modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-05 18:32:21
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 540
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-05 18:32:07
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:57Z | 46 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"OpenPipe/mistral-ft-optimized-1218",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-24T03:17:40Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- OpenPipe/mistral-ft-optimized-1218
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mistral-ft-optimized-1218-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:56Z | 65 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"argilla/notus-7b-v1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-24T02:45:54Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- argilla/notus-7b-v1
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./notus-7b-v1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:55Z | 58 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"GreenNode/GreenNode-mini-7B-v1olet",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-24T02:30:22Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- GreenNode/GreenNode-mini-7B-v1olet
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./GreenNode-mini-7B-v1olet-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:54Z | 40 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"maywell/Synatra-V0.1-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-24T01:57:07Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- maywell/Synatra-V0.1-7B-Instruct
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Synatra-V0.1-7B-Instruct-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:54Z | 45 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"euclaise/Ferret_7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-24T01:40:50Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- euclaise/Ferret_7B
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Ferret_7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:51Z | 95 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"OpenBuddy/openbuddy-zephyr-7b-v14.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-24T00:36:07Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- OpenBuddy/openbuddy-zephyr-7b-v14.1
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./openbuddy-zephyr-7b-v14.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:49Z | 75 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Keynote-Technology/KAI-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-24T00:19:59Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Keynote-Technology/KAI-7B-Instruct-v0.1
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./KAI-7B-Instruct-v0.1-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:46Z | 97 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"rvv-karma/Commonsense-QA-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-23T19:21:24Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- rvv-karma/Commonsense-QA-Mistral-7B
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Commonsense-QA-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:45Z | 42 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Ansoi/tiledfloor",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-23T18:34:20Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Ansoi/tiledfloor
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./tiledfloor-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:44Z | 61 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Sacralet/mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-23T18:15:36Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Sacralet/mistral-7B
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mistral-7B-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:44Z | 66 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"machinists/Mistral-7B-Instruct-SQL",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-23T17:50:34Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- machinists/Mistral-7B-Instruct-SQL
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Mistral-7B-Instruct-SQL-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:43Z | 52 | 2 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"arjunssat/rfp_instruct_model",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp",
"base_model:quantized:MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp",
"conversational"
] |
text-generation
| 2024-01-23T17:32:01Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- merge
- mergekit
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- arjunssat/rfp_instruct_model
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
model_name: rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF
base_model: MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp)
## Description
[MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF) contains GGUF format model files for [MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF) and below it, a specific filename to download, such as: rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF.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 MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF](https://huggingface.co/MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-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 MaziyarPanahi/rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF.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>
## 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 35 -m rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./rfp_instruct_model-Mistral-7B-Instruct-v0.2-slerp-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:41Z | 100 | 4 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"HuggingFaceH4/zephyr-7b-beta",
"pytorch",
"generated_from_trainer",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"arxiv:2305.18290",
"arxiv:2310.16944",
"base_model:mistralai/Mistral-7B-v0.1",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-23T14:57:07Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- HuggingFaceH4/zephyr-7b-beta
- pytorch
- generated_from_trainer
- en
- dataset:HuggingFaceH4/ultrachat_200k
- dataset:HuggingFaceH4/ultrafeedback_binarized
- arxiv:2305.18290
- arxiv:2310.16944
- base_model:mistralai/Mistral-7B-v0.1
- license:mit
- model-index
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF.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 [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF) zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-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 [MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF) zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF.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 35 -m zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./zephyr-7b-beta-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-26T06:34:41Z | 95 | 6 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"migtissera/Tess-XS-v1-3-yarn-128K",
"pytorch",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:migtissera/Tess-XS-v1-3-yarn-128K",
"base_model:MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-23T14:17:31Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- migtissera/Tess-XS-v1-3-yarn-128K
- pytorch
- custom_code
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
- base_model:mistralai/Mistral-7B-Instruct-v0.1
- base_model:migtissera/Tess-XS-v1-3-yarn-128K
model_name: Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [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.
