use it!
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
|
carlot/whisper-base-mixed_basewhisper_v1.1
|
carlot
| 2023-11-23T09:29:36Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-21T05:18:09Z |
---
tags:
- generated_from_trainer
datasets:
- audiofolder
model-index:
- name: whisper-base-mixed_basewhisper_v1.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-base-mixed_basewhisper_v1.1
This model was trained from scratch on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Cer: 47.2222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0 | 1000.0 | 1000 | 0.0000 | 69.4444 |
| 0.0 | 2000.0 | 2000 | 0.0000 | 44.4444 |
| 0.0 | 3000.0 | 3000 | 0.0000 | 47.2222 |
| 0.0 | 4000.0 | 4000 | 0.0000 | 47.2222 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.15.0
|
Mousaicv/selfrag-lora
|
Mousaicv
| 2023-11-23T09:26:00Z | 8 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:gpt4_reward_with_format",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-11-23T09:12:03Z |
---
base_model: mrs-7b
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- gpt4_reward_with_format
model-index:
- name: zephyr-7b-sft-lora
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. -->
# zephyr-7b-sft-lora
This model is a fine-tuned version of zephyr-7b on the gpt4_reward.
It achieves the following results on the evaluation set:
- Loss: 0.0911
## 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: 3
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 64
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1001 | 1.0 | 219 | 0.1006 |
| 0.0969 | 2.0 | 439 | 0.0930 |
| 0.0795 | 2.99 | 657 | 0.0911 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
h1t/oms_b_openclip_15_21
|
h1t
| 2023-11-23T09:21:05Z | 4 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"diffusers:OMSPipeline",
"region:us"
] |
text-to-image
| 2023-11-22T09:29:08Z |
---
library_name: diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
tags:
- text-to-image
license: openrail++
inference: false
---
# One More Step
One More Step (OMS) module was proposed in [One More Step: A Versatile Plug-and-Play Module for Rectifying Diffusion Schedule Flaws and Enhancing Low-Frequency Controls](https://github.com/mhh0318/OneMoreStep)
by *Minghui Hu, Jianbin Zheng, Chuanxia Zheng, Tat-Jen Cham et al.*
By **adding one small step** on the top the sampling process, we can address the issues caused by the current schedule flaws of diffusion models **without changing the original model parameters**. This also allows for some control over low-frequency information, such as color.
Our model is **versatile** and can be integrated into almost all widely-used Stable Diffusion frameworks. It's compatible with community favorites such as **LoRA, ControlNet, Adapter, and foundational models**.
## Usage
OMS now is supported 🤗 `diffusers` with a customized pipeline [github](https://github.com/mhh0318/OneMoreStep). To run the model (especially with `LCM` variant), first install the latest version of `diffusers` library as well as `accelerate` and `transformers`.
```bash
pip install --upgrade pip
pip install --upgrade diffusers transformers accelerate
```
And then we clone the repo
```bash
git clone https://github.com/mhh0318/OneMoreStep.git
cd OneMoreStep
```
### SD15 and SD21
Due to differences in the *VAE latent space* between SD1.5/SD2.1 and SDXL, the OMS module for SD1.5/SD2.1 cannot be shared with SDXL, **however, SD1.5/SD2.1 can share the same OMS module as well as with models like LCM that are based on SD1.5 or SD2.1.** For more details, please refer to our paper.
We have uploaded one OMS module for SD15/21 series at [h1t/oms_b_openclip_15_21](https://huggingface.co/h1t/oms_b_openclip_15_21), which has a base architecture, an OpenCLIP text encoder.
We simply put a demo here:
```python
import torch
from diffusers import StableDiffusionPipeline, LCMScheduler
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, variant="fp16", safety_checker=None).to('cuda')
pipe = OMSPipeline.from_pretrained('h1t/oms_b_openclip_15_21', sd_pipeline = sd_pipe, torch_dtype=torch.float16, variant="fp16", trust_remote_code=True)
pipe.to('cuda')
generator = torch.Generator(device=pipe.device).manual_seed(100)
prompt = "a starry night"
image = pipe(prompt, guidance_scale=7.5, num_inference_steps=20, oms_guidance_scale=2., generator=generator)
image['images'][0]
```
and without OMS:
```python
image = pipe(prompt, guidance_scale=7.5, num_inference_steps=20, oms_guidance_scale=2., generator=generator, oms_flag=False)
image['images'][0]
```
We found that the quality of the generative model has been greatly improved.
For more models and more functions like diverse prompt, please refer to [OMS Repo](https://github.com/mhh0318/OneMoreStep).
|
DContrerasF/poca-SoccerTwos-alt
|
DContrerasF
| 2023-11-23T09:17:23Z | 11 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2023-11-17T19:24:39Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: DContrerasF/poca-SoccerTwos-alt
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
frankminors123/Chinese-CodeLlama-7B-PT
|
frankminors123
| 2023-11-23T09:13:41Z | 3 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"zh",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-26T06:46:42Z |
---
license: apache-2.0
language:
- zh
- en
tags:
- code
---
# Chinese-CodeLlama-7B-PT
We have further expanded the vocabulary based on Chinese-LLaMA-2-7B which from 55296 to 75548, it is worth noting that the most of them are code tokens. On [MBPP](https://huggingface.co/datasets/mbpp), we calculated the compression rate of the tokenizer to be 4.509 `bytes/token`, and we will reduce this value in the future work to improve training and inference efficiency.
We pre-trained the model based on LoRA which the rank is 8 and the trainable LoRA layers contain `q_proj` and `v_proj`, at the same time, `embed_tokens` and `lm_head` layers were trained with full parameters. All trainable parameters are float32.
The training data contains approximately 400 million tokens which from high-quality code dataset on HuggingFace.
In addition, we applied `memory_efficient_attention` to the pre-training, which saves us a lot of GPU memory space. If you want to quickly use this technology in your LLaMA model, you can refer to my GitHub: https://github.com/FrankMinions/memory_efficient_adapter.
Our model can be used for SFT, and we hope to contribute more valuable work in the Chinese field.
The second version of our fine-tuned model named [Chinese-CodeLlama-7B-SFT-V2](https://huggingface.co/frankminors123/Chinese-CodeLlama-7B-SFT-V2) has been launched. We use a sequence length of 1k for pre-training (this model), and continue training based on this length during the fine-tuning stage. Based on a larger base period of rotary positional embeddings, it can support up 15k context length extrapolation at inference time.
|
mtc/microsoft-Orca-2-7b-classification-with-explanation-qlora-4bit
|
mtc
| 2023-11-23T09:11:34Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-11-23T09:11:09Z |
---
library_name: peft
---
## 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 0.5.0
|
tuanio/w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16
|
tuanio
| 2023-11-23T09:09:29Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"generated_from_trainer",
"base_model:nguyenvulebinh/wav2vec2-base-vietnamese-250h",
"base_model:finetune:nguyenvulebinh/wav2vec2-base-vietnamese-250h",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2023-11-23T08:07:13Z |
---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16
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. -->
# w2v2_ablation_with_ling_head-drop0.1-not-load-best-wer-best_on_tp0.025_tl10_fp0.001_fl16
This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4141
- Wer: 0.0914
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 119.415 | 0.94 | 100 | 91.5112 | 18.6364 |
| 74.7916 | 1.89 | 200 | 12.2928 | 0.9951 |
| 6.9068 | 2.83 | 300 | 5.2345 | 1.0 |
| 5.1207 | 3.77 | 400 | 5.0365 | 1.0 |
| 4.7306 | 4.72 | 500 | 4.9152 | 1.0 |
| 4.4974 | 5.66 | 600 | 4.9315 | 1.0 |
| 4.3923 | 6.6 | 700 | 4.7918 | 1.0 |
| 4.3447 | 7.55 | 800 | 4.6447 | 1.0 |
| 4.225 | 8.49 | 900 | 4.6061 | 1.0 |
| 3.9805 | 9.43 | 1000 | 3.6422 | 0.8733 |
| 2.8303 | 10.38 | 1100 | 1.7824 | 0.3489 |
| 1.5807 | 11.32 | 1200 | 1.0908 | 0.2162 |
| 1.1284 | 12.26 | 1300 | 0.8473 | 0.1640 |
| 0.8703 | 13.21 | 1400 | 0.7322 | 0.1423 |
| 0.7576 | 14.15 | 1500 | 0.6551 | 0.1325 |
| 0.6256 | 15.09 | 1600 | 0.6027 | 0.1387 |
| 0.594 | 16.04 | 1700 | 0.5550 | 0.1300 |
| 0.5492 | 16.98 | 1800 | 0.5200 | 0.1159 |
| 0.476 | 17.92 | 1900 | 0.5012 | 0.1091 |
| 0.4822 | 18.87 | 2000 | 0.5112 | 0.1074 |
| 0.4351 | 19.81 | 2100 | 0.4985 | 0.1179 |
| 0.4169 | 20.75 | 2200 | 0.4712 | 0.1061 |
| 0.3957 | 21.7 | 2300 | 0.4613 | 0.0988 |
| 0.3885 | 22.64 | 2400 | 0.4610 | 0.1025 |
| 0.3827 | 23.58 | 2500 | 0.4509 | 0.0978 |
| 0.3468 | 24.53 | 2600 | 0.4549 | 0.0951 |
| 0.3451 | 25.47 | 2700 | 0.4556 | 0.1019 |
| 0.3234 | 26.42 | 2800 | 0.4554 | 0.1104 |
| 0.31 | 27.36 | 2900 | 0.4568 | 0.0988 |
| 0.3026 | 28.3 | 3000 | 0.4211 | 0.0965 |
| 0.2905 | 29.25 | 3100 | 0.4305 | 0.0911 |
| 0.2964 | 30.19 | 3200 | 0.4379 | 0.0990 |
| 0.302 | 31.13 | 3300 | 0.4379 | 0.0943 |
| 0.2576 | 32.08 | 3400 | 0.4293 | 0.0933 |
| 0.2771 | 33.02 | 3500 | 0.4239 | 0.0928 |
| 0.268 | 33.96 | 3600 | 0.4228 | 0.0894 |
| 0.2458 | 34.91 | 3700 | 0.4288 | 0.0899 |
| 0.2553 | 35.85 | 3800 | 0.4312 | 0.0966 |
| 0.2424 | 36.79 | 3900 | 0.4162 | 0.0917 |
| 0.2501 | 37.74 | 4000 | 0.4088 | 0.0840 |
| 0.2498 | 38.68 | 4100 | 0.4144 | 0.0921 |
| 0.2273 | 39.62 | 4200 | 0.4154 | 0.0863 |
| 0.23 | 40.57 | 4300 | 0.4157 | 0.0868 |
| 0.2409 | 41.51 | 4400 | 0.4033 | 0.0826 |
| 0.248 | 42.45 | 4500 | 0.4122 | 0.0847 |
| 0.218 | 43.4 | 4600 | 0.4052 | 0.0848 |
| 0.1979 | 44.34 | 4700 | 0.4063 | 0.0887 |
| 0.2091 | 45.28 | 4800 | 0.4078 | 0.0823 |
| 0.2097 | 46.23 | 4900 | 0.4177 | 0.0893 |
| 0.2017 | 47.17 | 5000 | 0.4295 | 0.0887 |
| 0.1899 | 48.11 | 5100 | 0.4177 | 0.0919 |
| 0.195 | 49.06 | 5200 | 0.4109 | 0.0880 |
| 0.179 | 50.0 | 5300 | 0.4089 | 0.0879 |
| 0.1773 | 50.94 | 5400 | 0.4071 | 0.0843 |
| 0.1889 | 51.89 | 5500 | 0.4072 | 0.0885 |
| 0.1987 | 52.83 | 5600 | 0.4033 | 0.0873 |
| 0.1979 | 53.77 | 5700 | 0.4033 | 0.0928 |
| 0.1777 | 54.72 | 5800 | 0.4077 | 0.0898 |
| 0.1742 | 55.66 | 5900 | 0.3969 | 0.0838 |
| 0.1678 | 56.6 | 6000 | 0.3997 | 0.0806 |
| 0.1726 | 57.55 | 6100 | 0.3978 | 0.0885 |
| 0.1602 | 58.49 | 6200 | 0.3967 | 0.0860 |
| 0.1681 | 59.43 | 6300 | 0.4039 | 0.0901 |
| 0.1594 | 60.38 | 6400 | 0.3992 | 0.0856 |
| 0.171 | 61.32 | 6500 | 0.4058 | 0.0890 |
| 0.1691 | 62.26 | 6600 | 0.4078 | 0.0842 |
| 0.1724 | 63.21 | 6700 | 0.4161 | 0.0903 |
| 0.172 | 64.15 | 6800 | 0.4121 | 0.0899 |
| 0.1717 | 65.09 | 6900 | 0.4111 | 0.0878 |
| 0.1775 | 66.04 | 7000 | 0.4109 | 0.0926 |
| 0.1607 | 66.98 | 7100 | 0.4080 | 0.0908 |
| 0.1606 | 67.92 | 7200 | 0.4070 | 0.0930 |
| 0.1801 | 68.87 | 7300 | 0.4096 | 0.0908 |
| 0.16 | 69.81 | 7400 | 0.4030 | 0.0933 |
| 0.1433 | 70.75 | 7500 | 0.4059 | 0.0920 |
| 0.1473 | 71.7 | 7600 | 0.4120 | 0.0979 |
| 0.1396 | 72.64 | 7700 | 0.4062 | 0.0922 |
| 0.1429 | 73.58 | 7800 | 0.4079 | 0.0899 |
| 0.1332 | 74.53 | 7900 | 0.4055 | 0.0851 |
| 0.1429 | 75.47 | 8000 | 0.4081 | 0.0922 |
| 0.1528 | 76.42 | 8100 | 0.4083 | 0.0853 |
| 0.1547 | 77.36 | 8200 | 0.4139 | 0.0945 |
| 0.1384 | 78.3 | 8300 | 0.4111 | 0.0933 |
| 0.1696 | 79.25 | 8400 | 0.4132 | 0.0943 |
| 0.1483 | 80.19 | 8500 | 0.4139 | 0.0906 |
| 0.1547 | 81.13 | 8600 | 0.4156 | 0.0959 |
| 0.149 | 82.08 | 8700 | 0.4119 | 0.0905 |
| 0.1294 | 83.02 | 8800 | 0.4145 | 0.0945 |
| 0.1383 | 83.96 | 8900 | 0.4151 | 0.0917 |
| 0.1356 | 84.91 | 9000 | 0.4165 | 0.0952 |
| 0.1491 | 85.85 | 9100 | 0.4188 | 0.0950 |
| 0.1395 | 86.79 | 9200 | 0.4174 | 0.0950 |
| 0.1439 | 87.74 | 9300 | 0.4151 | 0.0919 |
| 0.1421 | 88.68 | 9400 | 0.4152 | 0.0931 |
| 0.1443 | 89.62 | 9500 | 0.4160 | 0.0944 |
| 0.1429 | 90.57 | 9600 | 0.4138 | 0.0928 |
| 0.1397 | 91.51 | 9700 | 0.4149 | 0.0918 |
| 0.155 | 92.45 | 9800 | 0.4144 | 0.0915 |
| 0.1406 | 93.4 | 9900 | 0.4139 | 0.0921 |
| 0.1328 | 94.34 | 10000 | 0.4140 | 0.0929 |
| 0.1461 | 95.28 | 10100 | 0.4142 | 0.0914 |
| 0.1455 | 96.23 | 10200 | 0.4142 | 0.0913 |
| 0.155 | 97.17 | 10300 | 0.4139 | 0.0914 |
| 0.147 | 98.11 | 10400 | 0.4140 | 0.0918 |
| 0.1298 | 99.06 | 10500 | 0.4140 | 0.0917 |
| 0.1508 | 100.0 | 10600 | 0.4141 | 0.0914 |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
|
joedonino/zephyr-7b-radia-html-events-v6
|
joedonino
| 2023-11-23T09:04:55Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"region:us"
] | null | 2023-11-23T09:04:34Z |
---
library_name: peft
base_model: HuggingFaceH4/zephyr-7b-beta
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
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: float16
### Framework versions
- PEFT 0.6.3.dev0
|
joddiy/my_awesome_eli5_mlm_model
|
joddiy
| 2023-11-23T09:01:08Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:eli5",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-23T08:55:12Z |
---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
datasets:
- eli5
model-index:
- name: my_awesome_eli5_mlm_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_mlm_model
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0341
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2476 | 1.0 | 1128 | 2.0795 |
| 2.1662 | 2.0 | 2256 | 2.0502 |
| 2.1006 | 3.0 | 3384 | 2.0148 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Xwin-LM/Xwin-Math-7B-V1.0
|
Xwin-LM
| 2023-11-23T09:00:00Z | 12 | 2 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T07:00:33Z |
---
license: llama2
---
# Xwin-Math
<p align="center">
<a href="https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Math"><img src="https://img.shields.io/badge/GitHub-yellow.svg?style=social&logo=github"></a>
<a href="https://huggingface.co/Xwin-LM"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue"></a>
</p>
Xwin-Math is a series of powerful SFT LLMs for math problems based on LLaMA-2.
## 🔥 News
- 💥 [Nov, 2023] The [Xwin-Math-70B-V1.0](https://huggingface.co/Xwin-LM/Xwin-Math-70B-V1.0) model achieves **31.8 pass@1 on the MATH benchmark** and **87.0 pass@1 on the GSM8K benchmark**. This performance places it first amongst all open-source models!
- 💥 [Nov, 2023] The [Xwin-Math-7B-V1.0](https://huggingface.co/Xwin-LM/Xwin-Math-7B-V1.0) and [Xwin-Math-13B-V1.0](https://huggingface.co/Xwin-LM/Xwin-Math-13B-V1.0) models achieve **66.6 and 76.2 pass@1 on the GSM8K benchmark**, ranking as top-1 among all LLaMA-2 based 7B and 13B open-source models respectively!
## ✨ Model Card
| Model | GSM8K | MATH | Checkpoint | License |
|:-:|:-:|:-:|:-:|:-:|
|Xwin-Math-7B-V1.0 | 66.6 | 17.4 | 🤗 <a href="https://huggingface.co/Xwin-LM/Xwin-Math-7B-V1.0" target="_blank">HF Link</a> | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License|
|Xwin-Math-13B-V1.0| 76.2 | 21.7 | 🤗 <a href="https://huggingface.co/Xwin-LM/Xwin-Math-13B-V1.0" target="_blank">HF Link</a> | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License|
|Xwin-Math-70B-V1.0| 87.0 | 31.8 | 🤗 <a href="https://huggingface.co/Xwin-LM/Xwin-Math-70B-V1.0" target="_blank">HF Link</a> | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License|
## 🚀 Benchmarks
### Xwin-Math performance on [MATH](https://github.com/hendrycks/math) and [GSM8K](https://github.com/openai/grade-school-math).