* [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.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
### 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
## 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: [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download [MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-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 MaziyarPanahi/Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 35 -m Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
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. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# 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 = Llama(
model_path="./Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Tess-XS-v1-3-yarn-128K-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## 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)
|
vierlinglukas/Reinforce-1
|
vierlinglukas
| 2024-01-26T06:33:13Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T06:33:03Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
twinarta/w2v-bert-2.0-indonesian-colab-CV16.0
|
twinarta
| 2024-01-26T06:32:16Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T05:11:26Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
inniok/xlm-roberta-base-finetuned-panx-fr
|
inniok
| 2024-01-26T06:32:05Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-26T06:28:49Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: validation
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8391891891891892
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2842
- F1: 0.8392
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.549 | 1.0 | 191 | 0.3192 | 0.7917 |
| 0.2571 | 2.0 | 382 | 0.2828 | 0.8269 |
| 0.1745 | 3.0 | 573 | 0.2842 | 0.8392 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
LoneStriker/Smaugv0.1-6.0bpw-h6-exl2
|
LoneStriker
| 2024-01-26T06:30:33Z | 6 | 4 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"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
| 2024-01-26T06:19:10Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
|
wiez-man/phi-1_5_FT_code_typescript_merged
|
wiez-man
| 2024-01-26T06:29:59Z | 33 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T06:08:59Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
tinywell/q-FrozenLake-v1-4x4-noSlippery
|
tinywell
| 2024-01-26T06:18:20Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T06:18:15Z |
---
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="tinywell/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"])
```
|
coke0zero/ppo-Huggy
|
coke0zero
| 2024-01-26T06:15:04Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-01-26T06:14:58Z |
---
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: coke0zero/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jlbaker361/dcgan-wikiart100-clip
|
jlbaker361
| 2024-01-26T06:14:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-25T16:30:02Z |
---
{}
---
Creative Adversarial Network
epochs: 50
dataset jlbaker361/wikiart-balanced100
n classes 27
batch_size 4
images where resized to 512
and then center cropped to: 512
used clip=True
discriminator parameters:
init_dim: 32
final_dim 512
generator parameters:
input noise_dim: 100
|
nm-testing/TinyLlama-1.1B-intermediate-step-1431k-3T-gsms8k-pruned50-quant-ds
|
nm-testing
| 2024-01-26T06:12:34Z | 2 | 0 |
transformers
|
[
"transformers",
"onnx",
"llama",
"text-generation",
"deepsparse",
"conversational",
"arxiv:2301.00774",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"base_model:quantized:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-26T03:30:35Z |
---
base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
inference: false
model_type: llama
prompt_template: |
Question
{prompt}\n
Answer:
quantized_by: mwitiderrick
tags:
- deepsparse
---
## TinyLlama-1.1B-intermediate-step-1431k-3T-gsms8k-pruned50-quant-ds
This repo contains model files for [TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) optimized for [DeepSparse](https://github.com/neuralmagic/deepsparse), a CPU inference runtime for sparse models.
This model was quantized and pruned with [SparseGPT](https://arxiv.org/abs/2301.00774), using [SparseML](https://github.com/neuralmagic/sparseml).
## Inference
Install [DeepSparse LLM](https://github.com/neuralmagic/deepsparse) for fast inference on CPUs:
```bash
pip install deepsparse-nightly[llm]
```
Run in a [Python pipeline](https://github.com/neuralmagic/deepsparse/blob/main/docs/llms/text-generation-pipeline.md):
```python
from deepsparse import TextGeneration
prompt = "James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week?"
formatted_prompt = f"Question:{prompt}\nAnswer:"
model = TextGeneration(model_path="hf:nm-testing/TinyLlama-1.1B-intermediate-step-1431k-3T-gsms8k-pruned50-quant-ds")
print(model(formatted_prompt, max_new_tokens=200).generations[0].text)
"""
He runs 3*60=<<3*60=180>>180 meters a week
So he runs 180/3=<<180/3=60>>60 times a week
#### 60
"""
```
To obtain the final model the following process was followed:
- Fine-tune the base model on GSM8K for 2 epochs using HF SFTrainer
- Sparsify the model to 50% using SparseML
- Fine-tune the sparse model on the GSM8K dataset
- Perform one-shot quantization of the resulting model
|
EzraWilliam/wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod1
|
EzraWilliam
| 2024-01-26T06:07:17Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:xtreme_s",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-25T16:10:11Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- wer
model-index:
- name: wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: xtreme_s
type: xtreme_s
config: fleurs.id_id
split: test
args: fleurs.id_id
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- 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. -->
# wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9538
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 4.5081 | 15.38 | 100 | 2.9538 | 1.0 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
paisanx/Reinforce-Pixelcopter-PLE-v4
|
paisanx
| 2024-01-26T06:06:11Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T06:05:59Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 140.50 +/- 144.81
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
|
LoneStriker/Smaugv0.1-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-26T06:00:32Z | 6 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"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
| 2024-01-26T05:52:43Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
|
csukuangfj/icefall_asr_tal-csasr_pruned_transducer_stateless5
|
csukuangfj
| 2024-01-26T05:54:57Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2024-01-26T04:44:37Z |
Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/428
# Pre-trained Transducer-Stateless5 models for the TAL_CSASR dataset with icefall.
The model was trained on the far data of [TAL_CSASR](https://ai.100tal.com/dataset) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/tal_csasr/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5"
./pruned_transducer_stateless5/train.py \
--world-size 6 \
--num-epochs 30 \
--start-epoch 1 \
--exp-dir pruned_transducer_stateless5/exp \
--lang-dir data/lang_char \
--max-duration 90
```
## Evaluation results
The decoding results (CER%) on TAL_CSASR(dev and test) are listed below:
|decoding-method | epoch(iter) | avg | dev | test |
|--|--|--|--|--|
|greedy_search | 30 | 24 | 7.49 | 7.58|
|modified_beam_search | 30 | 24 | 7.33 | 7.38|
|fast_beam_search | 30 | 24 | 7.32 | 7.42|
|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 7.39|
|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 7.22|
|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 7.27|
|greedy_search | 348000 | 30 | 7.46 | 7.54|
|modified_beam_search | 348000 | 30 | 7.24 | 7.36|
|fast_beam_search | 348000 | 30 | 7.25 | 7.39 |
The results (CER(%) and WER(%)) for Chinese CER and English WER respectivly (zh: Chinese, en: English):
|decoding-method | epoch(iter) | avg | dev | dev_zh | dev_en | test | test_zh | test_en |
|--|--|--|--|--|--|--|--|--|
|greedy_search(use-averaged-model=True) | 30 | 24 | 7.30 | 6.48 | 19.19 |7.39| 6.66 | 19.13|
|modified_beam_search(use-averaged-model=True) | 30 | 24 | 7.15 | 6.35 | 18.95 | 7.22| 6.50 | 18.70 |
|fast_beam_search(use-averaged-model=True) | 30 | 24 | 7.18 | 6.39| 18.90 | 7.27| 6.55 | 18.77|
|
LoneStriker/Smaugv0.1-3.0bpw-h6-exl2
|
LoneStriker
| 2024-01-26T05:52:41Z | 7 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"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
| 2024-01-26T05:46:01Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
---
This model is a finetune of jondurbin's excellent [bagel](https://huggingface.co/jondurbin/bagel-34b-v0.2) model.
It has been trained with new datasets and a new technique, which we will share to the community soon.
|
Wowso/SOLAR-10.7B-dpo-v1-awq
|
Wowso
| 2024-01-26T05:33:34Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-01-26T05:15:00Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
bhaskars113/beer-model
|
bhaskars113
| 2024-01-26T05:27:37Z | 49 | 1 |
setfit
|
[
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
] |
text-classification
| 2024-01-26T05:26:56Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
metrics:
- accuracy
widget:
- text: There’s been an early morning skate that’s been going on for years. They have
a Facebook page. I think its “6:30 am morning drop-in skate at lakeview” might
be on a break right now, but would be a good way to find some other drop in skates
or a way into some of the beer leagues. Mountain biking is a great year around
activity, and a good way to meet people. Stop into any of the shops and ask about
group rides.
- text: So despite the minor audience, i think they can be quite damaging. Especially
after the burning of the tents. It's only the beginning (not that I am expecting
some form of January 6 style thing), they have been brewing for a while and have
now found an outlet.
- text: My worry is that pretty much my entire social life is spent at a bar of some
sort, especially in the winter. So this month might suck. But I'm going to go
to my golf course's bar after work today with my 6pk of Craft non-alcoholic beer
so we'll see how that goes! Best of luck to you as well!
- text: '@GayChemist @TylerDinucci It''s Wisconsin. I assume that "drinking fountains"
mean beer there.'