Xwin-Math-70B-V1.0 has achieved **31.8% on MATH** and **87.0% on GSM8K**. These scores are **5.3** and **3.1** points higher, respectively, than the previous state-of-the-art open-source MetaMath and LEMAv1 model.
| **Model** |**MATH (Our test)** | **GSM8K (Our test)** |
|:-:|:-:|:-:|
| GPT-4 (zero-shot) | 52.4 | 94.8 |
| GPT-35-Turbo (8-shot)| 37.1 | 81.0 |
| |
| WizardMath-70B | 23.9 | 81.1 |
| MAmmoTH-70B | 20.8 | 72.6 |
| MetaMath-70B | 26.5 | 82.0 |
| LEMAv1-70B | 25.9 | 83.9 |
|**Xwin-Math-70B-V1.0** |**31.8**|**87.0**|
| |
| WizardMath-13B | 15.0 | 63.7 |
| MAmmoTH-13B | 12.3 | 56.2 |
| MetaMath-13B | 22.7 | 70.9 |
| LEMAv1-13B | 13.6 | 65.0 |
|**Xwin-Math-13B-V1.0** | 21.7 | 76.2 |
| |
| WizardMath-7B | 10.9 | 55.0 |
| MAmmoTH-7B | 9.6 | 50.2 |
| MetaMath-7B | 20.1 | 66.6 |
| LEMAv1-7B | 10.0 | 54.7 |
|**Xwin-Math-7B-V1.0** | 17.4 | 66.6 |
We obtain these results using our flexible evaluation strategy. Due to differences in environment and hardware, the test results may be slightly different from the report, but we ensure that the evaluation is as accurate and fair as possible.
### Xwin-Math performance on other math benchmarks.
Our 70B model shows strong mathematical reasoning capabilities among all open-sourced models. Also note that our model even approaches or surpasses the performance of GPT-35-Turbo on some benchmarks.
| **Model** | SVAMP | ASDiv | NumGlue | Algebra | MAWPS | **Average** |
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| GPT-35-Turbo (8-shot)| 80.6 | 84.1 | 81.8 | 90.5 | 91.7 | 85.7 |
| |
| WizardMath-70B | 80.2 | 75.8 | 71.4 | 64.0 | 74.9 | 73.3 |
| MAmmoTH-70B | 71.2 | 73.9 | 62.7 | 58.1 | 72.2 | 67.6 |
| MetaMath-70B | 85.8 | 81.1 | 77.5 | 79.7 | 81.4 | 81.1 |
| LEMAv1-70B-MATH * | 81.6 | 77.1 | 72.1 | 69.4 | 81.8 | 76.5 |
|**Xwin-Math-70B-V1.0** | 84.0 | 84.1 | 81.3 | 78.4 | 90.8 | 83.7 |
\* LEMAv1 has two models, and we report the better LEMAv1-70B-MATH model in these benchmarks.
## 🔨 Evaluation
In order to evaluate a model's mathematical capabilities more flexibly and ensure a fair comparison of results, particularly for the MATH benchmark, we have developed a new evaluation tool. We have also assessed the pass@1 results of recent models on MATH and GSM8K benchmarks, which provides more accurate results.
We hope this toolkit can benefit open-source community by providing more accurate insights and conclusions. For a deeper understanding of our evaluation tool and methods, please visit [here](https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Math/eval)
* "Report" refers to the accuracy stated in the original papers.
* "Repro" indicates the results is reproduced by generating responses and evaluating them using the respective open-source models and scripts.
* "Strict" and "Flex" denote the results we achieved by employing our two strategies to extract answer and evaluate the same responses as "Repro".
| Model | MATH <br> (Report) <br/> |MATH <br> (Repro) <br/> | MATH <br> (Strict) <br/> |MATH <br> (Flex) <br/> | GSM8K <br> (Report) <br/> |GSM8K <br> (Repro) <br/>| GSM8K <br> (Strict) <br/> | GSM8K <br> (Report) <br/> |
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| GPT-35-Turbo (8-shot)| 34.1 | - | 23.8 | 37.1 | 80.8 | - | 77.9 | 81.0 |
| |
| WizardMath-70B | 22.7 | 23.0 | 23.9 | 23.9 | 81.6 | 81.4 | 81.1 | 81.1 |
| MAmmoTH-70B | 21.1 | 18.0 | 20.0 | 20.8 | 72.4 | 72.6 | 72.6 | 72.6 |
| MetaMath-70B | 26.6 | 25.9 | 26.3 | 26.5 | 82.3 | 82.3 | 82.0 | 82.0 |
|**Xwin-Math-70B-V1.0** | - | - |**31.8**|**31.8**| - | - |**87.0**|**87.0**|
| |
| WizardMath-13B | 14.0 | 14.2 | 14.9 | 15.0 | 63.9 | 63.9 | 63.7 | 63.7 |
| MAmmoTH-13B | 12.9 | 10.8 | 11.8 | 12.3 | 56.3 | 56.2 | 56.1 | 56.2 |
| MetaMath-13B | 22.4 | 22.5 | 22.6 | 22.7 | 72.3 | 71.0 | 70.9 | 70.9 |
|**Xwin-Math-13B-V1.0** | - | - | 21.6 | 21.7 | - | - | 76.2 | 76.2 |
| |
| WizardMath-7B | 10.7 | 10.3 | 10.9 | 10.9 | 54.9 | 55.2 | 55.0 | 55.0 |
| MAmmoTH-7B | 10.4 | 8.6 | 9.1 | 9.6 | 50.5 | 50.2 | 50.2 | 50.2 |
| MetaMath-7B | 19.8 | 19.6 | 19.9 | 20.1 | 66.5 | 66.6 | 66.6 | 66.6 |
|**Xwin-Math-7B-V1.0** | - | - | 17.3 | 17.4 | - | - | 66.6 | 66.6 |
### Installation
Before you start, please install the requirements.
```bash
pip install -r requirements.txt
```
We tested our result using `python 3.8` and `cuda 11.8`. We recommend you use docker.
```bash
docker run --gpus all -it --rm --ipc=host superbench/dev:cuda11.8
```
### Generate
To generate the model's responses, you can use the `generate.py` script. Please be aware that generating responses is separate from verifying their correctness. After that, we will then check for their correctness.
For the generation process, we use the Vicuna-v1.1 system prompt with chain-of-thought and format instruction. We also employ a greedy decoding strategy and set the maximum sequence length to 2048.
```
"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {instruction} Give your solution in detail. In the end, write your final answer in the format of 'The answer is: <ANSWER>.'. ASSISTANT: "
```
Here is an simple example to generate using [vLLM](https://docs.vllm.ai/en/latest/).
```bash
cd eval
python generate.py --dataset_path dataset/gsm8k.json --model_path path/to/your/model --tensor_parallel_size 4
```
By default the results will be output to the `eval/response`, using the prompt `eval/prompt/xwin_math.json`. If you wish to change the output path or use a different prompt
```bash
python generate.py --dataset_path dataset/gsm8k.json --model_path path/to/your/model --tensor_parallel_size 4 --output_path /your/path --prompt_path /your/path
```
We provide some datasets (in `eval/dataset`):
- `gsm8k.json`: GSM8K.
- `math.json`: MATH.
- `combination.json`: A combination of many benchmarks, can evaluate the OOD capability of the model.
If you wan't to use your own datasets, please format your dataset like this.
```jsonc
[
{
"question": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
"answer": "18",
"type": "GSM8K",
"subtype": "",
"level": 0,
},
// ... more data items
]
```
### Evaluate
To verify the accuracy of the answers after generation, you can use the `check.py script.
Here is an simple example
```bash
cd eval
python eval.py /path/to/model/response
```
The result will be saved in `eval/evaluation`
If you do not want to save the results or want to change the save path
```bash
python eval.py --data_path /path/to/model/response --save_path /path/to/save --save_result True
```
Once you run the script, the terminal will display the output as a table. This table will show the number of instances for each benchmark and the corresponding accuracy. Here is a hypothetical example of what the output might look like:
||Type|Subtype|Level|Correct|Incorrect|Total|Accuracy|
|---|---|---|---|---|---|---|---|
|0|MAWPS|addsub|0|359|33|392|0.915816|
|1|MAWPS|multiarith|0|586|14|600|0.976667|
|...|
## Citation
Please consider citing our work if you use the data or code in this repo.
```
@software{xwin-math,
title = {Xwin-Math},
author = {Xwin-Math Team},
url = {https://github.com/Xwin-LM/Xwin-LM/Xwin-Math},
version = {pre-release},
year = {2023},
month = {11},
}
```
## Acknowledgements
Thanks to [Llama 2](https://ai.meta.com/llama/), [FastChat](https://github.com/lm-sys/FastChat), and [vLLM](https://github.com/vllm-project/vllm).
|
SamJu3/sd-danielle-model-lora-ssd
|
SamJu3
| 2023-11-23T08:59:34Z | 2 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:segmind/SSD-1B",
"base_model:adapter:segmind/SSD-1B",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-23T04:57:41Z |
---
license: creativeml-openrail-m
base_model: segmind/SSD-1B
dataset: /home/cora3/vscode_project/SweetBrothers/kohya_ss/images/train/oca
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - SamJu3/sd-danielle-model-lora-ssd
These are LoRA adaption weights for segmind/SSD-1B. The weights were fine-tuned on the /home/cora3/vscode_project/SweetBrothers/kohya_ss/images/train/oca dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
TheBloke/Noromaid-20B-v0.1.1-GGUF
|
TheBloke
| 2023-11-23T08:58:48Z | 899 | 23 |
transformers
|
[
"transformers",
"gguf",
"llama",
"base_model:NeverSleep/Noromaid-20b-v0.1.1",
"base_model:quantized:NeverSleep/Noromaid-20b-v0.1.1",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-23T08:47:36Z |
---
base_model: NeverSleep/Noromaid-20b-v0.1.1
inference: false
license: cc-by-nc-4.0
model_creator: IkariDev and Undi
model_name: Noromaid 20B v0.1.1
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{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>
<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 -->
# Noromaid 20B v0.1.1 - GGUF
- Model creator: [IkariDev and Undi](https://huggingface.co/NeverSleep)
- Original model: [Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF)
* [IkariDev and Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [IkariDev and Undi's Noromaid 20B v0.1.1](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [noromaid-20b-v0.1.1.Q2_K.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q2_K.gguf) | Q2_K | 2 | 8.31 GB| 10.81 GB | smallest, significant quality loss - not recommended for most purposes |
| [noromaid-20b-v0.1.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q3_K_S.gguf) | Q3_K_S | 3 | 8.66 GB| 11.16 GB | very small, high quality loss |
| [noromaid-20b-v0.1.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q3_K_M.gguf) | Q3_K_M | 3 | 9.70 GB| 12.20 GB | very small, high quality loss |
| [noromaid-20b-v0.1.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q3_K_L.gguf) | Q3_K_L | 3 | 10.63 GB| 13.13 GB | small, substantial quality loss |
| [noromaid-20b-v0.1.1.Q4_0.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q4_0.gguf) | Q4_0 | 4 | 11.29 GB| 13.79 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [noromaid-20b-v0.1.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q4_K_S.gguf) | Q4_K_S | 4 | 11.34 GB| 13.84 GB | small, greater quality loss |
| [noromaid-20b-v0.1.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q4_K_M.gguf) | Q4_K_M | 4 | 12.04 GB| 14.54 GB | medium, balanced quality - recommended |
| [noromaid-20b-v0.1.1.Q5_0.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q5_0.gguf) | Q5_0 | 5 | 13.77 GB| 16.27 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [noromaid-20b-v0.1.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q5_K_S.gguf) | Q5_K_S | 5 | 13.77 GB| 16.27 GB | large, low quality loss - recommended |
| [noromaid-20b-v0.1.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q5_K_M.gguf) | Q5_K_M | 5 | 14.16 GB| 16.66 GB | large, very low quality loss - recommended |
| [noromaid-20b-v0.1.1.Q6_K.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q6_K.gguf) | Q6_K | 6 | 16.40 GB| 18.90 GB | very large, extremely low quality loss |
| [noromaid-20b-v0.1.1.Q8_0.gguf](https://huggingface.co/TheBloke/Noromaid-20B-v0.1.1-GGUF/blob/main/noromaid-20b-v0.1.1.Q8_0.gguf) | Q8_0 | 8 | 21.25 GB| 23.75 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Noromaid-20B-v0.1.1-GGUF and below it, a specific filename to download, such as: noromaid-20b-v0.1.1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Noromaid-20B-v0.1.1-GGUF noromaid-20b-v0.1.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Noromaid-20B-v0.1.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
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
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Noromaid-20B-v0.1.1-GGUF noromaid-20b-v0.1.1.Q4_K_M.gguf --local-dir . --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>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m noromaid-20b-v0.1.1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Noromaid-20B-v0.1.1-GGUF", model_file="noromaid-20b-v0.1.1.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run 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 start -->
# Original model card: IkariDev and Undi's Noromaid 20B v0.1.1
---
# Disclaimer:
## This is a ***TEST*** version, don't expect everything to work!!!
You may use our custom **prompting format**(scroll down to download them!), or simple alpaca. **(Choose which fits best for you!)**
---
# This model is a collab between [IkariDev](https://huggingface.co/IkariDev) and [Undi](https://huggingface.co/Undi95)!
Tired of the same merges everytime? Here it is, the Noromaid-20b-v0.1.1 model. Suitable for RP, ERP and general stuff.
[Recommended settings - No settings yet(Please suggest some over in the Community tab!)]
<!-- description start -->
## Description
<!-- [Recommended settings - contributed by localfultonextractor](https://files.catbox.moe/ue0tja.json) -->
This repo contains fp16 files of Noromaid-20b-v0.1.1.
[FP16 - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1)
<!-- [GGUF - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GGUF)-->
<!-- [GPTQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-GPTQ)-->
<!-- [exl2[8bpw-8h] - by AzureBlack](https://huggingface.co/AzureBlack/Echidna-13b-v0.3-8bpw-8h-exl2)-->
<!-- [AWQ - By TheBloke](https://huggingface.co/TheBloke/Athena-v4-AWQ)-->
<!-- [fp16 - by IkariDev+Undi95](https://huggingface.co/IkariDev/Athena-v4)-->
[GGUF - by IkariDev and Undi](https://huggingface.co/NeverSleep/Noromaid-20b-v0.1.1-GGUF)
<!-- [OLD(GGUF - by IkariDev+Undi95)](https://huggingface.co/IkariDev/Athena-v4-GGUF)-->
## Ratings:
Note: We have permission of all users to upload their ratings, we DONT screenshot random reviews without asking if we can put them here!
No ratings yet!
If you want your rating to be here, send us a message over on DC and we'll put up a screenshot of it here. DC name is "ikaridev" and "undi".
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom format, or Alpaca
### Custom format:
UPDATED!! SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
OLD SillyTavern config files: [Context](https://files.catbox.moe/x85uy1.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
## Training data used:
- [no_robots dataset](https://huggingface.co/Undi95/Llama2-13B-no_robots-alpaca-lora) let the model have more human behavior, enhances the output.
- [Aesir Private RP dataset] New data from a new and never used before dataset, add fresh data, no LimaRP spam, this is 100% new. Thanks to the [MinvervaAI Team](https://huggingface.co/MinervaAI) and, in particular, [Gryphe](https://huggingface.co/Gryphe) for letting us use it!
## Others
Undi: If you want to support me, you can [here](https://ko-fi.com/undiai).
IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek
<!-- original-model-card end -->
|
zrjin/icefall-asr-zipformer-multi-zh-en-2023-11-22
|
zrjin
| 2023-11-23T08:57:52Z | 0 | 0 | null |
[
"tensorboard",
"onnx",
"region:us"
] | null | 2023-11-22T08:52:54Z |
See https://github.com/k2-fsa/icefall/pull/1265
|
waldie/Karen_TheEditor_V2_CREATIVE_Mistral_7B-8bpw-h8-exl2
|
waldie
| 2023-11-23T08:52:46Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"llm",
"llama",
"spellcheck",
"grammar",
"conversational",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T08:32:46Z |
---
tags:
- llm
- llama
- spellcheck
- grammar
license: llama2
---
quant of [FPHam's](https://huggingface.co/FPHam) [Karen_TheEditor_V2_CREATIVE_Mistral_7B](https://huggingface.co/FPHam/Karen_TheEditor_V2_CREATIVE_Mistral_7B)
wikitext used as calibration dataset.