- text: It's great! Spent some time at the brewery and got it there. Craft beer and
hot sauces are probably my two biggest vices lol.
pipeline_tag: text-classification
inference: true
base_model: sentence-transformers/paraphrase-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.0
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 6 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 3 | <ul><li>"I can relate to a lot of what you've described here. I call myself a beer snob because I enjoy trying new craft beers but won't usually go for any thing else, and I know what I like when it comes to beer. I'm currently on a hiatus because while I was doing well at having less for a couple of weeks, last weekend I went for 8/day two weekend days in a row. My doctor told me recently that 3 is a healthy limit, and I more than doubled that on those days so ... When it becomes a problem for you; When you're too sick the next day to do anything, etc, that's what eventually made me decide I want to shift my focus from drinking beer to everything else in life. It was fun while it lasted, may not be over for me entirely I just don't like where I was headed. Hope this helps."</li><li>'I’ve been trying out NA beer. They’re significantly lower calories and, well, non-alcoholic. It scratches the itch for me.'</li><li>"Yeah, I weigh myself daily and definitely notice how much even a couple hundred extra calories can stall my weight loss. It's been helpful in motivating myself: while a couple of beers after work doesn't seem like much, it's not worth it when I hop on the scale the next few days. Being strict also helped me choose healthier options. If I know a slice of cake has the same calories as a high-protein wrap but is more likely to make me feel tired and hungry later, it naturally becomes a lot less appealing."</li></ul> |
| 1 | <ul><li>"These things happen sometimes, and that's what rescue meds are for. Today is the day I try to get out of the house and meet up with friends at a bar. Part of why I'm not drinking right now is because I'd skip going out and just drink beers at home. I want to socialize more. I'm a bit hesitant to go today though."</li><li>"Beer and boardgames in Heidelberg. It's an Event you can find on Meetup. Just 20 to 40 mostly international people meeting to play boardgames. No drinking beer needed, I drink apple juice. There is also a boardgame group meeting at the kolosseum in Heidelberg Thursdays at 17 uhr Hope you like those options"</li><li>'I go to a lot of motorcycle meetups, they are often at bars or breweries. My usual m.o. is to have one regular beer, and then switch over to Athletic Brews, a Non alcoholic beer. I like the taste, and it’s nice to be able to chill and drink with the other guys. I just really don’t want to risk riding home after having more than one alcoholic beer.'</li></ul> |
| 4 | <ul><li>'Like the title says, what are your ways of keeping track of bourbons and other spirits you’ve tried over the years? When it comes to beer I’ve used the app untappd to rate and discover new craft beers in the past and I’ve really enjoyed using something like that.'</li><li>'Enjoy it. S-189 is a great yeast. I would not call it a Cold IPA. I would just call it a tasty noble-dry-hopped pilsner. Prost! Click to expand...'</li><li>"I've got some super high gravity homebrews that are over 20 years old - some over 25. They've been kept properly. Last one I cracked was a few years ago (over 20 years old - an imperial stout with elderberries from my sister's farm) and it was sublime. I wouldn't bother with commercial American light lagers that are over six months old (they are so underhopped anyway and those hop oils deteriorate), but whatever floats your goat."</li></ul> |
| 2 | <ul><li>"Taking one beer or glass of wine or so helps me relax and calm my thoughts. And it's tasty."</li><li>"Every person I know who works night shift has a drink or two after their shift just to fall asleep lol it might be a common thing. If it's in moderation and it's not ruining your health or interfering with your life, I'd say there's nothing wrong with having a beer or two to relax after work. After all you worked for it."</li><li>'Yes rdr2 is a really good game if you just want to relax, i come back from work sometimes order 2 beers, a burger, and fries and just enjoy the good music, scenery, and story'</li></ul> |