|
PriyankSisodia/bloom_7b_23nov
|
PriyankSisodia
| 2023-11-23T08:48:04Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloom-7b1",
"base_model:adapter:bigscience/bloom-7b1",
"region:us"
] | null | 2023-11-23T08:48:00Z |
---
library_name: peft
base_model: bigscience/bloom-7b1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
dsmsb/16class_all9k_promptcorr_tweet_300other_23nov23_v2_robt
|
dsmsb
| 2023-11-23T08:42:28Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T07:35:01Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 16class_all9k_promptcorr_tweet_300other_23nov23_v2_robt
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. -->
# 16class_all9k_promptcorr_tweet_300other_23nov23_v2_robt
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1210
- Accuracy: 0.9706
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.6489 | 1.0 | 721 | 0.6413 | 0.8062 |
| 0.792 | 2.0 | 1442 | 0.4073 | 0.8760 |
| 0.4619 | 3.0 | 2163 | 0.2995 | 0.9101 |
| 0.3801 | 4.0 | 2884 | 0.2219 | 0.9373 |
| 0.2681 | 5.0 | 3605 | 0.1771 | 0.9533 |
| 0.2398 | 6.0 | 4326 | 0.1524 | 0.9599 |
| 0.2007 | 7.0 | 5047 | 0.1299 | 0.9674 |
| 0.1708 | 8.0 | 5768 | 0.1210 | 0.9706 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
MayIBorn/cola-deberta_initialize_dW_A_with_svd_from_back
|
MayIBorn
| 2023-11-23T08:26:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/deberta-v2-xxlarge",
"base_model:adapter:microsoft/deberta-v2-xxlarge",
"region:us"
] | null | 2023-11-23T08:26:13Z |
---
library_name: peft
base_model: microsoft/deberta-v2-xxlarge
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
|
huatougui/my_awesome_model
|
huatougui
| 2023-11-23T08:25:17Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T07:26:11Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
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: 0.93204
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
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: 0.2539
- Accuracy: 0.9320
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2245 | 1.0 | 1563 | 0.2077 | 0.9247 |
| 0.1215 | 2.0 | 3126 | 0.2539 | 0.9320 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
StevenPerrin/ppo-Pyramids
|
StevenPerrin
| 2023-11-23T08:11:32Z | 14 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-11-23T08:07:03Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: StevenPerrin/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
S6six/stories-toon-anime
|
S6six
| 2023-11-23T08:06:00Z | 0 | 0 | null |
[
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-23T08:02:45Z |
---
language:
- en
tags:
- stable-diffusion
- text-to-image
license: creativeml-openrail-m
inference: true
---
|
dbailleul/ppo-SnowballTarget
|
dbailleul
| 2023-11-23T07:58:09Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-11-23T07:58:06Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dbailleul/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
LoneStriker/airoboros-m-7b-3.1.2-dare-0.85-4.0bpw-h6-exl2
|
LoneStriker
| 2023-11-23T07:49:45Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T07:47:20Z |
---
license: apache-2.0
---
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| uukuguy/speechless-code-mistral-orca-7b-v1.0 | 55.33 | 59.64 | 82.25 | 61.33 | 48.45 | 77.51 | 8.26 | 49.89 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| Open-Orca/Mistral-7B-SlimOrca | 53.34 | 62.54 | 83.86 | 62.77 | 54.23 | 77.43 | 21.38 | 11.2 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
| ehartford/dolphin-2.2.1-mistral-7b | 53.06 | 63.48 | 83.86 | 63.28 | 53.17 | 78.37 | 21.08 | 8.19 |
| teknium/CollectiveCognition-v1-Mistral-7B | 52.55 | 62.37 | 85.5 | 62.76 | 54.48 | 77.58 | 17.89 | 7.22 |
| HuggingFaceH4/zephyr-7b-alpha | 52.4 | 61.01 | 84.04 | 61.39 | 57.9 | 78.61 | 14.03 | 9.82 |
| ehartford/samantha-1.2-mistral-7b | 52.16 | 64.08 | 85.08 | 63.91 | 50.4 | 78.53 | 16.98 | 6.13 |
|
LoneStriker/SynthIA-7B-v1.3-dare-0.85-8.0bpw-h8-exl2
|
LoneStriker
| 2023-11-23T07:45:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T07:40:38Z |
---
license: llama2
---
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| uukuguy/speechless-code-mistral-orca-7b-v1.0 | 55.33 | 59.64 | 82.25 | 61.33 | 48.45 | 77.51 | 8.26 | 49.89 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| Open-Orca/Mistral-7B-SlimOrca | 53.34 | 62.54 | 83.86 | 62.77 | 54.23 | 77.43 | 21.38 | 11.2 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
| ehartford/dolphin-2.2.1-mistral-7b | 53.06 | 63.48 | 83.86 | 63.28 | 53.17 | 78.37 | 21.08 | 8.19 |
| teknium/CollectiveCognition-v1-Mistral-7B | 52.55 | 62.37 | 85.5 | 62.76 | 54.48 | 77.58 | 17.89 | 7.22 |
| HuggingFaceH4/zephyr-7b-alpha | 52.4 | 61.01 | 84.04 | 61.39 | 57.9 | 78.61 | 14.03 | 9.82 |
| ehartford/samantha-1.2-mistral-7b | 52.16 | 64.08 | 85.08 | 63.91 | 50.4 | 78.53 | 16.98 | 6.13 |
|
LoneStriker/SynthIA-7B-v1.3-dare-0.85-6.0bpw-h6-exl2
|
LoneStriker
| 2023-11-23T07:37:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T07:34:10Z |
---
license: llama2
---
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| uukuguy/speechless-code-mistral-orca-7b-v1.0 | 55.33 | 59.64 | 82.25 | 61.33 | 48.45 | 77.51 | 8.26 | 49.89 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| Open-Orca/Mistral-7B-SlimOrca | 53.34 | 62.54 | 83.86 | 62.77 | 54.23 | 77.43 | 21.38 | 11.2 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
| ehartford/dolphin-2.2.1-mistral-7b | 53.06 | 63.48 | 83.86 | 63.28 | 53.17 | 78.37 | 21.08 | 8.19 |
| teknium/CollectiveCognition-v1-Mistral-7B | 52.55 | 62.37 | 85.5 | 62.76 | 54.48 | 77.58 | 17.89 | 7.22 |
| HuggingFaceH4/zephyr-7b-alpha | 52.4 | 61.01 | 84.04 | 61.39 | 57.9 | 78.61 | 14.03 | 9.82 |
| ehartford/samantha-1.2-mistral-7b | 52.16 | 64.08 | 85.08 | 63.91 | 50.4 | 78.53 | 16.98 | 6.13 |
|
KuyaJay/Bweshmix
|
KuyaJay
| 2023-11-23T07:33:56Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-11-23T07:29:46Z |
---
license: creativeml-openrail-m
---
|
LoneStriker/SynthIA-7B-v1.3-dare-0.85-4.0bpw-h6-exl2
|
LoneStriker
| 2023-11-23T07:30:20Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T07:27:55Z |
---
license: llama2
---
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| uukuguy/speechless-code-mistral-orca-7b-v1.0 | 55.33 | 59.64 | 82.25 | 61.33 | 48.45 | 77.51 | 8.26 | 49.89 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| Open-Orca/Mistral-7B-SlimOrca | 53.34 | 62.54 | 83.86 | 62.77 | 54.23 | 77.43 | 21.38 | 11.2 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
| ehartford/dolphin-2.2.1-mistral-7b | 53.06 | 63.48 | 83.86 | 63.28 | 53.17 | 78.37 | 21.08 | 8.19 |
| teknium/CollectiveCognition-v1-Mistral-7B | 52.55 | 62.37 | 85.5 | 62.76 | 54.48 | 77.58 | 17.89 | 7.22 |
| HuggingFaceH4/zephyr-7b-alpha | 52.4 | 61.01 | 84.04 | 61.39 | 57.9 | 78.61 | 14.03 | 9.82 |
| ehartford/samantha-1.2-mistral-7b | 52.16 | 64.08 | 85.08 | 63.91 | 50.4 | 78.53 | 16.98 | 6.13 |
|
spayot/pnw-passerines
|
spayot
| 2023-11-23T07:28:19Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-23T07:28:12Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: pnw-passerines
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8059701323509216
---
# pnw-passerines
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### chickadee
#### junco
#### sparrow
|
WineDuck/openclip-rs5m
|
WineDuck
| 2023-11-23T07:19:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-11-23T06:58:43Z |
# openclip模型加载代码
- 首先下载本仓库中的模型
- 按照下述代码加载openclip模型
```python
model_name = "ViT-B/32"
pretrained = False
model, _, _ = open_clip.create_model_and_transforms(model_name, pretrained=pretrained)
my_ckpt = torch.load("openclip_vit_b32.pt")
model.load_state_dict(my_ckpt)
```
|
Tjooner/bart-sql
|
Tjooner
| 2023-11-23T07:16:47Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:facebook/bart-large",
"base_model:adapter:facebook/bart-large",
"region:us"
] | null | 2023-11-23T07:11:57Z |
---
library_name: peft
base_model: facebook/bart-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
PriyankSisodia/bloom_3B_test20ep_23nov
|
PriyankSisodia
| 2023-11-23T07:15:55Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:bigscience/bloom-3b",
"base_model:adapter:bigscience/bloom-3b",
"region:us"
] | null | 2023-11-23T07:15:50Z |
---
library_name: peft
base_model: bigscience/bloom-3b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
|
KMJJJJ/ppo
|
KMJJJJ
| 2023-11-23T07:15:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T07:09:50Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.12
name: mean_reward
verified: false
---
# **PPO** Agent playing **PandaReachDense-v3**
This is a trained model of a **PPO** agent playing **PandaReachDense-v3**
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
...
```
|
trng1305/layoutlmv2-sroie-test
|
trng1305
| 2023-11-23T07:06:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-23T06:35:22Z |
---
tags:
- generated_from_trainer
model-index:
- name: layoutlmv2-sroie-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv2-sroie-test
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0312
- Address: {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907}
- Company: {'precision': 0.966491458607096, 'recall': 0.9865861837692823, 'f1': 0.9764354463989379, 'number': 1491}
- Date: {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428}
- Total: {'precision': 0.8783068783068783, 'recall': 0.894878706199461, 'f1': 0.8865153538050735, 'number': 371}
- Overall Precision: 0.9792
- Overall Recall: 0.9858
- Overall F1: 0.9825
- Overall Accuracy: 0.9947
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Address | Company | Date | Total | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3558 | 1.0 | 40 | 0.0698 | {'precision': 0.9855256475368207, 'recall': 0.9933452777066804, 'f1': 0.9894200127469727, 'number': 3907} | {'precision': 0.8677685950413223, 'recall': 0.9859154929577465, 'f1': 0.9230769230769231, 'number': 1491} | {'precision': 0.8384458077709611, 'recall': 0.9579439252336449, 'f1': 0.8942202835332607, 'number': 428} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 371} | 0.9412 | 0.9296 | 0.9354 | 0.9808 |
| 0.0489 | 2.0 | 80 | 0.0374 | {'precision': 0.9917547023962896, 'recall': 0.9851548502687484, 'f1': 0.9884437596302004, 'number': 3907} | {'precision': 0.92625, 'recall': 0.993963782696177, 'f1': 0.9589129731478485, 'number': 1491} | {'precision': 0.9699074074074074, 'recall': 0.9789719626168224, 'f1': 0.9744186046511628, 'number': 428} | {'precision': 0.7655367231638418, 'recall': 0.7304582210242587, 'f1': 0.7475862068965516, 'number': 371} | 0.9607 | 0.9716 | 0.9661 | 0.9899 |
| 0.0282 | 3.0 | 120 | 0.0277 | {'precision': 0.9913198876691346, 'recall': 0.993857179421551, 'f1': 0.9925869120654397, 'number': 3907} | {'precision': 0.9633986928104575, 'recall': 0.98859825620389, 'f1': 0.9758358159549818, 'number': 1491} | {'precision': 0.9929078014184397, 'recall': 0.9813084112149533, 'f1': 0.9870740305522915, 'number': 428} | {'precision': 0.8376068376068376, 'recall': 0.7924528301886793, 'f1': 0.8144044321329641, 'number': 371} | 0.9759 | 0.9797 | 0.9778 | 0.9933 |
| 0.0194 | 4.0 | 160 | 0.0259 | {'precision': 0.990063694267516, 'recall': 0.9946250319938572, 'f1': 0.9923391215526048, 'number': 3907} | {'precision': 0.9754152823920266, 'recall': 0.9845741113346748, 'f1': 0.9799732977303072, 'number': 1491} | {'precision': 0.9929245283018868, 'recall': 0.9836448598130841, 'f1': 0.9882629107981221, 'number': 428} | {'precision': 0.8320209973753281, 'recall': 0.8544474393530997, 'f1': 0.8430851063829787, 'number': 371} | 0.9771 | 0.9831 | 0.9801 | 0.9939 |
| 0.0148 | 5.0 | 200 | 0.0259 | {'precision': 0.990316004077472, 'recall': 0.9946250319938572, 'f1': 0.9924658408887755, 'number': 3907} | {'precision': 0.9597141000649773, 'recall': 0.9906103286384976, 'f1': 0.9749174917491749, 'number': 1491} | {'precision': 0.9952941176470588, 'recall': 0.9883177570093458, 'f1': 0.9917936694021102, 'number': 428} | {'precision': 0.8621621621621621, 'recall': 0.8598382749326146, 'f1': 0.8609986504723346, 'number': 371} | 0.9756 | 0.9852 | 0.9803 | 0.9940 |
| 0.0113 | 6.0 | 240 | 0.0255 | {'precision': 0.9910714285714286, 'recall': 0.9943690811364219, 'f1': 0.9927175162897662, 'number': 3907} | {'precision': 0.9659239842726082, 'recall': 0.98859825620389, 'f1': 0.9771295989393438, 'number': 1491} | {'precision': 0.9976415094339622, 'recall': 0.9883177570093458, 'f1': 0.9929577464788731, 'number': 428} | {'precision': 0.9008746355685131, 'recall': 0.8328840970350404, 'f1': 0.8655462184873949, 'number': 371} | 0.9804 | 0.9829 | 0.9816 | 0.9944 |
| 0.0094 | 7.0 | 280 | 0.0267 | {'precision': 0.9908233494774408, 'recall': 0.9948809828512926, 'f1': 0.9928480204342274, 'number': 3907} | {'precision': 0.9627450980392157, 'recall': 0.9879275653923542, 'f1': 0.9751737835153922, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} | {'precision': 0.8787878787878788, 'recall': 0.8598382749326146, 'f1': 0.8692098092643051, 'number': 371} | 0.9777 | 0.9845 | 0.9811 | 0.9942 |
| 0.0082 | 8.0 | 320 | 0.0274 | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907} | {'precision': 0.9671268902038133, 'recall': 0.9865861837692823, 'f1': 0.9767596281540504, 'number': 1491} | {'precision': 0.9929577464788732, 'recall': 0.9883177570093458, 'f1': 0.990632318501171, 'number': 428} | {'precision': 0.8898071625344353, 'recall': 0.8706199460916442, 'f1': 0.880108991825613, 'number': 371} | 0.9798 | 0.9845 | 0.9821 | 0.9946 |
| 0.0069 | 9.0 | 360 | 0.0273 | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907} | {'precision': 0.972203838517538, 'recall': 0.9852448021462106, 'f1': 0.9786808794137242, 'number': 1491} | {'precision': 1.0, 'recall': 0.9813084112149533, 'f1': 0.9905660377358491, 'number': 428} | {'precision': 0.88, 'recall': 0.889487870619946, 'f1': 0.8847184986595173, 'number': 371} | 0.9807 | 0.9848 | 0.9828 | 0.9948 |
| 0.0055 | 10.0 | 400 | 0.0291 | {'precision': 0.9905636317266003, 'recall': 0.9941131302789864, 'f1': 0.9923352069494124, 'number': 3907} | {'precision': 0.9671268902038133, 'recall': 0.9865861837692823, 'f1': 0.9767596281540504, 'number': 1491} | {'precision': 0.9976359338061466, 'recall': 0.985981308411215, 'f1': 0.991774383078731, 'number': 428} | {'precision': 0.9025787965616046, 'recall': 0.8490566037735849, 'f1': 0.875, 'number': 371} | 0.9804 | 0.9831 | 0.9817 | 0.9944 |
| 0.0045 | 11.0 | 440 | 0.0292 | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907} | {'precision': 0.9696169088507266, 'recall': 0.9845741113346748, 'f1': 0.9770382695507488, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428} | {'precision': 0.8817204301075269, 'recall': 0.8840970350404312, 'f1': 0.882907133243607, 'number': 371} | 0.9802 | 0.9847 | 0.9825 | 0.9947 |
| 0.0042 | 12.0 | 480 | 0.0310 | {'precision': 0.9913221031138336, 'recall': 0.9941131302789864, 'f1': 0.9927156549520767, 'number': 3907} | {'precision': 0.9683794466403162, 'recall': 0.9859154929577465, 'f1': 0.9770687936191426, 'number': 1491} | {'precision': 1.0, 'recall': 0.9836448598130841, 'f1': 0.9917550058892814, 'number': 428} | {'precision': 0.8763440860215054, 'recall': 0.8787061994609164, 'f1': 0.8775235531628534, 'number': 371} | 0.9795 | 0.9845 | 0.9820 | 0.9945 |
| 0.0038 | 13.0 | 520 | 0.0316 | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907} | {'precision': 0.9652230971128609, 'recall': 0.9865861837692823, 'f1': 0.9757877280265339, 'number': 1491} | {'precision': 0.9952830188679245, 'recall': 0.985981308411215, 'f1': 0.9906103286384976, 'number': 428} | {'precision': 0.8649350649350649, 'recall': 0.8975741239892183, 'f1': 0.8809523809523809, 'number': 371} | 0.9776 | 0.9860 | 0.9818 | 0.9944 |
| 0.0035 | 14.0 | 560 | 0.0311 | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907} | {'precision': 0.9658568614576494, 'recall': 0.9865861837692823, 'f1': 0.9761114797611148, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428} | {'precision': 0.8790322580645161, 'recall': 0.8814016172506739, 'f1': 0.8802153432032301, 'number': 371} | 0.9791 | 0.9850 | 0.9821 | 0.9945 |
| 0.0032 | 15.0 | 600 | 0.0312 | {'precision': 0.9915751850906306, 'recall': 0.9941131302789864, 'f1': 0.992842535787321, 'number': 3907} | {'precision': 0.966491458607096, 'recall': 0.9865861837692823, 'f1': 0.9764354463989379, 'number': 1491} | {'precision': 1.0, 'recall': 0.985981308411215, 'f1': 0.9929411764705882, 'number': 428} | {'precision': 0.8783068783068783, 'recall': 0.894878706199461, 'f1': 0.8865153538050735, 'number': 371} | 0.9792 | 0.9858 | 0.9825 | 0.9947 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
|
thu-coai/CDial-GPT_LCCC-base
|
thu-coai
| 2023-11-23T06:57:54Z | 42 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"conversational",
"dataset:silver/lccc",
"arxiv:1901.08149",
"arxiv:2008.03946",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
license: mit
tags:
- conversational
datasets: silver/lccc
---
## Chinese pre-trained dialogue model (CDial-GPT)
This project provides a large-scale Chinese GPT model pre-trained on the dataset [LCCC](https://huggingface.co/datasets/silver/lccc).
We present a series of Chinese GPT model that are first pre-trained on a Chinese novel dataset and then post-trained on our LCCC dataset.
Similar to [TransferTransfo](https://arxiv.org/abs/1901.08149), we concatenate all dialogue histories into one context sentence, and use this sentence to predict the response. The input of our model consists of word embedding, speaker embedding, and positional embedding of each word.
Paper: [A Large-Scale Chinese Short-Text Conversation Dataset](https://arxiv.org/pdf/2008.03946.pdf)
### How to use
```python
from transformers import OpenAIGPTLMHeadModel, GPT2LMHeadModel, BertTokenizer
import torch
tokenizer = BertTokenizer.from_pretrained("thu-coai/CDial-GPT_LCCC-base")
model = OpenAIGPTLMHeadModel.from_pretrained("thu-coai/CDial-GPT_LCCC-base")
```
For more details, please refer to our [repo.](https://github.com/thu-coai/CDial-GPT) on github.
|
LoneStriker/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-8.0bpw-h8-exl2
|
LoneStriker
| 2023-11-23T06:49:46Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T06:45:17Z |
---
license: llama2
---
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| uukuguy/speechless-code-mistral-orca-7b-v1.0 | 55.33 | 59.64 | 82.25 | 61.33 | 48.45 | 77.51 | 8.26 | 49.89 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| Open-Orca/Mistral-7B-SlimOrca | 53.34 | 62.54 | 83.86 | 62.77 | 54.23 | 77.43 | 21.38 | 11.2 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
| ehartford/dolphin-2.2.1-mistral-7b | 53.06 | 63.48 | 83.86 | 63.28 | 53.17 | 78.37 | 21.08 | 8.19 |
| teknium/CollectiveCognition-v1-Mistral-7B | 52.55 | 62.37 | 85.5 | 62.76 | 54.48 | 77.58 | 17.89 | 7.22 |
| HuggingFaceH4/zephyr-7b-alpha | 52.4 | 61.01 | 84.04 | 61.39 | 57.9 | 78.61 | 14.03 | 9.82 |
| ehartford/samantha-1.2-mistral-7b | 52.16 | 64.08 | 85.08 | 63.91 | 50.4 | 78.53 | 16.98 | 6.13 |
|
qqplot23/BASE_short
|
qqplot23
| 2023-11-23T06:48:38Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-22T15:45:45Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: BASE_short
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. -->
# BASE_short
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3241
- Ppl: 28.7555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 22554
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ppl |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 4.4673 | 1.25 | 4000 | 4.2416 | 71.9939 |
| 3.8603 | 2.5 | 8000 | 3.7253 | 43.0163 |
| 3.638 | 3.75 | 12000 | 3.5322 | 35.4396 |
| 3.5229 | 5.01 | 16000 | 3.4322 | 32.0556 |
| 3.4377 | 6.26 | 20000 | 3.3749 | 30.2611 |
| 3.3972 | 7.51 | 24000 | 3.3411 | 29.2534 |
| 3.3688 | 8.76 | 28000 | 3.3241 | 28.7555 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
|
jim23/ppo-Huggy
|
jim23
| 2023-11-23T06:48:13Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-11-23T06:48:01Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: jim23/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Gurveer05/FloraBERT-small
|
Gurveer05
| 2023-11-23T06:45:57Z | 0 | 0 |
transformers
|
[
"transformers",
"biology",
"endpoints_compatible",
"region:us"
] | null | 2023-10-05T16:58:58Z |
---
metrics:
- mse
- accuracy
library_name: transformers
tags:
- biology
---
This is **roberta-base trained on DNA promoter sequences of plants and fine-tuned on gene expression values (normalized to tpm)** in 8 tissues of maize cultivars corresponding to their individual promoter sequences.
Currently, this model is trained on a subset of the total data (FloraBERT-"small"). There are 47 million parameters in this model.
References:
- [GitHub Repository](https://github.com/gurveervirk/florabert/)
- [Kaggle Dataset](https://www.kaggle.com/datasets/gsv001100/florabert-project)
|
MayIBorn/cola-_deberta_normal
|
MayIBorn
| 2023-11-23T06:43:07Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/deberta-v2-xxlarge",
"base_model:adapter:microsoft/deberta-v2-xxlarge",
"region:us"
] | null | 2023-11-23T06:42:59Z |
---
library_name: peft
base_model: microsoft/deberta-v2-xxlarge
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
|
wookiee-jk/a2c
|
wookiee-jk
| 2023-11-23T06:38:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T06:32:33Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.17 +/- 0.12
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
SebastianSchramm/Sheared-LLaMA-1.3B-sft-lora-merged-dpo-lora
|
SebastianSchramm
| 2023-11-23T06:36:47Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:SebastianSchramm/Sheared-LLaMA-1.3B-sft-lora-merged",
"base_model:finetune:SebastianSchramm/Sheared-LLaMA-1.3B-sft-lora-merged",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-21T15:24:28Z |
---
base_model: SebastianSchramm/Sheared-LLaMA-1.3B-sft-lora-merged
tags:
- generated_from_trainer
model-index:
- name: Sheared-LLaMA-1.3B-sft-lora-merged-dpo-lora
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. -->
# Sheared-LLaMA-1.3B-sft-lora-merged-dpo-lora
This model is a fine-tuned version of [SebastianSchramm/Sheared-LLaMA-1.3B-sft-lora-merged](https://huggingface.co/SebastianSchramm/Sheared-LLaMA-1.3B-sft-lora-merged) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5420
- Rewards/chosen: 0.1616
- Rewards/rejected: -0.5054
- Rewards/accuracies: 0.7235
- Rewards/margins: 0.6671
- Logps/rejected: -310.8146
- Logps/chosen: -390.9498
- Logits/rejected: 1.5247
- Logits/chosen: 1.9232
## 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: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 2905
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6903 | 0.1 | 100 | 0.6886 | 0.0199 | 0.0096 | 0.5550 | 0.0103 | -305.6642 | -392.3674 | 1.7053 | 2.1440 |
| 0.6517 | 0.21 | 200 | 0.6528 | 0.1804 | 0.0815 | 0.6450 | 0.0989 | -304.9448 | -390.7620 | 1.6923 | 2.1313 |
| 0.6324 | 0.31 | 300 | 0.6121 | 0.2511 | 0.0019 | 0.6695 | 0.2492 | -305.7412 | -390.0551 | 1.6578 | 2.0913 |
| 0.5872 | 0.41 | 400 | 0.5910 | 0.2076 | -0.1612 | 0.6845 | 0.3688 | -307.3726 | -390.4905 | 1.6514 | 2.0758 |
| 0.5861 | 0.52 | 500 | 0.5803 | 0.1840 | -0.2505 | 0.6925 | 0.4345 | -308.2649 | -390.7263 | 1.6531 | 2.0720 |
| 0.5738 | 0.62 | 600 | 0.5745 | 0.2016 | -0.2704 | 0.6890 | 0.4720 | -308.4646 | -390.5502 | 1.6548 | 2.0712 |
| 0.5638 | 0.72 | 700 | 0.5672 | 0.1923 | -0.3135 | 0.6975 | 0.5058 | -308.8952 | -390.6433 | 1.6514 | 2.0752 |
| 0.6039 | 0.83 | 800 | 0.5637 | 0.1706 | -0.3437 | 0.6995 | 0.5142 | -309.1969 | -390.8604 | 1.6458 | 2.0656 |
| 0.5981 | 0.93 | 900 | 0.5608 | 0.1821 | -0.3513 | 0.7080 | 0.5334 | -309.2732 | -390.7452 | 1.6138 | 2.0300 |
| 0.552 | 1.03 | 1000 | 0.5570 | 0.1276 | -0.4321 | 0.7095 | 0.5597 | -310.0816 | -391.2901 | 1.6076 | 2.0250 |
| 0.5507 | 1.14 | 1100 | 0.5565 | 0.1445 | -0.4121 | 0.7130 | 0.5566 | -309.8816 | -391.1214 | 1.5997 | 2.0176 |
| 0.5564 | 1.24 | 1200 | 0.5531 | 0.1479 | -0.4472 | 0.7130 | 0.5951 | -310.2328 | -391.0874 | 1.5845 | 2.0027 |
| 0.5619 | 1.34 | 1300 | 0.5532 | 0.1715 | -0.4064 | 0.7155 | 0.5779 | -309.8241 | -390.8512 | 1.5804 | 1.9939 |
| 0.5621 | 1.45 | 1400 | 0.5523 | 0.2002 | -0.3671 | 0.7150 | 0.5673 | -309.4312 | -390.5642 | 1.5934 | 2.0072 |
| 0.56 | 1.55 | 1500 | 0.5498 | 0.1680 | -0.4310 | 0.7205 | 0.5991 | -310.0704 | -390.8858 | 1.5743 | 1.9812 |
| 0.5531 | 1.65 | 1600 | 0.5469 | 0.1491 | -0.4793 | 0.7185 | 0.6284 | -310.5534 | -391.0749 | 1.5568 | 1.9653 |
| 0.5218 | 1.76 | 1700 | 0.5467 | 0.1834 | -0.4315 | 0.7205 | 0.6149 | -310.0752 | -390.7322 | 1.5731 | 1.9781 |
| 0.543 | 1.86 | 1800 | 0.5447 | 0.1430 | -0.5009 | 0.7215 | 0.6439 | -310.7689 | -391.1362 | 1.5700 | 1.9751 |
| 0.529 | 1.96 | 1900 | 0.5445 | 0.1667 | -0.4695 | 0.7215 | 0.6361 | -310.4550 | -390.8997 | 1.5571 | 1.9625 |
| 0.5312 | 2.07 | 2000 | 0.5447 | 0.1438 | -0.5149 | 0.7200 | 0.6587 | -310.9095 | -391.1281 | 1.5486 | 1.9504 |
| 0.557 | 2.17 | 2100 | 0.5443 | 0.1455 | -0.5144 | 0.7195 | 0.6600 | -310.9048 | -391.1111 | 1.5426 | 1.9432 |
| 0.5161 | 2.27 | 2200 | 0.5436 | 0.1370 | -0.5410 | 0.7210 | 0.6780 | -311.1705 | -391.1960 | 1.5366 | 1.9349 |
| 0.5543 | 2.38 | 2300 | 0.5436 | 0.2141 | -0.4398 | 0.7225 | 0.6539 | -310.1584 | -390.4249 | 1.5232 | 1.9243 |
| 0.5393 | 2.48 | 2400 | 0.5431 | 0.2152 | -0.4348 | 0.7215 | 0.6500 | -310.1086 | -390.4145 | 1.5262 | 1.9255 |
| 0.55 | 2.58 | 2500 | 0.5426 | 0.1735 | -0.4873 | 0.7255 | 0.6608 | -310.6333 | -390.8309 | 1.5277 | 1.9284 |
| 0.516 | 2.69 | 2600 | 0.5423 | 0.1701 | -0.4904 | 0.7275 | 0.6605 | -310.6644 | -390.8657 | 1.5234 | 1.9238 |
| 0.5132 | 2.79 | 2700 | 0.5420 | 0.1688 | -0.4945 | 0.7245 | 0.6632 | -310.7050 | -390.8787 | 1.5213 | 1.9202 |
| 0.5412 | 2.89 | 2800 | 0.5422 | 0.1669 | -0.4991 | 0.7220 | 0.6660 | -310.7516 | -390.8975 | 1.5223 | 1.9216 |
| 0.5387 | 3.0 | 2900 | 0.5421 | 0.1629 | -0.5025 | 0.7235 | 0.6654 | -310.7856 | -390.9372 | 1.5241 | 1.9217 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
ariiaq/a2c
|
ariiaq
| 2023-11-23T06:35:44Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T06:30:51Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.18 +/- 0.11
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
piecurus/convnext-tiny-224-finetuned-eurosat-albumentations
|
piecurus
| 2023-11-23T06:22:03Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/convnext-tiny-224",
"base_model:finetune:facebook/convnext-tiny-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-20T21:30:56Z |
---
license: apache-2.0
base_model: facebook/convnext-tiny-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: convnext-tiny-224-finetuned-eurosat-albumentations
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9622222222222222
---
<!-- 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. -->
# convnext-tiny-224-finetuned-eurosat-albumentations
This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2257
- Accuracy: 0.9622
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2446 | 1.0 | 190 | 0.2257 | 0.9622 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Anis1123/zephyr-support-chatbot
|
Anis1123
| 2023-11-23T05:47:45Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ",
"region:us"
] | null | 2023-11-22T17:13:08Z |
---
base_model: TheBloke/zephyr-7B-alpha-GPTQ
model-index:
- name: zephyr-support-chatbot
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. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
joshhu1123/DPO-llama2-no7
|
joshhu1123
| 2023-11-23T05:39:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-11-23T05:39:16Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
|
StevenPerrin/ppo-SnowballTarget
|
StevenPerrin
| 2023-11-23T05:26:44Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-11-23T05:26:36Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: StevenPerrin/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
varun3dec/sd-class-butterflies-32
|
varun3dec
| 2023-11-23T05:18:59Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-11-23T05:18:17Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('varun3dec/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
ThuyNT03/CS431_Car-COQE_CSI
|
ThuyNT03
| 2023-11-23T05:14:24Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T04:58:29Z |
---
base_model: bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: CS431_Car-COQE_CSI
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. -->
# CS431_Car-COQE_CSI
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
habanoz/phi-1_5-lr-5-3epch-airoboros3.1-1k-instruct-V1
|
habanoz
| 2023-11-23T04:44:29Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"en",
"dataset:habanoz/airoboros-3.1-no-mathjson-max-1k",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T01:21:28Z |
---
license: apache-2.0
datasets:
- habanoz/airoboros-3.1-no-mathjson-max-1k
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model: microsoft/phi-1_5
---
phi 1.5 finetune on airoboros-3.1-no-mathjson-max-1k (a subset of airoboros-3.1) using qlora.
**train metrics**
- epoch = 3.0
- train_loss = 1.1384
- train_runtime = 5:25:54.30
- train_samples_per_second = 3.065
- train_steps_per_second = 0.191
**eval metrics**
- epoch = 3.0
- eval_loss = 0.8639
- eval_runtime = 0:00:26.59
- eval_samples_per_second = 7.596
- eval_steps_per_second = 1.918
SFT code: https://github.com/habanoz/qlora.git
command:
```bash
accelerate launch $BASE_DIR/qlora/train.py \
--model_name_or_path $BASE_MODEL \
--working_dir $BASE_DIR/$OUTPUT_NAME-checkpoints \
--output_dir $BASE_DIR/$OUTPUT_NAME-peft \
--merged_output_dir $BASE_DIR/$OUTPUT_NAME \
--final_output_dir $BASE_DIR/$OUTPUT_NAME-final \
--num_train_epochs 3 \
--logging_steps 1 \
--save_strategy steps \
--save_steps 120 \
--save_total_limit 2 \
--data_seed 11422 \
--evaluation_strategy steps \
--per_device_eval_batch_size 4 \
--eval_dataset_size 0.01 \
--eval_steps 120 \
--max_new_tokens 1024 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--do_train \
--do_eval \
--lora_r 64 \
--lora_alpha 16 \
--lora_modules all \
--bits 4 \
--double_quant \
--quant_type nf4 \
--lr_scheduler_type constant \
--dataset habanoz/airoboros-3.1-no-mathjson-max-1k \
--dataset_format airoboros_chat \
--model_max_len 1024 \
--per_device_train_batch_size 1 \
--gradient_accumulation_steps 16 \
--learning_rate 1e-5 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--lora_dropout 0.0 \
--weight_decay 0.0 \
--seed 11422 \
--gradient_checkpointing False \
--use_flash_attention_2 \
--ddp_find_unused_parameters False \
--trust_remote_code True
```
|
afrideva/Tiny-Vicuna-1B-GGUF
|
afrideva
| 2023-11-23T04:35:25Z | 49,797 | 4 | null |
[
"gguf",
"ggml",
"quantized",
"q2_k",
"q3_k_m",
"q4_k_m",
"q5_k_m",
"q6_k",
"q8_0",
"text-generation",
"base_model:Jiayi-Pan/Tiny-Vicuna-1B",
"base_model:quantized:Jiayi-Pan/Tiny-Vicuna-1B",
"region:us"
] |
text-generation
| 2023-11-23T04:17:26Z |
---
base_model: Jiayi-Pan/Tiny-Vicuna-1B
inference: false
model_creator: Jiayi-Pan
model_name: Tiny-Vicuna-1B
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# Jiayi-Pan/Tiny-Vicuna-1B-GGUF
Quantized GGUF model files for [Tiny-Vicuna-1B](https://huggingface.co/Jiayi-Pan/Tiny-Vicuna-1B) from [Jiayi-Pan](https://huggingface.co/Jiayi-Pan)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [tiny-vicuna-1b.q2_k.gguf](https://huggingface.co/afrideva/Tiny-Vicuna-1B-GGUF/resolve/main/tiny-vicuna-1b.q2_k.gguf) | q2_k | 482.14 MB |
| [tiny-vicuna-1b.q3_k_m.gguf](https://huggingface.co/afrideva/Tiny-Vicuna-1B-GGUF/resolve/main/tiny-vicuna-1b.q3_k_m.gguf) | q3_k_m | 549.85 MB |
| [tiny-vicuna-1b.q4_k_m.gguf](https://huggingface.co/afrideva/Tiny-Vicuna-1B-GGUF/resolve/main/tiny-vicuna-1b.q4_k_m.gguf) | q4_k_m | 667.81 MB |
| [tiny-vicuna-1b.q5_k_m.gguf](https://huggingface.co/afrideva/Tiny-Vicuna-1B-GGUF/resolve/main/tiny-vicuna-1b.q5_k_m.gguf) | q5_k_m | 782.04 MB |
| [tiny-vicuna-1b.q6_k.gguf](https://huggingface.co/afrideva/Tiny-Vicuna-1B-GGUF/resolve/main/tiny-vicuna-1b.q6_k.gguf) | q6_k | 903.41 MB |
| [tiny-vicuna-1b.q8_0.gguf](https://huggingface.co/afrideva/Tiny-Vicuna-1B-GGUF/resolve/main/tiny-vicuna-1b.q8_0.gguf) | q8_0 | 1.17 GB |
## Original Model Card:
# Tiny Vicuna 1B
TinyLLama 1.1B finetuned with WizardVicuna dataset.
Easy to iterate on for early experiments!
|
lukekim420/rules-8bit
|
lukekim420
| 2023-11-23T04:28:25Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | null | 2023-11-23T04:28:22Z |
---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
|
Faliu/Taxi-v3
|
Faliu
| 2023-11-23T04:14:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T04:14:54Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Faliu/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Faliu/q-FrozenLake-v1-4x4-noSlippery
|
Faliu
| 2023-11-23T04:09:59Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T04:09:56Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Faliu/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
redflanker93/ppo-SnowballTarget
|
redflanker93
| 2023-11-23T04:00:00Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-11-15T20:08:28Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: redflanker93/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
typeof/neural-chat-7b-v3-1-sharded
|
typeof
| 2023-11-23T03:58:04Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-22T20:56:29Z |
---
license: apache-2.0
---
## Fine-tuning on [Habana](https://habana.ai/) Gaudi2
This model is a fine-tuned model based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the open source dataset [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca). Then we align it with DPO algorithm. For more details, you can refer our blog: [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Habana Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3).
## Model date
Neural-chat-7b-v3-1 was trained between September and October, 2023.
## Evaluation
We submit our model to [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the model performance has been **improved significantly** as we see from the average metric of 7 tasks from the leaderboard.
| Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | Winogrande (5-s) | GSM8K (5-s) | DROP (3-s) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
|[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 50.32 | 59.58 | 83.31 | 64.16 | 42.15 | 78.37 | 18.12 | 6.14 |
| [Intel/neural-chat-7b-v3](https://huggingface.co/Intel/neural-chat-7b-v3) | **57.31** | 67.15 | 83.29 | 62.26 | 58.77 | 78.06 | 1.21 | 50.43 |
| [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) | **59.06** | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-04
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-HPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2.0
### Training sample code
Here is the sample code to reproduce the model: [Sample Code](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/examples/finetuning/finetune_neuralchat_v3/README.md).