| 5 | <ul><li>'Having a beer at a wine farm, ah bo gintsa bona will never leave me ????'</li><li>"As a java lover, I determined to locate out which quickly meals chain in Canada made the fine coffee... and the reply certainly took me via surprise. To get an concept of the great espresso in Canada, I tried the brew from three of the most famous espresso chains in the country. I stored it easy by means of sampling a small black espresso from Starbucks, Tim Hortons and McDonald's to discover out which one reigns supreme."</li><li>"Despite it being only 92 with 35% humidity, a notably comfortable summer evening, my beer is gone and I'd better go cook up some supper . Y'all have fun!"</li></ul> |
| 0 | <ul><li>'It is 6:00 am. I’m preparing to head to the Muni with my father to celebrate the lives of my lifelong Browns fan uncle and grandfather who both passed away last week. Beers will be had, good times will be celebrated, and the Browns will kick ass. It is written. IT IS GAMEDAY MY DUDES.'</li><li>'I live in the "boonies" and beer is $14+ a can at any sporting event or concert I go to. Its wild.'</li><li>'Love it everytime! Best wings ever in my opinion especially the Carolina sweet & tangy. Bar quesadillas are to die for , lots of beer options for beer lovers like myself. & ALWAYS has the games on that I want to watch whether it’s basketball , baseball or football . My go to bar on the island .'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.0 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("bhaskars113/beer-model")
# Run inference
preds = model("@GayChemist @TylerDinucci It's Wisconsin. I assume that \"drinking fountains\" mean beer there.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 60.6875 | 260 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 16 |
| 1 | 16 |
| 2 | 16 |
| 3 | 16 |
| 4 | 16 |
| 5 | 16 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0042 | 1 | 0.2324 | - |
| 0.2083 | 50 | 0.1497 | - |
| 0.4167 | 100 | 0.0141 | - |
| 0.625 | 150 | 0.002 | - |
| 0.8333 | 200 | 0.0013 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.2.2
- Transformers: 4.35.2
- PyTorch: 2.1.0+cu121
- Datasets: 2.16.1
- Tokenizers: 0.15.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
tiagoblima/mt5_base-qg-aap-oficial
|
tiagoblima
| 2024-01-26T05:20:37Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:tiagoblima/preprocessed-du-qg-squadv1_pt",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-26T03:39:46Z |
---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
datasets:
- tiagoblima/preprocessed-du-qg-squadv1_pt
model-index:
- name: mt5_base-qg-aap-oficial
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. -->
# mt5_base-qg-aap-oficial
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the tiagoblima/preprocessed-du-qg-squadv1_pt dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0870
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7144 | 1.0 | 1386 | 1.3310 |
| 1.5556 | 2.0 | 2772 | 1.2120 |
| 1.4427 | 3.0 | 4158 | 1.1406 |
| 1.3878 | 4.0 | 5544 | 1.0971 |
| 1.3626 | 5.0 | 6930 | 1.0870 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.1
|
YouKnwMe/Direct-sm-private-e1
|
YouKnwMe
| 2024-01-26T05:00:43Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-26T05:00:00Z |
---
license: cc-by-nc-4.0
---
Description to load and test will be added soon. More details on training and data will be added aswell.
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
TBD
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
dlibf/zephyr-7b-dpo-full_sft3epoch
|
dlibf
| 2024-01-26T04:54:18Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T02:23:44Z |
---
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: zephyr-7b-dpo-full_sft3epoch
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-dpo-full_sft3epoch
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6172
- Rewards/chosen: -1.5792
- Rewards/rejected: -1.8655
- Rewards/accuracies: 0.625
- Rewards/margins: 0.2863
- Logps/rejected: -1146.1112
- Logps/chosen: -1218.1312
- Logits/rejected: -3.6422
- Logits/chosen: -3.6317
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6601 | 0.21 | 100 | 0.6572 | -0.5696 | -0.7082 | 0.6133 | 0.1387 | -1030.3876 | -1117.1681 | -3.8281 | -3.8175 |
| 0.6329 | 0.42 | 200 | 0.6378 | -1.1629 | -1.3547 | 0.6523 | 0.1918 | -1095.0327 | -1176.4983 | -3.7205 | -3.7123 |
| 0.6251 | 0.63 | 300 | 0.6219 | -1.5227 | -1.7758 | 0.6484 | 0.2530 | -1137.1422 | -1212.4856 | -3.6798 | -3.6707 |
| 0.6163 | 0.84 | 400 | 0.6192 | -1.4583 | -1.7357 | 0.6289 | 0.2774 | -1133.1334 | -1206.0380 | -3.6473 | -3.6358 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1
|
YingJie0202/llama2-qlora-finetunined-french
|
YingJie0202
| 2024-01-26T04:40:06Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-25T11:49:51Z |
---
library_name: peft
base_model: NousResearch/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
liudoujiang/hhhhh
|
liudoujiang
| 2024-01-26T04:38:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T04:36:28Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: hhhhh
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
---
|
e22vvb/EN_mt5-base_5_wikiSQL
|
e22vvb
| 2024-01-26T04:31:56Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:wikisql",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-22T17:50:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikisql
model-index:
- name: EN_mt5-base_5_wikiSQL
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. -->
# EN_mt5-base_5_wikiSQL
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the wikisql dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0907
- Rouge2 Precision: 0.8556
- Rouge2 Recall: 0.7785
- Rouge2 Fmeasure: 0.8095
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
|:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
| 0.156 | 1.0 | 4049 | 0.1163 | 0.8282 | 0.7534 | 0.7831 |
| 0.1218 | 2.0 | 8098 | 0.1007 | 0.8452 | 0.7679 | 0.7989 |
| 0.1056 | 3.0 | 12147 | 0.0944 | 0.8521 | 0.7749 | 0.8058 |
| 0.0967 | 4.0 | 16196 | 0.0921 | 0.8552 | 0.7784 | 0.8092 |
| 0.0935 | 5.0 | 20245 | 0.0907 | 0.8556 | 0.7785 | 0.8095 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.7.dev0
- Tokenizers 0.13.3
|
royallab/Buttercup-4x7B-exl2
|
royallab
| 2024-01-26T04:25:54Z | 1 | 1 | null |
[
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-01-25T23:03:38Z |
---
license: apache-2.0
language:
- en
---
## Information
This is a Exl2 quantized version of [Buttercup-4x7B-bf16](https://huggingface.co/Kquant03/Buttercup-4x7B-bf16)
Please refer to the original creator for more information.
Calibration dataset: Exllamav2 default
## Branches:
- main: Measurement files
- 4bpw: 4 bits per weight
- 5bpw: 5 bits per weight
- 6bpw: 6 bits per weight
## Notes
- 6bpw is recommended for the best quality to vram usage ratio (assuming you have enough vram).
- Please ask for more bpws in the community tab if necessary.
## Run in TabbyAPI
TabbyAPI is a pure exllamav2 FastAPI server developed by us. You can find TabbyAPI's source code here: [https://github.com/theroyallab/TabbyAPI](https://github.com/theroyallab/TabbyAPI)
If you don't have huggingface-cli, please run `pip install huggingface_hub`.
To run this model, follow these steps:
1. Make a directory inside your models folder called `Buttercup-4x7B-bf16-exl2`
2. Open a terminal inside your models folder
3. Run `huggingface-cli download royallab/Buttercup-4x7B-bf16-exl2 --revision 6.0bpw-h6 --local-dir Buttercup-4x7B-bf16-exl2 --local-dir-use-symlinks False`
1. The `--revision` flag corresponds to the branch name on the model repo. Please select the appropriate bpw branch for your system.
4. Inside TabbyAPI's config.yml, set `model_name` to `Buttercup-4x7B-bf16-exl2` or you can use the `/model/load` endpoint after launching.
5. Launch TabbyAPI inside your python env by running `python main.py`
## Donate?
All my infrastructure and cloud expenses are paid out of pocket. If you'd like to donate, you can do so here: https://ko-fi.com/doctorshotgun
You should not feel obligated to donate, but if you do, I'd appreciate it.
---
|
LoneStriker/Snorkel-Mistral-PairRM-DPO-6.0bpw-h6-exl2
|
LoneStriker
| 2024-01-26T04:11:02Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"dataset:snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset",
"arxiv:2305.18290",
"arxiv:2306.02561",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T19:38:56Z |
---
license: apache-2.0
datasets:
- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
pipeline_tag: text-generation
---
We offer a temporary HF space for everyone to try out the model: -> [**Snorkel-Mistral-PairRM-DPO Space**](https://huggingface.co/spaces/snorkelai/snorkelai_mistral_pairrm_dpo_text_inference)
We also provide an inference endpoint for everyone to test the model.