## Prompt Template
```
### System:
{system}
### User:
{usr}
### Assistant:
```
## Inference with transformers
```python
import transformers
model_name = 'Intel/neural-chat-7b-v3-1'
model = transformers.AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
def generate_response(system_input, user_input):
# Format the input using the provided template
prompt = f"### System:\n{system_input}\n### User:\n{user_input}\n### Assistant:\n"
# Tokenize and encode the prompt
inputs = tokenizer.encode(prompt, return_tensors="pt")
# Generate a response
outputs = model.generate(inputs, max_length=1000, num_return_sequences=1)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's response
return response.split("### Assistant:\n")[-1]
# Example usage
system_input = "You are a chatbot developed by Intel. Please answer all questions to the best of your ability."
user_input = "How does the neural-chat-7b-v3-1 model work?"
response = generate_response(system_input, user_input)
print(response)
```
## Ethical Considerations and Limitations
neural-chat-7b-v3-1 can produce factually incorrect output, and should not be relied on to produce factually accurate information. neural-chat-7b-v3-1 was trained on [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca) based on [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of neural-chat-7b-v3-1, developers should perform safety testing.
## Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes.
## Organizations developing the model
The NeuralChat team with members from Intel/DCAI/AISE/AIPT. Core team members: Kaokao Lv, Liang Lv, Chang Wang, Wenxin Zhang, Xuhui Ren, and Haihao Shen.
## Useful links
* Intel Neural Compressor [link](https://github.com/intel/neural-compressor)
* Intel Extension for Transformers [link](https://github.com/intel/intel-extension-for-transformers)
# [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_Intel__neural-chat-7b-v3-1)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 59.06 |
| ARC (25-shot) | 66.21 |
| HellaSwag (10-shot) | 83.64 |
| MMLU (5-shot) | 62.37 |
| TruthfulQA (0-shot) | 59.65 |
| Winogrande (5-shot) | 78.14 |
| GSM8K (5-shot) | 19.56 |
| DROP (3-shot) | 43.84 |
|
jimboHsueh/HW3_2
|
jimboHsueh
| 2023-11-23T03:48:15Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:jimboHsueh/save_hw3",
"base_model:adapter:jimboHsueh/save_hw3",
"region:us"
] | null | 2023-11-23T03:10:55Z |
---
library_name: peft
base_model: jimboHsueh/save_hw3
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
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
|
mirodil/whisper-small-uzbek
|
mirodil
| 2023-11-23T03:45:41Z | 25 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"uz",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-21T19:09:30Z |
---
language:
- uz
license: apache-2.0
base_model: openai/whisper-small
tags:
- automatic-speech-recognition
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Uzbek
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Uzbek
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5058
- Wer Ortho: 45.0514
- Wer: 34.8505
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.4898 | 0.27 | 500 | 0.5058 | 45.0514 | 34.8505 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
chris32/living-spaces-classification
|
chris32
| 2023-11-23T03:44:43Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-23T02:58:30Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: living-spaces-classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8976945281028748
---
# living-spaces-classification
House & Apartaments Classification model🤗🖼️
## Example Images
#### Exterior
#### Interior
#### bathroom
#### bedroom
#### closets
#### dining_room
#### kitchen
#### living_room
#### others
|
Dans-DiscountModels/Dans-AdventurousWinds-7b
|
Dans-DiscountModels
| 2023-11-23T03:29:26Z | 41 | 9 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:PocketDoc/Floyd-Text-Adventures",
"dataset:PocketDoc/Choose-Your-Story-Long-Text-Adventures",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-04T01:00:24Z |
---
language:
- en
datasets:
- PocketDoc/Floyd-Text-Adventures
- PocketDoc/Choose-Your-Story-Long-Text-Adventures
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
---
# What is the model for?
This model is proficient in crafting text-based adventure games. It can both concise replies and more expansive, novel-like descriptions. The ability to alternate between these two response styles can be triggered by a distinct system message.
# What's in the sausage?
This model was trained on [Mistral-7b](https://huggingface.co/mistralai/Mistral-7B-v0.1) using a custom version of the floyd and cys datasets from the skein text adventure dataset. The dataset has been improved over what was used in my previous model "Dans-CreepingSenseOfDoom" and this time presented in the more traditional adventure mode format.
## Prompt format and examples:
#### Format description:
Traditional adventure format with a description of the genre, tags, and other misc information at the top followed by an optional introductory message. User messages are prefixed with two new lines and "> ", model messages are one new line after the last user message or the description message if an introductory message is desired.
#### Examples of shorter responses:
```
[Genres: Science Fiction]
[Tags: humor, old school, sci fi]
[Mode: Adventure]
[Description: A puzzle about committing acts of financial skulduggery and exploiting ridiculous magical items.]
[Misc: Writing era: 1993]
[Intro]
It is the year 2045. You are a young man in his twenties living in New York City. Your father was an inventor who died when you were very small; your mother raised you alone for many years until she remarried. Now you live with your stepfather, but he doesn't care much for you and has never given you any money to help support yourself. You have no job and little hope of getting one because of your lack of experience. However, you do have some unusual abilities that could be put to good use if only you knew how...
> Look around
You are standing on the sidewalk outside your apartment building. The street is busy with traffic and people going about their daily business. Across the street from you stands a tall office tower where your stepfather works as an accountant. To your left is a convenience store where you can buy food and drinks.
> You go into the store
The doorbell rings as you enter the store. An elderly woman behind the counter looks up at you expectantly. "Hello," she says cheerfully, "what can I get for you?"
> You tell her you'll browse
"All right then," she replies brightly, "just take your time."
> You ask if she's hiring
"I'm sorry," she says sadly, "but we don't need anyone new just now."
> You leave without buying anything
As you walk out of the store, the bell jingles merrily.
> You go see your stepfather
When you arrive at work, your stepfather greets you warmly. He invites you into his office and offers you a seat. "So," he begins eagerly, "how would you like to make some extra cash? There's this guy named Mr. Jones who owes my company thousands of dollars. If we could somehow convince him to pay us back, it would really help our bottom line."
He pauses expectantly, waiting for your response.
> You ask him whats involved
"Well," he continues excitedly, "it turns out that Mr. Jones has a very valuable antique watch that he keeps locked away in a safe deposit box at the bank. We think that if we could somehow break into the box and steal the watch, we could sell it for enough money to cover his debt."
Your heart races as you realize the potential danger of such a scheme. But before you can say anything, your stepfather cuts off your thoughts by continuing, "Of course, there's always the chance that something could go wrong and we might end up losing everything..."
```
#### Examples of longer novel like responses:
```
[Genres: grimdark fantasy] [Tags: Fantasy, Grimdark, Serious, Villain Protagonist, War] [Description: This story is definitely more "story" than "game", however there are two distinct paths that can be followed, each with their own pitfalls and additional choices. This story can get long, and assuming you're surviving, years do pass in it and you and your environment will change.] [First message intro] [Mode: Interactive Storyteller]
You have been a soldier for as long as you remember. You don't know who you were before the war began, but you were someone else. Someone less angry. More forgiving. Perhaps even kinder.
It all started when you were very young. The King of this land was attacked by an army from another kingdom. His forces were outmatched, his city overrun. He had no choice but to flee into the forest, taking what few soldiers he could muster along with him.
As the king fled deeper into the woods, his army fell back, forming a protective wall around them. They fought valiantly against the invaders, giving the king time to escape, but they eventually succumbed to overwhelming numbers. When the fighting finally ended, only one man remained alive; you.
The king found you hiding behind some bushes, covered in blood and dirt. He took pity on you and offered you a place at court if you would swear allegiance to him. Without hesitation, you accepted. And so began your journey down the path of darkness...
> You meet with the king to discuss the plans
A week has passed since the battle and you have just met with the king to discuss future plans. It seems like things are finally starting to settle down after such chaos. However, there is still much work to be done before we can truly consider ourselves safe again.
The first order of business is rebuilding our army. We need men willing to fight for us once more. Men who understand what needs to be done and aren't afraid to die trying. I believe I have found such warriors among those loyal subjects who survived the initial attack. If we train them properly, then perhaps they might prove useful against our enemies next time around.
Next, we must find ways to improve our defenses. There are many areas where improvements could be made. For instance, we should construct better walls around our cities. These walls must not only keep intruders out, but also allow us easy access inside should anyone try to breach them. In addition, we must strengthen our gates so that no matter how strong our enemy becomes, he cannot break through without great effort. Finally, we must fortify our castles and other strategic locations throughout the country. By doing these things, we will ensure that nobody ever attacks us again!
> You ask what he desires your involvement to be
After hearing about his plans, you feel compelled to help him achieve victory. After all, you owe everything to him. But before agreeing to anything, you want to make sure that he understands exactly what you expect from him. So you decide to approach him directly.
"Your Majesty," you begin, "I am honored beyond measure to serve under your command. However, before committing myself fully to this cause, I wish to clarify certain matters."
He nods slowly. "Very well. What would you like me to explain?"
"Well," you say, "first off, I want to know why you chose me above everyone else? Why did you pick me specifically?"
He smiles softly. "Because I saw potential within you. Potential which others lacked. Potentials which could prove valuable during times of crisis. Therefore, I felt confident enough to entrust my fate to you."
"But what makes you think I possess such abilities?" you ask curiously.
"Oh, nothing special really," he replies casually. "Just raw determination combined with intelligence and resourcefulness. Those qualities alone are enough to carry us forward toward victory!"
```
# Some quick and dirty training details:
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="150" height="24"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Sequence length: 4096
- \# of epochs: 3
- Training time: 1 hour
- Hardware: 1x RTX 3090
- Training type: QLoRA
- PEFT R/A: 32/32
# Credits:
### Skein Text Adventure Data:
Thank you to the [Kobold AI](https://huggingface.co/KoboldAI) community for curating the Skein dataset, which is pivotal to this model's capabilities.
|
tuanio/w2v2_ablation_focal_ctc_a0.75_g2.0-best_on-ling_head-tp0.025_tl10_fp0.001_fl16
|
tuanio
| 2023-11-23T03:25:07Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"generated_from_trainer",
"base_model:nguyenvulebinh/wav2vec2-base-vietnamese-250h",
"base_model:finetune:nguyenvulebinh/wav2vec2-base-vietnamese-250h",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2023-11-23T01:19:15Z |
---
license: cc-by-nc-4.0
base_model: nguyenvulebinh/wav2vec2-base-vietnamese-250h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2_ablation_focal_ctc_a0.75_g2.0-best_on-ling_head-tp0.025_tl10_fp0.001_fl16
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. -->
# w2v2_ablation_focal_ctc_a0.75_g2.0-best_on-ling_head-tp0.025_tl10_fp0.001_fl16
This model is a fine-tuned version of [nguyenvulebinh/wav2vec2-base-vietnamese-250h](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vietnamese-250h) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8829
- Wer: 0.0879
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 1337.3802 | 0.94 | 100 | 875.6535 | 18.6404 |
| 928.4498 | 1.89 | 200 | 336.8592 | 17.0854 |
| 159.8141 | 2.83 | 300 | 65.9143 | 1.0 |
| 84.4352 | 3.77 | 400 | 60.3730 | 1.0 |
| 77.6086 | 4.72 | 500 | 57.3593 | 1.0 |
| 74.6091 | 5.66 | 600 | 56.1616 | 1.0 |
| 73.5983 | 6.6 | 700 | 55.2774 | 1.0 |
| 72.9967 | 7.55 | 800 | 54.6511 | 1.0 |
| 71.2266 | 8.49 | 900 | 54.5362 | 1.0 |
| 69.7741 | 9.43 | 1000 | 51.8718 | 0.9648 |
| 58.1878 | 10.38 | 1100 | 28.9001 | 0.5655 |
| 32.9238 | 11.32 | 1200 | 12.7097 | 0.2391 |
| 21.0735 | 12.26 | 1300 | 8.5885 | 0.1785 |
| 15.9281 | 13.21 | 1400 | 6.8959 | 0.1529 |
| 13.7108 | 14.15 | 1500 | 5.7514 | 0.1392 |
| 11.2293 | 15.09 | 1600 | 4.9739 | 0.1244 |
| 10.3682 | 16.04 | 1700 | 4.5084 | 0.1237 |
| 9.6654 | 16.98 | 1800 | 4.3703 | 0.1259 |
| 8.816 | 17.92 | 1900 | 4.1278 | 0.1143 |
| 8.8608 | 18.87 | 2000 | 3.9105 | 0.1074 |
| 7.8629 | 19.81 | 2100 | 3.9114 | 0.1237 |
| 7.8569 | 20.75 | 2200 | 3.7354 | 0.1121 |
| 7.3392 | 21.7 | 2300 | 3.6668 | 0.1056 |
| 7.2164 | 22.64 | 2400 | 3.5747 | 0.1128 |
| 7.2758 | 23.58 | 2500 | 3.4933 | 0.1016 |
| 6.4516 | 24.53 | 2600 | 3.4821 | 0.0988 |
| 6.45 | 25.47 | 2700 | 3.3720 | 0.0996 |
| 6.0068 | 26.42 | 2800 | 3.4425 | 0.1044 |
| 5.5781 | 27.36 | 2900 | 3.3221 | 0.1014 |
| 5.5837 | 28.3 | 3000 | 3.4974 | 0.1041 |
| 5.7895 | 29.25 | 3100 | 3.3536 | 0.0950 |
| 5.6272 | 30.19 | 3200 | 3.2036 | 0.0960 |
| 5.594 | 31.13 | 3300 | 3.1747 | 0.0913 |
| 4.791 | 32.08 | 3400 | 3.1225 | 0.1038 |
| 5.0596 | 33.02 | 3500 | 3.2113 | 0.1095 |
| 4.985 | 33.96 | 3600 | 3.0622 | 0.0929 |
| 4.731 | 34.91 | 3700 | 3.0940 | 0.0956 |
| 4.6287 | 35.85 | 3800 | 3.0453 | 0.0961 |
| 4.5235 | 36.79 | 3900 | 3.0351 | 0.1019 |
| 4.7715 | 37.74 | 4000 | 3.0237 | 0.0928 |
| 4.7101 | 38.68 | 4100 | 3.0250 | 0.0943 |
| 4.243 | 39.62 | 4200 | 2.9704 | 0.0980 |
| 4.4015 | 40.57 | 4300 | 2.9600 | 0.0871 |
| 4.4545 | 41.51 | 4400 | 2.9806 | 0.0858 |
| 4.662 | 42.45 | 4500 | 2.9668 | 0.0969 |
| 4.0696 | 43.4 | 4600 | 2.9349 | 0.0935 |
| 3.5668 | 44.34 | 4700 | 2.9190 | 0.0917 |
| 3.8214 | 45.28 | 4800 | 2.9490 | 0.0901 |
| 3.8215 | 46.23 | 4900 | 2.9371 | 0.0912 |
| 3.6593 | 47.17 | 5000 | 2.9408 | 0.0875 |
| 3.3709 | 48.11 | 5100 | 2.9577 | 0.0920 |
| 3.5768 | 49.06 | 5200 | 2.9863 | 0.0940 |
| 3.3018 | 50.0 | 5300 | 2.9437 | 0.1003 |
| 3.2921 | 50.94 | 5400 | 2.9195 | 0.0923 |
| 3.4551 | 51.89 | 5500 | 2.9410 | 0.0950 |
| 3.6576 | 52.83 | 5600 | 2.9520 | 0.1011 |
| 3.5078 | 53.77 | 5700 | 2.8926 | 0.0937 |
| 3.0777 | 54.72 | 5800 | 2.8971 | 0.0913 |
| 3.0572 | 55.66 | 5900 | 2.8693 | 0.0891 |
| 3.0486 | 56.6 | 6000 | 2.8876 | 0.0882 |
| 3.1283 | 57.55 | 6100 | 2.8597 | 0.0913 |
| 2.8705 | 58.49 | 6200 | 2.9080 | 0.0904 |
| 3.0644 | 59.43 | 6300 | 2.9106 | 0.0917 |
| 2.8822 | 60.38 | 6400 | 2.9231 | 0.0891 |
| 3.2338 | 61.32 | 6500 | 2.9511 | 0.0903 |
| 3.048 | 62.26 | 6600 | 2.9539 | 0.0898 |
| 3.094 | 63.21 | 6700 | 2.9490 | 0.0908 |
| 3.0581 | 64.15 | 6800 | 2.8952 | 0.0886 |
| 2.9343 | 65.09 | 6900 | 2.8926 | 0.0883 |
| 2.9497 | 66.04 | 7000 | 2.8732 | 0.0888 |
| 2.7788 | 66.98 | 7100 | 2.8837 | 0.0904 |
| 2.7765 | 67.92 | 7200 | 2.9169 | 0.0951 |
| 3.134 | 68.87 | 7300 | 2.9030 | 0.0926 |
| 2.8812 | 69.81 | 7400 | 2.9045 | 0.0921 |
| 2.615 | 70.75 | 7500 | 2.9148 | 0.0871 |
| 2.5678 | 71.7 | 7600 | 2.9435 | 0.0922 |
| 2.4858 | 72.64 | 7700 | 2.9050 | 0.0928 |
| 2.5367 | 73.58 | 7800 | 2.8948 | 0.0878 |
| 2.3228 | 74.53 | 7900 | 2.8995 | 0.0891 |
| 2.5849 | 75.47 | 8000 | 2.9289 | 0.0928 |
| 2.6645 | 76.42 | 8100 | 2.8950 | 0.0884 |
| 2.6634 | 77.36 | 8200 | 2.9194 | 0.0922 |
| 2.393 | 78.3 | 8300 | 2.9074 | 0.0919 |
| 3.0675 | 79.25 | 8400 | 2.8927 | 0.0908 |
| 2.6344 | 80.19 | 8500 | 2.8768 | 0.0891 |
| 2.5742 | 81.13 | 8600 | 2.8809 | 0.0911 |
| 2.6523 | 82.08 | 8700 | 2.8639 | 0.0863 |
| 2.2657 | 83.02 | 8800 | 2.8809 | 0.0912 |
| 2.3238 | 83.96 | 8900 | 2.8764 | 0.0893 |
| 2.3664 | 84.91 | 9000 | 2.8738 | 0.0913 |
| 2.5655 | 85.85 | 9100 | 2.8876 | 0.0904 |
| 2.4372 | 86.79 | 9200 | 2.9024 | 0.0910 |
| 2.5267 | 87.74 | 9300 | 2.8922 | 0.0898 |
| 2.471 | 88.68 | 9400 | 2.8893 | 0.0884 |
| 2.5225 | 89.62 | 9500 | 2.8852 | 0.0888 |
| 2.4752 | 90.57 | 9600 | 2.8876 | 0.0892 |
| 2.5029 | 91.51 | 9700 | 2.8883 | 0.0885 |
| 2.7052 | 92.45 | 9800 | 2.8825 | 0.0871 |
| 2.4682 | 93.4 | 9900 | 2.8780 | 0.0870 |
| 2.3672 | 94.34 | 10000 | 2.8810 | 0.0872 |
| 2.5325 | 95.28 | 10100 | 2.8842 | 0.0884 |
| 2.4877 | 96.23 | 10200 | 2.8833 | 0.0884 |
| 2.7373 | 97.17 | 10300 | 2.8825 | 0.0882 |
| 2.5574 | 98.11 | 10400 | 2.8833 | 0.0881 |
| 2.2097 | 99.06 | 10500 | 2.8823 | 0.0883 |
| 2.5919 | 100.0 | 10600 | 2.8829 | 0.0879 |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.14.1
|
bubuxiong/ppo-LunarLander-v2
|
bubuxiong
| 2023-11-23T03:07:05Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-23T03:06:40Z |
---
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: 239.44 +/- 15.88
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
...