It may initially take a few minutes to activate, but will eventually operate at the standard speed of HF's 7B model text inference endpoint.
The speed of inference depends on HF endpoint performance and is not related to Snorkel offerings.
This endpoint is designed for initial trials, not for ongoing production use. Have fun!
```
import requests
API_URL = "https://t1q6ks6fusyg1qq7.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
"Accept" : "application/json",
"Content-Type": "application/json"
}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "[INST] Recommend me some Hollywood movies [/INST]",
"parameters": {}
})
```
### Dataset:
Training dataset: [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset)
We utilize ONLY the prompts from [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized); **no external LLM responses used**.
### Methodology:
1. Generate five response variations for each prompt from a subset of 20,000 using the LLM - to start, we used [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
2. Apply [PairRM](https://huggingface.co/llm-blender/PairRM) for response reranking.
3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses.
4. Use this LLM as the base model for the next iteration, repeating three times in total.
This overview provides a high-level summary of our approach.
We plan to release more detailed results and findings in the coming weeks on the [Snorkel blog.](https://snorkel.ai/blog/)
The prompt format follows the Mistral model:
```[INST] {prompt} [/INST]```
### Training recipe:
- The provided data is formatted to be compatible with the Hugging Face's [Zephyr recipe](https://github.com/huggingface/alignment-handbook/tree/main/recipes/zephyr-7b-beta).
We executed the n_th DPO iteration using the "train/test_iteration_{n}".
### Key Premises:
- **Specialization Requirement**: For most enterprise use cases, using LLMs "off-the-shelf" falls short of production quality, necessitating additional fine-tuning and alignment.
- **Ease of Model Building**: Creating ranking/scoring/classification models is simpler than developing high-quality, manually annotated datasets for long-form responses.
- **Alignment Recipe**: Using smaller but specialized teacher models (reward models) can incrementally align LLMs towards specific axes.
### Applications:
Unlike our customers, who have very specific use cases to align LLMs to,
the AlpacaEval 2.0 leaderboard measures the ability of LLMS to follow user instructions.
With this demonstration, we focus on the general approach to alignment.
Thus, we use a general-purpose reward model - the performant [PairRM model](https://huggingface.co/llm-blender/PairRM).
We use the [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model as our base LLM.
For interest in building your **specialized internal reward models
that reflect your enterprises' needs**, please contact the Snorkel AI team or consider attending our
[**Enterprise LLM Summit: Building GenAI with Your Data on January 25, 2024**](https://snorkel.ai/event/enterprise-llm-summit/)
to learn more about "Programmatically scaling human preferences and alignment in GenAI".
### Result:
On [**Alpaca-Eval 2.0**](https://tatsu-lab.github.io/alpaca_eval/):
- The base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) scored **14.72**.
After applying the above methodology:
- This model scored **30.22** - ranked 3rd and the highest for an open-source base model at the time of publication.
- When post-processing the model outputs with PairRM-best-of-16, which involved generating 16 responses and selecting the highest-scoring response by PairRM, we scored **34.86** - ranked 2nd.
The best model on the leaderboard is "gpt-4-turbo", which is also the judge of optimal responses.
We recognize that the Alpaca-Eval 2.0 benchmark does not entirely capture the full range of capabilities and performances of LLMs.
However, in our current work, where the goal is to align with general "human preferences," Alpaca-Eval 2.0 serves as a suitable and representative benchmark.
Moving forward, we anticipate further contributions from the community regarding new alignment axes, and conduct evaluations using other appropriate benchmarks.
The Alpaca-Eval 2.0 evaluator, "gpt-4-turbo," exhibits a bias towards longer responses.
This tendency might also be present in our chosen reward model, resulting in our model producing lengthier responses after DPO iterations,
which can be among the factors to our higher ranks on the leaderboard.
Future work could include measures to control response length and other relevant metrics.
### Limitations:
The model is a quick demonstration that the LLMs can be programmatically aligned using smaller specialized reward models.
It does not have any moderation mechanisms.
We look forward to continuing to engage with the research community and our customers exploring optimal methods for getting models to respect guardrails,
allowing for deployment in environments requiring moderated outputs.
### Contemporary Work and Acknowledgements:
- The Mistral AI Team for developing and releasing the advanced Mistral-7B-Instruct-v0.2 model.