```
|
imdltjwns/my_asr_model
|
imdltjwns
| 2023-11-23T03:06:48Z | 5 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-23T01:45:07Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: my_asr_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_asr_model
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 2000
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
|
phuong-tk-nguyen/swin-base-patch4-window7-224-in22k-finetuned-cifar10
|
phuong-tk-nguyen
| 2023-11-23T02:58:45Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224-in22k",
"base_model:finetune:microsoft/swin-base-patch4-window7-224-in22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-23T01:43:13Z |
---
license: apache-2.0
base_model: microsoft/swin-base-patch4-window7-224-in22k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-base-patch4-window7-224-in22k-finetuned-cifar10
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9858
---
<!-- 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. -->
# swin-base-patch4-window7-224-in22k-finetuned-cifar10
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0414
- Accuracy: 0.9858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.303 | 0.03 | 10 | 2.1672 | 0.2334 |
| 2.0158 | 0.06 | 20 | 1.6672 | 0.657 |
| 1.4855 | 0.09 | 30 | 0.8292 | 0.8704 |
| 0.7451 | 0.11 | 40 | 0.2578 | 0.93 |
| 0.5618 | 0.14 | 50 | 0.1476 | 0.962 |
| 0.4545 | 0.17 | 60 | 0.1248 | 0.9642 |
| 0.4587 | 0.2 | 70 | 0.0941 | 0.9748 |
| 0.3911 | 0.23 | 80 | 0.0944 | 0.9712 |
| 0.3839 | 0.26 | 90 | 0.0848 | 0.9756 |
| 0.3864 | 0.28 | 100 | 0.0744 | 0.978 |
| 0.3141 | 0.31 | 110 | 0.0673 | 0.98 |
| 0.3764 | 0.34 | 120 | 0.0706 | 0.9764 |
| 0.3003 | 0.37 | 130 | 0.0600 | 0.984 |
| 0.3566 | 0.4 | 140 | 0.0562 | 0.9826 |
| 0.2855 | 0.43 | 150 | 0.0567 | 0.9816 |
| 0.3351 | 0.45 | 160 | 0.0543 | 0.9828 |
| 0.2977 | 0.48 | 170 | 0.0568 | 0.9798 |
| 0.2924 | 0.51 | 180 | 0.0577 | 0.9804 |
| 0.2884 | 0.54 | 190 | 0.0551 | 0.983 |
| 0.3067 | 0.57 | 200 | 0.0487 | 0.983 |
| 0.3159 | 0.6 | 210 | 0.0513 | 0.984 |
| 0.2795 | 0.63 | 220 | 0.0460 | 0.9846 |
| 0.3113 | 0.65 | 230 | 0.0495 | 0.9832 |
| 0.2882 | 0.68 | 240 | 0.0475 | 0.9838 |
| 0.263 | 0.71 | 250 | 0.0449 | 0.9854 |
| 0.2686 | 0.74 | 260 | 0.0510 | 0.9826 |
| 0.2705 | 0.77 | 270 | 0.0483 | 0.9846 |
| 0.2807 | 0.8 | 280 | 0.0430 | 0.9854 |
| 0.2583 | 0.82 | 290 | 0.0452 | 0.9858 |
| 0.2346 | 0.85 | 300 | 0.0435 | 0.9858 |
| 0.2294 | 0.88 | 310 | 0.0434 | 0.986 |
| 0.2608 | 0.91 | 320 | 0.0433 | 0.986 |
| 0.2642 | 0.94 | 330 | 0.0425 | 0.9866 |
| 0.2781 | 0.97 | 340 | 0.0417 | 0.986 |
| 0.247 | 1.0 | 350 | 0.0414 | 0.9858 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
UuuNyaa/yazawa_nico-v1
|
UuuNyaa
| 2023-11-23T02:41:58Z | 3 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:WarriorMama777/OrangeMixs",
"base_model:adapter:WarriorMama777/OrangeMixs",
"license:cc0-1.0",
"region:us"
] |
text-to-image
| 2023-11-21T13:57:01Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
<lora:yazawa_nico-v1:1>, (yazawa nico:1.1), solo, 1girl, happy, otonokizaka
school uniform, red eyes, short twintails, pink cardigan, green striped
bowtie, long sleeves, pleated skirt, white shirt
parameters:
negative_prompt: (worst quality, low quality:1.4), (EasyNegativeV2)
output:
url: images/00417-3307402248.jpg
- text: >-
<lora:yazawa_nico-v1:1>, (yazawa nico:1.1), solo, 1girl, japanese clothes,
kimono
parameters:
negative_prompt: (worst quality, low quality:1.4), (EasyNegativeV2)
output:
url: images/00541-2337232867.jpg
- text: <lora:yazawa_nico-v1:1>, (yazawa nico:1.1), solo, 1girl, idol costume
parameters:
negative_prompt: (worst quality, low quality:1.4), (EasyNegativeV2)
output:
url: images/00569-1154389131.jpg
base_model: WarriorMama777/OrangeMixs
instance_prompt: yazawa nico
license: cc0-1.0
---
# yazawa_nico-v1
The LoRA that trained through [Yazawa Nico](https://love-live.fandom.com/wiki/Nico_Yazawa) images.
Yazawa Nico is a main character in [Love Live!](https://love-live.fandom.com/wiki/Love_Live!)
<Gallery />
## Preferred settings
Basically, the same settings as for AOM3 are recommended.
| Name | Value |
|-----------------|------------------------------------------------|
| Base model | [AOM3A1B_orangemixs.safetensors](https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/Models/AbyssOrangeMix3/AOM3A1B_orangemixs.safetensors?download=true) |
| VAE | [orangemix.vae.pt](https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt?download=true) |
| LoRA | [yazawa_nico-v1](https://huggingface.co/UuuNyaa/yazawa_nico-v1/resolve/main/yazawa_nico-v1.safetensors?download=true) |
| Sampling method | DPM++ 2M Karras |
| Sampling steps | Test: 20+, illustration: 28+ |
| Clipskip | 1 or 2 |
| CFG | 8 (6 - 12) |
| Upscaler | Latenet |
| Denoise strength | 0.6 (0.5 ~ 0.7) |
## Trigger words
You should use `yazawa nico` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/UuuNyaa/yazawa_nico-v1/tree/main) them in the Files & versions tab.
|
yinjie1949/zephyr-support-chatbot
|
yinjie1949
| 2023-11-23T02:29:46Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
] | null | 2023-11-23T01:51:39Z |
---
license: mit
base_model: TheBloke/zephyr-7B-alpha-GPTQ
tags:
- generated_from_trainer
model-index:
- name: zephyr-support-chatbot
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. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
ostapeno/flan-library-for-neo-1B_evol-test_run
|
ostapeno
| 2023-11-23T02:20:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-11-22T18:13:17Z |
Number of experts present in the library: 1
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| ostapeno/flan-library-for-neo-1B_evol=ai2_arc_ARC_Challenge_1_0_0 | EleutherAI/gpt-neo-1.3B | sordonia/flan-10k-flat/ai2_arc_ARC_Challenge_1_0_0 | lora |
Last updated on: 2023-11-23 02:17:32+00:00
|
Chat-UniVi/Chat-UniVi-ScienceQA
|
Chat-UniVi
| 2023-11-23T02:18:28Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"ChatUniVi",
"text-generation",
"arxiv:2311.08046",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-29T02:35:42Z |
---
license: llama2
---
# Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
**Paper or resources for more information:**
[[Paper](https://huggingface.co/papers/2311.08046)] [[Code](https://github.com/PKU-YuanGroup/Chat-UniVi)]
|
TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ
|
TheBloke
| 2023-11-23T02:13:02Z | 25 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:uukuguy/CollectiveCognition-v1.1-Mistral-7B-dare-0.85",
"base_model:quantized:uukuguy/CollectiveCognition-v1.1-Mistral-7B-dare-0.85",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-11-23T01:44:35Z |
---
base_model: uukuguy/CollectiveCognition-v1.1-Mistral-7B-dare-0.85
inference: false
license: llama2
model_creator: Jiangwen Su
model_name: Collectivecognition V1.1 Mistral 7B Dare 0.85
model_type: mistral
prompt_template: "User: {prompt}\nAssistant: \n"
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 -->
# Collectivecognition V1.1 Mistral 7B Dare 0.85 - GPTQ
- Model creator: [Jiangwen Su](https://huggingface.co/uukuguy)
- Original model: [Collectivecognition V1.1 Mistral 7B Dare 0.85](https://huggingface.co/uukuguy/CollectiveCognition-v1.1-Mistral-7B-dare-0.85)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Jiangwen Su's Collectivecognition V1.1 Mistral 7B Dare 0.85](https://huggingface.co/uukuguy/CollectiveCognition-v1.1-Mistral-7B-dare-0.85).
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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GGUF)
* [Jiangwen Su's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/uukuguy/CollectiveCognition-v1.1-Mistral-7B-dare-0.85)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: User-Assistant-lc
```
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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.29 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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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 `CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ`:
```shell
mkdir CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ
huggingface-cli download TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ --local-dir CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ
huggingface-cli download TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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 CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ --local-dir CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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: `CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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/CollectiveCognition-v1.1-Mistral-7B-dare-0.85-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: Jiangwen Su's Collectivecognition V1.1 Mistral 7B Dare 0.85
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| uukuguy/speechless-code-mistral-orca-7b-v1.0 | 55.33 | 59.64 | 82.25 | 61.33 | 48.45 | 77.51 | 8.26 | 49.89 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| Open-Orca/Mistral-7B-SlimOrca | 53.34 | 62.54 | 83.86 | 62.77 | 54.23 | 77.43 | 21.38 | 11.2 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
| ehartford/dolphin-2.2.1-mistral-7b | 53.06 | 63.48 | 83.86 | 63.28 | 53.17 | 78.37 | 21.08 | 8.19 |
| teknium/CollectiveCognition-v1-Mistral-7B | 52.55 | 62.37 | 85.5 | 62.76 | 54.48 | 77.58 | 17.89 | 7.22 |
| HuggingFaceH4/zephyr-7b-alpha | 52.4 | 61.01 | 84.04 | 61.39 | 57.9 | 78.61 | 14.03 | 9.82 |
| ehartford/samantha-1.2-mistral-7b | 52.16 | 64.08 | 85.08 | 63.91 | 50.4 | 78.53 | 16.98 | 6.13 |
|
gayanin/pubmed-abs-ins-con-05
|
gayanin
| 2023-11-23T02:12:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-23T00:57:49Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: pubmed-abs-ins-con-05
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. -->
# pubmed-abs-ins-con-05
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0628
## 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_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2037 | 0.11 | 500 | 0.1196 |
| 0.1558 | 0.21 | 1000 | 0.1121 |
| 0.1542 | 0.32 | 1500 | 0.0949 |
| 0.2147 | 0.43 | 2000 | 0.0913 |
| 0.0961 | 0.54 | 2500 | 0.0884 |
| 0.108 | 0.64 | 3000 | 0.0817 |
| 0.1098 | 0.75 | 3500 | 0.0798 |
| 0.1288 | 0.86 | 4000 | 0.0771 |
| 0.0962 | 0.96 | 4500 | 0.0757 |
| 0.0858 | 1.07 | 5000 | 0.0751 |
| 0.0759 | 1.18 | 5500 | 0.0749 |
| 0.0668 | 1.28 | 6000 | 0.0755 |
| 0.0792 | 1.39 | 6500 | 0.0711 |
| 0.0906 | 1.5 | 7000 | 0.0702 |
| 0.0564 | 1.61 | 7500 | 0.0703 |
| 0.0616 | 1.71 | 8000 | 0.0682 |
| 0.12 | 1.82 | 8500 | 0.0669 |
| 0.066 | 1.93 | 9000 | 0.0651 |
| 0.0569 | 2.03 | 9500 | 0.0665 |
| 0.0576 | 2.14 | 10000 | 0.0658 |
| 0.0584 | 2.25 | 10500 | 0.0662 |
| 0.044 | 2.35 | 11000 | 0.0680 |
| 0.0598 | 2.46 | 11500 | 0.0644 |
| 0.052 | 2.57 | 12000 | 0.0641 |
| 0.0589 | 2.68 | 12500 | 0.0625 |
| 0.039 | 2.78 | 13000 | 0.0638 |
| 0.0388 | 2.89 | 13500 | 0.0637 |
| 0.0598 | 3.0 | 14000 | 0.0628 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.14.1
|
gayanin/pubmed-abs-ins-con-03
|
gayanin
| 2023-11-23T02:12:55Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-23T00:58:15Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: pubmed-abs-ins-con-03
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. -->
# pubmed-abs-ins-con-03
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0608
## 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_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2122 | 0.11 | 500 | 0.1152 |
| 0.1463 | 0.21 | 1000 | 0.1051 |
| 0.1388 | 0.32 | 1500 | 0.0947 |
| 0.2079 | 0.43 | 2000 | 0.0869 |
| 0.1049 | 0.54 | 2500 | 0.0875 |
| 0.1135 | 0.64 | 3000 | 0.0802 |
| 0.1019 | 0.75 | 3500 | 0.0747 |
| 0.1079 | 0.86 | 4000 | 0.0731 |
| 0.0999 | 0.96 | 4500 | 0.0691 |
| 0.0792 | 1.07 | 5000 | 0.0723 |
| 0.0728 | 1.18 | 5500 | 0.0729 |
| 0.0802 | 1.28 | 6000 | 0.0733 |
| 0.066 | 1.39 | 6500 | 0.0683 |
| 0.0788 | 1.5 | 7000 | 0.0681 |
| 0.0656 | 1.61 | 7500 | 0.0692 |
| 0.061 | 1.71 | 8000 | 0.0670 |
| 0.1196 | 1.82 | 8500 | 0.0629 |
| 0.0687 | 1.93 | 9000 | 0.0620 |
| 0.0586 | 2.03 | 9500 | 0.0639 |
| 0.0646 | 2.14 | 10000 | 0.0645 |
| 0.0456 | 2.25 | 10500 | 0.0651 |
| 0.0502 | 2.35 | 11000 | 0.0636 |
| 0.0505 | 2.46 | 11500 | 0.0625 |
| 0.0633 | 2.57 | 12000 | 0.0603 |
| 0.0514 | 2.68 | 12500 | 0.0603 |
| 0.0421 | 2.78 | 13000 | 0.0613 |
| 0.0407 | 2.89 | 13500 | 0.0607 |
| 0.0436 | 3.0 | 14000 | 0.0608 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.14.1
|
gayanin/pubmed-abs-ins-con-04
|
gayanin
| 2023-11-23T02:11:57Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-23T00:57:32Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: pubmed-abs-ins-con-04
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. -->
# pubmed-abs-ins-con-04
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
## 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_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1838 | 0.11 | 500 | 0.1188 |
| 0.1451 | 0.21 | 1000 | 0.1138 |
| 0.1363 | 0.32 | 1500 | 0.0935 |
| 0.2084 | 0.43 | 2000 | 0.0868 |
| 0.1054 | 0.54 | 2500 | 0.0866 |
| 0.1228 | 0.64 | 3000 | 0.0789 |
| 0.0911 | 0.75 | 3500 | 0.0771 |
| 0.1134 | 0.86 | 4000 | 0.0733 |
| 0.0853 | 0.96 | 4500 | 0.0727 |
| 0.0822 | 1.07 | 5000 | 0.0734 |
| 0.0699 | 1.18 | 5500 | 0.0716 |
| 0.0767 | 1.28 | 6000 | 0.0741 |
| 0.0675 | 1.39 | 6500 | 0.0713 |
| 0.0724 | 1.5 | 7000 | 0.0693 |
| 0.0643 | 1.61 | 7500 | 0.0674 |
| 0.0614 | 1.71 | 8000 | 0.0668 |
| 0.1225 | 1.82 | 8500 | 0.0633 |
| 0.0704 | 1.93 | 9000 | 0.0623 |
| 0.055 | 2.03 | 9500 | 0.0660 |
| 0.0567 | 2.14 | 10000 | 0.0633 |
| 0.052 | 2.25 | 10500 | 0.0658 |
| 0.0459 | 2.35 | 11000 | 0.0644 |
| 0.0572 | 2.46 | 11500 | 0.0628 |
| 0.0604 | 2.57 | 12000 | 0.0615 |
| 0.0516 | 2.68 | 12500 | 0.0611 |
| 0.0424 | 2.78 | 13000 | 0.0616 |
| 0.0385 | 2.89 | 13500 | 0.0615 |
| 0.0448 | 3.0 | 14000 | 0.0614 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.14.1
|
gayanin/pubmed-abs-ins-con-02
|
gayanin
| 2023-11-23T02:10:34Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-23T00:56:33Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: pubmed-abs-ins-con-02
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. -->
# pubmed-abs-ins-con-02
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0602
## 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_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1802 | 0.11 | 500 | 0.1129 |
| 0.1436 | 0.21 | 1000 | 0.1032 |
| 0.1185 | 0.32 | 1500 | 0.0901 |
| 0.1794 | 0.43 | 2000 | 0.0878 |
| 0.1027 | 0.54 | 2500 | 0.0879 |
| 0.1001 | 0.64 | 3000 | 0.0804 |
| 0.0835 | 0.75 | 3500 | 0.0751 |
| 0.0946 | 0.86 | 4000 | 0.0744 |
| 0.0891 | 0.96 | 4500 | 0.0730 |
| 0.0657 | 1.07 | 5000 | 0.0707 |
| 0.0732 | 1.18 | 5500 | 0.0703 |
| 0.0718 | 1.28 | 6000 | 0.0739 |
| 0.0671 | 1.39 | 6500 | 0.0680 |
| 0.0806 | 1.5 | 7000 | 0.0683 |
| 0.0527 | 1.61 | 7500 | 0.0687 |
| 0.0615 | 1.71 | 8000 | 0.0663 |
| 0.1018 | 1.82 | 8500 | 0.0633 |
| 0.068 | 1.93 | 9000 | 0.0617 |
| 0.055 | 2.03 | 9500 | 0.0642 |
| 0.0594 | 2.14 | 10000 | 0.0641 |
| 0.0496 | 2.25 | 10500 | 0.0640 |
| 0.0423 | 2.35 | 11000 | 0.0635 |
| 0.0535 | 2.46 | 11500 | 0.0610 |
| 0.0582 | 2.57 | 12000 | 0.0604 |
| 0.0539 | 2.68 | 12500 | 0.0598 |
| 0.0399 | 2.78 | 13000 | 0.0607 |
| 0.0392 | 2.89 | 13500 | 0.0607 |
| 0.0467 | 3.0 | 14000 | 0.0602 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0
- Datasets 2.14.7
- Tokenizers 0.14.1
|
typeof/mistral-7b-instruct
|
typeof
| 2023-11-23T01:47:29Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"conversational",
"arxiv:2310.06825",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-23T01:33:04Z |
---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
inference:
parameters:
temperature: 0.7
---
# Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
|
yesj1234/jako_mbartLarge_100p_run1
|
yesj1234
| 2023-11-23T01:38:50Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mbart",
"text2text-generation",
"generated_from_trainer",
"ja",
"ko",
"base_model:facebook/mbart-large-50-many-to-many-mmt",
"base_model:finetune:facebook/mbart-large-50-many-to-many-mmt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-23T01:30:21Z |
---
language:
- ja
- ko
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: jako_mbartLarge_100p_run1
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. -->
# jako_mbartLarge_100p_run1
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6815
- Bleu: 57.9768
- Gen Len: 17.2796
## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:|
| 0.7241 | 1.0 | 43504 | 0.7178 | 56.112 | 17.3165 |
| 0.5523 | 2.0 | 87009 | 0.6815 | 57.9768 | 17.2796 |
| 0.4356 | 3.0 | 130513 | 0.7014 | 58.5493 | 17.1569 |
| 0.3407 | 4.0 | 174018 | 0.7513 | 58.3826 | 17.1623 |
| 0.2605 | 5.0 | 217522 | 0.8122 | 58.7238 | 17.0888 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
TheBloke/airoboros-m-7B-3.1.2-dare-0.85-AWQ
|
TheBloke
| 2023-11-23T01:33:02Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:uukuguy/airoboros-m-7b-3.1.2-dare-0.85",
"base_model:quantized:uukuguy/airoboros-m-7b-3.1.2-dare-0.85",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2023-11-23T01:15:20Z |
---
base_model: uukuguy/airoboros-m-7b-3.1.2-dare-0.85
inference: false
license: apache-2.0
model_creator: Jiangwen Su
model_name: Airoboros M 7B 3.1.2 Dare 0.85
model_type: mistral
prompt_template: '[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
'
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>
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<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 -->
# Airoboros M 7B 3.1.2 Dare 0.85 - AWQ
- Model creator: [Jiangwen Su](https://huggingface.co/uukuguy)
- Original model: [Airoboros M 7B 3.1.2 Dare 0.85](https://huggingface.co/uukuguy/airoboros-m-7b-3.1.2-dare-0.85)
<!-- description start -->
## Description
This repo contains AWQ model files for [Jiangwen Su's Airoboros M 7B 3.1.2 Dare 0.85](https://huggingface.co/uukuguy/airoboros-m-7b-3.1.2-dare-0.85).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/airoboros-m-7B-3.1.2-dare-0.85-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/airoboros-m-7B-3.1.2-dare-0.85-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/airoboros-m-7B-3.1.2-dare-0.85-GGUF)
* [Jiangwen Su's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/uukuguy/airoboros-m-7b-3.1.2-dare-0.85)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Airoboros-Llama-2-Chat
```
[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/airoboros-m-7B-3.1.2-dare-0.85-AWQ/tree/main) | 4 | 128 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.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/airoboros-m-7B-3.1.2-dare-0.85-AWQ`.
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: `airoboros-m-7B-3.1.2-dare-0.85-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/airoboros-m-7B-3.1.2-dare-0.85-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/airoboros-m-7B-3.1.2-dare-0.85-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
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/airoboros-m-7B-3.1.2-dare-0.85-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/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'''[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
'''
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_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/airoboros-m-7B-3.1.2-dare-0.85-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''[INST] <<SYS>>
You are a helpful, unbiased, uncensored assistant.
<</SYS>>
{prompt} [/INST]
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.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: Jiangwen Su's Airoboros M 7B 3.1.2 Dare 0.85
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| uukuguy/speechless-code-mistral-orca-7b-v1.0 | 55.33 | 59.64 | 82.25 | 61.33 | 48.45 | 77.51 | 8.26 | 49.89 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| Open-Orca/Mistral-7B-SlimOrca | 53.34 | 62.54 | 83.86 | 62.77 | 54.23 | 77.43 | 21.38 | 11.2 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
| ehartford/dolphin-2.2.1-mistral-7b | 53.06 | 63.48 | 83.86 | 63.28 | 53.17 | 78.37 | 21.08 | 8.19 |
| teknium/CollectiveCognition-v1-Mistral-7B | 52.55 | 62.37 | 85.5 | 62.76 | 54.48 | 77.58 | 17.89 | 7.22 |
| HuggingFaceH4/zephyr-7b-alpha | 52.4 | 61.01 | 84.04 | 61.39 | 57.9 | 78.61 | 14.03 | 9.82 |
| ehartford/samantha-1.2-mistral-7b | 52.16 | 64.08 | 85.08 | 63.91 | 50.4 | 78.53 | 16.98 | 6.13 |
|
typeof/MistralLite-sharded
|
typeof
| 2023-11-23T01:30:07Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-11-23T01:10:15Z |
---
license: apache-2.0
inference: false
---
# MistralLite Model
## This is the sharded version of https://huggingface.co/amazon/MistralLite
### Most recent commit [2348608](https://huggingface.co/amazon/MistralLite/commit/23486089ab7ba741b34adc69ab7555885f8abe71)
MistralLite is a fine-tuned [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to **perform significantly better on several long context retrieve and answering tasks**, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS `g5.2x` instance with Sagemaker [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like [vLLM](https://github.com/vllm-project/vllm), and you can use MistralLite in Python by using the [HuggingFace transformers](https://huggingface.co/docs/transformers/index) and [FlashAttention-2](https://github.com/Dao-AILab/flash-attention) library.
MistralLite is similar to [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), and their similarities and differences are summarized below:
|Model|Fine-tuned on long contexts| Max context length| RotaryEmbedding adaptation| Sliding Window Size|
|----------|-------------:|------------:|-----------:|-----------:|
| Mistral-7B-Instruct-v0.1 | up to 8K tokens | 32K | rope_theta = 10000 | 4096 |
| MistralLite | up to 16K tokens | 32K | **rope_theta = 1000000** | **16384** |
**Important - Use the prompt template below for MistralLite:**
```<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>```
## Motivation of Developing MistralLite
Since the release of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), the model became increasingly popular because its strong performance
on a wide range of benchmarks. But most of the benchmarks are evaluated on `short context`, and not much has been investigated on its performance on long context tasks.
Then We evaluated `Mistral-7B-Instruct-v0.1` against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context.
Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens,
there were some limitations on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and produced `Mistrallite`. The model managed to `significantly boost the performance of long context handling` over Mistral-7B-Instruct-v0.1. The detailed `long context evalutaion results` are as below:
1. [Topic Retrieval](https://lmsys.org/blog/2023-06-29-longchat/)
|Model Name|Input length| Input length | Input length| Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|
| | 2851| 5568 |8313 | 11044 | 13780
| Mistral-7B-Instruct-v0.1 | 100% | 50% | 2% | 0% | 0% |
| MistralLite | **100%** | **100%** | **100%** | **100%** | **98%** |
2. [Line Retrieval](https://lmsys.org/blog/2023-06-29-longchat/#longeval-results)
|Model Name|Input length| Input length | Input length| Input length| Input length|Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|-----------:|
| | 3818| 5661 |7505 | 9354 | 11188 | 12657
| Mistral-7B-Instruct-v0.1 | **98%** | 62% | 42% | 42% | 32% | 30% |
| MistralLite | **98%** | **92%** | **88%** | **76%** | **70%** | **60%** |
3. [Pass key Retrieval](https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101)
|Model Name|Input length| Input length | Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|
| | 3264| 5396 |8329 | 10197 |
| Mistral-7B-Instruct-v0.1 | **100%** | 50% | 20% | 30% |
| MistralLite | **100%** | **100%** | **100%** | **100%** |
4. [Question Answering with Long Input Texts](https://nyu-mll.github.io/quality/)
|Model Name| Test set Accuracy | Hard subset Accuracy|
|----------|-------------:|-------------:|
| Mistral-7B-Instruct-v0.1 | 44.3% | 39.7% |
| MistralLite | **64.4%** | **56.2%** |
### Example test on long context of 13400 tokens
- Context is from [Amazon Aurora FAQs](https://aws.amazon.com/rds/aurora/faqs/)
- Question: *please tell me how does pgvector help with Generative AI and give me some examples.*
- Answer from MistralLite:
```code
pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition.
You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock (limited preview) or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video.
With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, customer service next best action recommendations based on successful transcripts or chat session dialogs, and more.
```
## Model Details
- **Developed by:** [AWS Contributors](https://github.com/orgs/aws-samples/teams/aws-prototype-ml-apac)
- **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Language:** English
- **Finetuned from weights:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Finetuned on data:**
- [SLidingEncoder and Decoder (SLED)](https://huggingface.co/datasets/tau/sled)
- [(Long) Natural Questions (NQ)](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections#multi-passage-qa-from-natural-questions)
- [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- **Supported Serving Framework:**
- [Text-Generation-Inference 1.1.0](https://github.com/huggingface/text-generation-inference/tree/v1.1.0)
- [vLLM](https://github.com/vllm-project/vllm)
- [HuggingFace transformers](https://huggingface.co/docs/transformers/index)
- [HuggingFace Text Generation Inference (TGI) container on SageMaker](https://github.com/awslabs/llm-hosting-container)
- **Model License:** Apache 2.0
- **Contact:** [GitHub issues](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/issues)
- **Inference Code** [Github Repo](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/)
## MistralLite LM-Eval Results
### Methodology
- Please see https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- revision=4ececff
- Note: we used --model hf-causal-experimental instead of --model hf-causal
### Results
|Average|hellaswag| arc_challenge|truthful_qa (mc2)| MMLU (acc)|
|----------|-------------:|------------:|-----------:|-----------:|
| 0.57221 | 0.81617 | 0.58874 | 0.38275 | 0.5012 |
## How to Use MistralLite from Python Code (HuggingFace transformers) ##
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/huggingface-transformers/example_usage.ipynb).
### Install the necessary packages
Requires: [transformers](https://pypi.org/project/transformers/) 4.34.0 or later, [flash-attn](https://pypi.org/project/flash-attn/) 2.3.1.post1 or later,
and [accelerate](https://pypi.org/project/accelerate/) 0.23.0 or later.
```shell
pip install transformers==4.34.0
pip install flash-attn==2.3.1.post1 --no-build-isolation
pip install accelerate==0.23.0
```
### You can then try the following example code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch
model_id = "amazon/MistralLite"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
torch_dtype=torch.bfloat16,
use_flash_attention_2=True,
device_map="auto",)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
sequences = pipeline(
prompt,
max_new_tokens=400,
do_sample=False,
return_full_text=False,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"{seq['generated_text']}")
```
**Important** - Use the prompt template below for MistralLite:
```
<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>
```
## How to Serve MistralLite on TGI ##
**Important:**
- For an end-to-end example Jupyter notebook using the native TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi-custom/example_usage.ipynb).
### Start TGI server ###
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
docker run -d --gpus all --shm-size 1g -p 443:80 -v $(pwd)/models:/data ghcr.io/huggingface/text-generation-inference:1.1.0 \
--model-id amazon/MistralLite \
--max-input-length 16000 \
--max-total-tokens 16384 \
--max-batch-prefill-tokens 16384 \
--trust-remote-code
```
### Perform Inference ###
Example Python code for inference with TGI (requires `text_generation` 0.6.1 or later):
```shell
pip install text_generation==0.6.1
```
```python
from text_generation import Client
SERVER_PORT = 443
SERVER_HOST = "localhost"
SERVER_URL = f"{SERVER_HOST}:{SERVER_PORT}"
tgi_client = Client(f"http://{SERVER_URL}", timeout=60)
def invoke_tgi(prompt,
random_seed=1,
max_new_tokens=400,
print_stream=True,
assist_role=True):
if (assist_role):
prompt = f"<|prompter|>{prompt}</s><|assistant|>"
output = ""
for response in tgi_client.generate_stream(
prompt,
do_sample=False,
max_new_tokens=max_new_tokens,
return_full_text=False,
#temperature=None,
#truncate=None,
#seed=random_seed,
#typical_p=0.2,
):
if hasattr(response, "token"):
if not response.token.special:
snippet = response.token.text
output += snippet
if (print_stream):
print(snippet, end='', flush=True)
return output
prompt = "What are the main challenges to support a long context for LLM?"
result = invoke_tgi(prompt)
```
**Important** - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.
## How to Deploy MistralLite on Amazon SageMaker ##
**Important:**
- For an end-to-end example Jupyter notebook using the SageMaker built-in container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom docker container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi-custom/example_usage.ipynb).
### Install the necessary packages
Requires: [sagemaker](https://pypi.org/project/sagemaker/) 2.192.1 or later.
```shell
pip install sagemaker==2.192.1
```
### Deploy the Model as A SageMaker Endpoint ###
To deploy MistralLite on a SageMaker endpoint, please follow the example code as below.
```python
import sagemaker
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
import time
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
image_uri = get_huggingface_llm_image_uri(
backend="huggingface", # or lmi
region=region,
version="1.1.0"
)
model_name = "MistralLite-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
hub = {
'HF_MODEL_ID':'amazon/MistralLite',
'HF_TASK':'text-generation',
'SM_NUM_GPUS':'1',
"MAX_INPUT_LENGTH": '16000',
"MAX_TOTAL_TOKENS": '16384',
"MAX_BATCH_PREFILL_TOKENS": '16384',
"MAX_BATCH_TOTAL_TOKENS": '16384',
}
model = HuggingFaceModel(
name=model_name,
env=hub,
role=role,
image_uri=image_uri
)
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
endpoint_name=model_name,
)
```
### Perform Inference ###
To call the endpoint, please follow the example code as below:
```python
input_data = {
"inputs": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
"parameters": {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
}
result = predictor.predict(input_data)[0]["generated_text"]
print(result)
```
or via [boto3](https://pypi.org/project/boto3/), and the example code is shown as below:
```python
import boto3
import json
def call_endpoint(client, prompt, endpoint_name, paramters):
client = boto3.client("sagemaker-runtime")
payload = {"inputs": prompt,
"parameters": parameters}
response = client.invoke_endpoint(EndpointName=endpoint_name,
Body=json.dumps(payload),
ContentType="application/json")
output = json.loads(response["Body"].read().decode())
result = output[0]["generated_text"]
return result
client = boto3.client("sagemaker-runtime")
parameters = {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
endpoint_name = predictor.endpoint_name
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
result = call_endpoint(client, prompt, endpoint_name, parameters)
print(result)
```
## How to Serve MistralLite on vLLM ##
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/vllm/example_usage.ipynb).
### Using vLLM as a server ###
When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example:
```shell
python3 -m vllm.entrypoints.api_server --model amazon/MistralLite
```
### Using vLLM in Python Code ###
When using vLLM from Python code, Please see the example code as below:
```python
from vllm import LLM, SamplingParams
prompts = [
"<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
]
sampling_params = SamplingParams(temperature=0, max_tokens=100)
llm = LLM(model="amazon/MistralLite",)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
## Limitations ##
Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.
|
ftorresa/Reinforce-Pixelcopter-PLE-v0
|
ftorresa
| 2023-11-23T01:14:35Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-22T23:02:42Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 22.70 +/- 20.67
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Arya6967/SAMMed-2d
|
Arya6967
| 2023-11-23T01:12:02Z | 0 | 0 | null |
[
"arxiv:2308.16184",
"region:us"
] | null | 2023-11-23T01:06:23Z |
# SAM-Med2D \[[Paper](https://arxiv.org/abs/2308.16184)]
[](https://openxlab.org.cn/apps/detail/litianbin/SAM-Med2D)
</a>
<a src="https://img.shields.io/badge/cs.CV-2308.16184-b31b1b?logo=arxiv&logoColor=red" href="https://arxiv.org/abs/2308.16184"> <img src="https://img.shields.io/badge/cs.CV-2308.16184-b31b1b?logo=arxiv&logoColor=red">
<a src="https://img.shields.io/badge/WeChat-Group-green?logo=wechat" href="https://github.com/OpenGVLab/SAM-Med2D/blob/main/assets/SAM-Med2D_wechat_group.jpeg"> <img src="https://img.shields.io/badge/WeChat-Group-green?logo=wechat">
</a>
<a target="_blank" href="https://colab.research.google.com/github/openmedlab/SAM-Med2D/blob/main/predictor_example.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
<!-- ## Description -->
## 🌤️ Highlights
- 🏆 Collected and curated the largest medical image segmentation dataset (4.6M images and 19.7M masks) to date for training models.
- 🏆 The most comprehensive fine-tuning based on Segment Anything Model (SAM).
- 🏆 Comprehensive evaluation of SAM-Med2D on large-scale datasets.
## 🔥 Updates
- (2023.09.02) Test code release
- (2023.08.31) Pre-trained model release
- (2023.08.31) Paper release
- (2023.08.26) Online Demo release
## 👉 Dataset
SAM-Med2D is trained and tested on a dataset that includes **4.6M images** and **19.7M masks**. This dataset covers 10 medical data modalities, 4 anatomical structures + lesions, and 31 major human organs. To our knowledge, this is currently the largest and most diverse medical image segmentation dataset in terms of quantity and coverage of categories.
<p align="center"><img width="800" alt="image" src="https://github.com/openmedlab/SAM-Med2D/blob/main/assets/dataset.png"></p>
## 👉 Framework
The pipeline of SAM-Med2D. We freeze the image encoder and incorporate learnable adapter layers in each Transformer block to acquire domain-specific knowledge in the medical field. We fine-tune the prompt encoder using point, Bbox, and mask information, while updating the parameters of the mask decoder through interactive training.
<p align="center"><img width="800" alt="image" src="https://github.com/OpenGVLab/SAM-Med2D/blob/main/assets/framwork.png"></p>
## 👉 Results
<table>
<caption align="center">Quantitative comparison of different methods on the test set: </caption>
<thead>
<tr>
<th>Model</th>
<th>Resolution</th>
<th>Bbox (%)</th>
<th>1 pt (%)</th>
<th>3 pts (%)</th>
<th>5 pts (%)</th>
<th>FPS</th>
<th>Checkpoint</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">SAM</td>
<td align="center">$256\times256$</td>
<td align="center">61.63</td>
<td align="center">18.94</td>
<td align="center">28.28</td>
<td align="center">37.47</td>
<td align="center">51</td>
<td align="center"><a href="https://drive.google.com/file/d/1_U26MIJhWnWVwmI5JkGg2cd2J6MvkqU-/view?usp=drive_link">Offical</a></td>
</tr>
<tr>
<td align="center">SAM</td>
<td align="center">$1024\times1024$</td>
<td align="center">74.49</td>
<td align="center">36.88</td>
<td align="center">42.00</td>
<td align="center">47.57</td>
<td align="center">8</td>
<td align="center"><a href="https://drive.google.com/file/d/1_U26MIJhWnWVwmI5JkGg2cd2J6MvkqU-/view?usp=drive_link">Offical</a></td>
</tr>
<tr>
<td align="center">FT-SAM</td>
<td align="center">$256\times256$</td>
<td align="center">73.56</td>
<td align="center">60.11</td>
<td align="center">70.95</td>
<td align="center">75.51</td>
<td align="center">51</td>
<td align="center"><a href="https://drive.google.com/file/d/1J4qQt9MZZYdv1eoxMTJ4FL8Fz65iUFM8/view?usp=drive_link">FT-SAM</a></td>
</tr>
<tr>
<td align="center">SAM-Med2D</td>
<td align="center">$256\times256$</td>
<td align="center">79.30</td>
<td align="center">70.01</td>
<td align="center">76.35</td>
<td align="center">78.68</td>
<td align="center">35</td>
<td align="center"><a href="https://drive.google.com/file/d/1ARiB5RkSsWmAB_8mqWnwDF8ZKTtFwsjl/view?usp=drive_link">SAM-Med2D</a></td>
</tr>
</tbody>
</table>
<table>
<caption align="center">Generalization validation on 9 MICCAI2023 datasets, where "*" denotes that we drop adapter layer of SAM-Med2D in test phase: </caption>
<thead>
<tr>
<th rowspan="2">Datasets</th>
<th colspan="3">Bbox prompt (%)</th>
<th colspan="3">1 point prompt (%)</th>
</tr>
<tr>
<th>SAM</th>
<th>SAM-Med2D</th>
<th>SAM-Med2D*</th>
<th>SAM</th>
<th>SAM-Med2D</th>
<th>SAM-Med2D*</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center"><a href="https://www.synapse.org/#!Synapse:syn51236108/wiki/621615">CrossMoDA23</a></td>
<td align="center">78.98</td>
<td align="center">70.51</td>
<td align="center">84.62</td>
<td align="center">18.49</td>
<td align="center">46.08</td>
<td align="center">73.98</td>
</tr>
<tr>
<td align="center"><a href="https://kits-challenge.org/kits23/">KiTS23</a></td>
<td align="center">84.80</td>
<td align="center">76.32</td>
<td align="center">87.93</td>
<td align="center">38.93</td>
<td align="center">48.81</td>
<td align="center">79.87</td>
</tr>
<tr>
<td align="center"><a href="https://codalab.lisn.upsaclay.fr/competitions/12239#learn_the_details">FLARE23</a></td>
<td align="center">86.11</td>
<td align="center">83.51</td>
<td align="center">90.95</td>
<td align="center">51.05</td>
<td align="center">62.86</td>
<td align="center">85.10</td>
</tr>
<tr>
<td align="center"><a href="https://atlas-challenge.u-bourgogne.fr/">ATLAS2023</a></td>
<td align="center">82.98</td>
<td align="center">73.70</td>
<td align="center">86.56</td>
<td align="center">46.89</td>
<td align="center">34.72</td>
<td align="center">70.42</td>
</tr>
<tr>
<td align="center"><a href="https://multicenteraorta.grand-challenge.org/">SEG2023</a></td>
<td align="center">75.98</td>
<td align="center">68.02</td>
<td align="center">84.31</td>
<td align="center">11.75</td>
<td align="center">48.05</td>
<td align="center">69.85</td>
</tr>
<tr>
<td align="center"><a href="https://lnq2023.grand-challenge.org/lnq2023/">LNQ2023</a></td>
<td align="center">72.31</td>
<td align="center">63.84</td>
<td align="center">81.33</td>
<td align="center">3.81</td>
<td align="center">44.81</td>
<td align="center">59.84</td>
</tr>
<tr>
<td align="center"><a href="https://codalab.lisn.upsaclay.fr/competitions/9804">CAS2023</a></td>
<td align="center">52.34</td>
<td align="center">46.11</td>
<td align="center">60.38</td>
<td align="center">0.45</td>
<td align="center">28.79</td>
<td align="center">15.19</td>
</tr>
<tr>
<td align="center"><a href="https://tdsc-abus2023.grand-challenge.org/Dataset/">TDSC-ABUS2023</a></td>
<td align="center">71.66</td>
<td align="center">64.65</td>
<td align="center">76.65</td>
<td align="center">12.11</td>
<td align="center">35.99</td>
<td align="center">61.84</td>
</tr>
<tr>
<td align="center"><a href="https://toothfairy.grand-challenge.org/toothfairy/">ToothFairy2023</a></td>
<td align="center">65.86</td>
<td align="center">57.45</td>
<td align="center">75.29</td>
<td align="center">1.01</td>
<td align="center">32.12</td>
<td align="center">47.32</td>
</tr>
<tr>
<td align="center">Weighted sum</td>
<td align="center">85.35</td>
<td align="center">81.93</td>
<td align="center">90.12</td>
<td align="center">48.08</td>
<td align="center">60.31</td>
<td align="center">83.41</td>
</tr>
</tbody>
</table>
## 👉 Visualization
<p align="center"><img width="800" alt="image" src="https://github.com/openmedlab/SAM-Med2D/blob/main/assets/visualization.png"></p>
## 👉 Test
Prepare your own dataset and refer to the samples in `SAM-Med2D/data_demo` to replace them according to your specific scenario. You need to generate the "label2image_test.json" file before running "test.py"
```bash
cd ./SAM-Med2d
python test.py
```
- work_dir: Specifies the working directory for the testing process. Default value is "workdir".
- batch_size: 1.
- image_size: Default value is 256.
- boxes_prompt: Use Bbox prompt to get segmentation results.
- point_num: Specifies the number of points. Default value is 1.
- iter_point: Specifies the number of iterations for point prompts.
- sam_checkpoint: Load sam or sammed checkpoint.
- encoder_adapter: Set to True if using SAM-Med2D's pretrained weights.
- save_pred: Whether to save the prediction results.
- prompt_path: Is there a fixed Prompt file? If not, the value is None, and it will be automatically generated in the latest prediction.
## 🚀 Try SAM-Med2D
- 🏆 **Gradio Online:** Online Demo can be found on [OpenXLab](https://openxlab.org.cn/apps/detail/litianbin/SAM-Med2D).
- 🏆 **Notebook Demo:** You can use [predictor_example.ipynb](https://github.com/openmedlab/SAM-Med2D/blob/main/predictor_example.ipynb) to run it locally to view the prediction results generated by different prompts.
- 🏆 **Gradio Local:** You can deploy [app.ipynb](https://github.com/openmedlab/SAM-Med2D/blob/main/app.ipynb) locally and upload test cases.
- **Notes:** Welcome to feedback [good case👍](https://github.com/OpenGVLab/SAM-Med2D/issues/2) and [bad case👎](https://github.com/OpenGVLab/SAM-Med2D/issues/1) in issue.
## 🗓️ Ongoing
- [ ] Train code release
- [x] Test code release
- [x] Pre-trained model release
- [x] Paper release
- [x] Online Demo release
## 🎫 License
This project is released under the [Apache 2.0 license](LICENSE).
## 💬 Discussion Group
If you have any inquiries regarding SAM-Med2D, you are welcome to join our WeChat group discussion by adding the contact below:
<p align="center"><img width="300" alt="image" src="https://github.com/OpenGVLab/SAM-Med2D/blob/main/assets/SAM-Med2D_wechat_group.jpeg"></p>
## 🤝 Acknowledgement
- We thank all medical workers and dataset owners for making public datasets available to the community.
- Thanks to the open-source of the following projects: [Segment Anything](https://github.com/facebookresearch/segment-anything)  
## 👋 Hiring & Global Collaboration
- **Hiring:** We are hiring researchers, engineers, and interns in General Vision Group, Shanghai AI Lab. If you are interested in Medical Foundation Models and General Medical AI, including designing benchmark datasets, general models, evaluation systems, and efficient tools, please contact us.
- **Global Collaboration:** We're on a mission to redefine medical research, aiming for a more universally adaptable model. Our passionate team is delving into foundational healthcare models, promoting the development of the medical community. Collaborate with us to increase competitiveness, reduce risk, and expand markets.
- **Contact:** Junjun He(hejunjun@pjlab.org.cn), Jin Ye(yejin@pjlab.org.cn), and Tianbin Li (litianbin@pjlab.org.cn).
## Reference
```
@misc{cheng2023sammed2d,
title={SAM-Med2D},
author={Junlong Cheng and Jin Ye and Zhongying Deng and Jianpin Chen and Tianbin Li and Haoyu Wang and Yanzhou Su and
Ziyan Huang and Jilong Chen and Lei Jiangand Hui Sun and Junjun He and Shaoting Zhang and Min Zhu and Yu Qiao},
year={2023},
eprint={2308.16184},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
owanr/SChem5Labels-mistralai-Mistral-7B-v0.1-inter-frequency-model-cross-ent
|
owanr
| 2023-11-23T01:11:58Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-11-23T00:49:35Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-mistralai-Mistral-7B-v0.1-inter-frequency-model-cross-ent
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. -->
# SChem5Labels-mistralai-Mistral-7B-v0.1-inter-frequency-model-cross-ent
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4869
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0106 | 0.02 | 69 | 0.9779 |
| 0.7159 | 1.02 | 138 | 0.6948 |
| 0.501 | 2.02 | 207 | 0.5356 |
| 0.4159 | 3.02 | 276 | 0.4473 |
| 0.3818 | 4.02 | 345 | 0.4104 |
| 0.359 | 5.02 | 414 | 0.4004 |
| 0.3514 | 6.02 | 483 | 0.3858 |
| 0.3326 | 7.02 | 552 | 0.3819 |
| 0.3099 | 8.02 | 621 | 0.3810 |
| 0.2878 | 9.02 | 690 | 0.4035 |
| 0.2635 | 10.02 | 759 | 0.4181 |
| 0.2274 | 11.02 | 828 | 0.4483 |
| 0.1877 | 12.02 | 897 | 0.4869 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
ddh0/rocket-3B-GGUF-fp16
|
ddh0
| 2023-11-23T01:09:12Z | 1 | 0 | null |
[
"gguf",
"text-generation",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-22T22:34:54Z |
---
license: cc-by-sa-4.0
pipeline_tag: text-generation
---
This is pansophic's [rocket-3B](https://huggingface.co/pansophic/rocket-3B), converted to GGUF. No other changes were made.
Two files are avaliable here:
- rocket-3B-**fp16**.gguf: the original model converted to GGUF without quantization
- rocket-3B-**q8_0-LOT**.gguf: the original model converted to GGUF with q8_0 quantization using the `--leave-output-tensor` command-line option
From llama.cpp/quantize --help:
```
--leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing
```
The model was converted using `convert-hf-to-gguf.py` from Georgi Gerganov's llama.cpp repo, commit `#8e672ef`.
All credit belongs to [pansophic](https://huggingface.co/pansophic) for training and releasing this model. Thank you!
|
TheBloke/speechless-mistral-7B-dare-0.85-AWQ
|
TheBloke
| 2023-11-23T01:03:33Z | 10 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:speechlessai/speechless-mistral-7b-dare-0.85",
"base_model:quantized:speechlessai/speechless-mistral-7b-dare-0.85",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2023-11-23T00:46:21Z |
---
base_model: speechlessai/speechless-mistral-7b-dare-0.85
inference: false
license: llama2
model_creator: Speechless.AI
model_name: Speechless Mistral 7B Dare 0.85
model_type: mistral
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>
<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 -->
# Speechless Mistral 7B Dare 0.85 - AWQ
- Model creator: [Speechless.AI](https://huggingface.co/speechlessai)
- Original model: [Speechless Mistral 7B Dare 0.85](https://huggingface.co/speechlessai/speechless-mistral-7b-dare-0.85)
<!-- description start -->
## Description
This repo contains AWQ model files for [Speechless.AI's Speechless Mistral 7B Dare 0.85](https://huggingface.co/speechlessai/speechless-mistral-7b-dare-0.85).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/speechless-mistral-7B-dare-0.85-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/speechless-mistral-7B-dare-0.85-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/speechless-mistral-7B-dare-0.85-GGUF)
* [Speechless.AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/speechlessai/speechless-mistral-7b-dare-0.85)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/speechless-mistral-7B-dare-0.85-AWQ/tree/main) | 4 | 128 | [open-instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.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/speechless-mistral-7B-dare-0.85-AWQ`.
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: `speechless-mistral-7B-dare-0.85-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/speechless-mistral-7B-dare-0.85-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''{prompt}
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/speechless-mistral-7B-dare-0.85-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
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/speechless-mistral-7B-dare-0.85-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/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_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/speechless-mistral-7B-dare-0.85-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.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: Speechless.AI's Speechless Mistral 7B Dare 0.85
Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.
Merged with below DARE models.
weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | DROP |
| ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ | ------ |
| Intel/neural-chat-7b-v3-1 | 59.06 | 66.21 | 83.64 | 62.37 | 59.65 | 78.14 | 19.56 | 43.84 |
| migtissera/SynthIA-7B-v1.3 | 57.11 | 62.12 | 83.45 | 62.65 | 51.37 | 78.85 | 17.59 | 43.76 |
| bhenrym14/mistral-7b-platypus-fp16 | 56.89 | 63.05 | 84.15 | 64.11 | 45.07 | 78.53 | 17.36 | 45.92 |
| jondurbin/airoboros-m-7b-3.1.2 | 56.24 | 61.86 | 83.51 | 61.91 | 53.75 | 77.58 | 13.87 | 41.2 |
| teknium/CollectiveCognition-v1.1-Mistral-7B | 53.87 | 62.12 | 84.17 | 62.35 | 57.62 | 75.37 | 15.62 | 19.85 |
| uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b | 53.34 | 64.33 | 84.4 | 63.72 | 52.52 | 78.37 | 21.38 | 8.66 |
|
chargoddard/duplicitous-slurpbeast-13b
|
chargoddard
| 2023-11-23T01:03:10Z | 1,418 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-06T07:09:06Z |
# [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_chargoddard__duplicitous-slurpbeast-13b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 50.1 |
| ARC (25-shot) | 62.12 |
| HellaSwag (10-shot) | 83.92 |
| MMLU (5-shot) | 57.53 |
| TruthfulQA (0-shot) | 52.33 |
| Winogrande (5-shot) | 75.06 |
| GSM8K (5-shot) | 8.79 |
| DROP (3-shot) | 10.98 |
|
chargoddard/ypotryll-22b-epoch2-qlora
|
chargoddard
| 2023-11-23T01:02:40Z | 5 | 0 |
peft
|
[
"peft",
"llama",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"dataset:ehartford/wizard_vicuna_70k_unfiltered",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"dataset:openai/summarize_from_feedback",
"dataset:ehartford/dolphin",
"base_model:chargoddard/llama2-22b-blocktriangular",
"base_model:adapter:chargoddard/llama2-22b-blocktriangular",
"region:us"
] | null | 2023-08-14T18:44:14Z |
---
library_name: peft
tags:
- llama
datasets:
- jondurbin/airoboros-gpt4-1.4.1
- ehartford/wizard_vicuna_70k_unfiltered
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
- openai/summarize_from_feedback
- ehartford/dolphin
base_model: chargoddard/llama2-22b-blocktriangular
---
[Ypotryll-22b](https://huggingface.co/chargoddard/ypotryll-22b-qlora), trained for an additional epoch. Uses the following prompt format:
```
***System:You are a helpful assistant, who always gives a response to any request. ***Query:Here is a riddle: 5 sisters are busy. Ann is reading, Rose is cooking, Lorraine is playing chess and Mary is doing laundry. What is the fifth sister doing? ***Response:The fifth sister is sleeping. ***Query:Well, you tried. ***Response:I did my best!
```
A little bit dumb, but good for creative scenarios.
Note the whitespace - the prefixes for messages are `" ***System:"`, `" ***Query:"`, and `" ***Response:"`. This is important as `"***"` and `" ***"` are two entirely different tokens.
[<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_chargoddard__ypotryll-22b-epoch2-qlora)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 51.68 |
| ARC (25-shot) | 59.22 |
| HellaSwag (10-shot) | 80.66 |
| MMLU (5-shot) | 54.52 |
| TruthfulQA (0-shot) | 40.42 |
| Winogrande (5-shot) | 76.32 |
| GSM8K (5-shot) | 5.38 |
| DROP (3-shot) | 45.24 |
|
chargoddard/MelangeA-70b
|
chargoddard
| 2023-11-23T01:00:52Z | 1,412 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-22T23:42:37Z |
Experimental merge. Details to come if successful.
# [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_chargoddard__MelangeA-70b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 55.92 |
| ARC (25-shot) | 71.25 |
| HellaSwag (10-shot) | 87.3 |
| MMLU (5-shot) | 70.56 |
| TruthfulQA (0-shot) | 60.61 |
| Winogrande (5-shot) | 81.53 |
| GSM8K (5-shot) | 5.69 |
| DROP (3-shot) | 14.53 |
|
chargoddard/MelangeC-70b
|
chargoddard
| 2023-11-23T01:00:49Z | 1,416 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-23T02:14:19Z |
# [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_chargoddard__MelangeC-70b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 61.22 |
| ARC (25-shot) | 71.67 |
| HellaSwag (10-shot) | 87.6 |
| MMLU (5-shot) | 70.37 |
| TruthfulQA (0-shot) | 58.13 |
| Winogrande (5-shot) | 83.98 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 56.81 |
|
typeof/openchat_3.5-sharded
|
typeof
| 2023-11-23T00:54:52Z | 15 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"openchat",
"C-RLFT",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:Open-Orca/OpenOrca",
"dataset:LDJnr/LessWrong-Amplify-Instruct",
"dataset:LDJnr/Pure-Dove",
"dataset:LDJnr/Verified-Camel",
"dataset:tiedong/goat",
"dataset:glaiveai/glaive-code-assistant",
"dataset:meta-math/MetaMathQA",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-22T01:20:20Z |
---
license: apache-2.0
tags:
- openchat
- mistral
- C-RLFT
datasets:
- openchat/openchat_sharegpt4_dataset
- Open-Orca/OpenOrca
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Pure-Dove
- LDJnr/Verified-Camel
- tiedong/goat
- glaiveai/glaive-code-assistant
- meta-math/MetaMathQA
library_name: transformers
pipeline_tag: text-generation
---
## This is the sharded version of https://huggingface.co/openchat/openchat_3.5
### Most recent commit [7e65595](https://huggingface.co/openchat/openchat_3.5/commit/7e65595159eacfe6895452858e4b0ca4059ab079)
# OpenChat: Advancing Open-source Language Models with Mixed-Quality Data
<div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
</div>
|
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