- The author of the [Direct Preference Optimization paper](https://arxiv.org/abs/2305.18290) for the innovative approach
- The author of the [Pairwise Reward Model for LLMs paper](https://arxiv.org/abs/2306.02561) for the powerful general-purpose reward model
- The HuggingFace team for the DPO implementation under [The Alignment Handbook](https://github.com/huggingface/alignment-handbook)
- We would also like to acknowledge contemporary work published independently on arXiv on 2024-01-18 by Meta & NYU (Yuan, et al) in a paper called [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020),
which proposes a similar general approach for creating alignment pairs from a larger set of candidate responses, but using the LLM as the reward model.
While this may work for general-purpose models, our experience has shown that task-specific reward models guided by SMEs are necessary for most
enterprise applications of LLMs for specific use cases, which is why we focus on the use of external reward models.
### The Snorkel AI Team
Hoang Tran, Chris Glaze, Braden Hancock
|
chathuranga-jayanath/codet5-small-v2
|
chathuranga-jayanath
| 2024-01-26T04:02:41Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-small",
"base_model:finetune:Salesforce/codet5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-26T03:39:52Z |
---
license: apache-2.0
base_model: Salesforce/codet5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: codet5-small-v2
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. -->
# codet5-small-v2
This model is a fine-tuned version of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0818
- Rouge1: 90.2445
- Rouge2: 87.8925
- Rougel: 90.3346
- Rougelsum: 90.4247
- Gen Len: 13.7838
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 20 | 1.6233 | 60.4701 | 43.4599 | 57.215 | 56.9531 | 13.5135 |
| No log | 2.0 | 40 | 0.5503 | 79.3375 | 69.4176 | 75.4377 | 75.2854 | 13.5676 |
| No log | 3.0 | 60 | 0.2397 | 66.4876 | 55.0069 | 62.0571 | 61.9916 | 11.8378 |
| No log | 4.0 | 80 | 0.1486 | 78.7452 | 74.9876 | 78.6615 | 78.6958 | 14.4054 |
| No log | 5.0 | 100 | 0.1200 | 81.8039 | 78.3534 | 82.0292 | 82.0142 | 14.3243 |
| No log | 6.0 | 120 | 0.1040 | 81.8039 | 78.3534 | 82.0292 | 82.0142 | 14.3243 |
| No log | 7.0 | 140 | 0.0931 | 88.2604 | 84.8265 | 88.0824 | 88.3097 | 14.4324 |
| No log | 8.0 | 160 | 0.0857 | 88.2604 | 84.8265 | 88.0824 | 88.3097 | 14.4324 |
| No log | 9.0 | 180 | 0.0834 | 90.2445 | 87.8925 | 90.3346 | 90.4247 | 13.7838 |
| No log | 10.0 | 200 | 0.0818 | 90.2445 | 87.8925 | 90.3346 | 90.4247 | 13.7838 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
LoneStriker/Snorkel-Mistral-PairRM-DPO-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-26T04:02:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"dataset:snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset",
"arxiv:2305.18290",
"arxiv:2306.02561",
"arxiv:2401.10020",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T19:30:35Z |
---
license: apache-2.0
datasets:
- snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset
pipeline_tag: text-generation
---
We offer a temporary HF space for everyone to try out the model: -> [**Snorkel-Mistral-PairRM-DPO Space**](https://huggingface.co/spaces/snorkelai/snorkelai_mistral_pairrm_dpo_text_inference)
We also provide an inference endpoint for everyone to test the model.
It may initially take a few minutes to activate, but will eventually operate at the standard speed of HF's 7B model text inference endpoint.
The speed of inference depends on HF endpoint performance and is not related to Snorkel offerings.
This endpoint is designed for initial trials, not for ongoing production use. Have fun!
```
import requests
API_URL = "https://t1q6ks6fusyg1qq7.us-east-1.aws.endpoints.huggingface.cloud"
headers = {
"Accept" : "application/json",
"Content-Type": "application/json"
}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "[INST] Recommend me some Hollywood movies [/INST]",
"parameters": {}
})
```
### Dataset:
Training dataset: [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset)
We utilize ONLY the prompts from [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized); **no external LLM responses used**.
### Methodology:
1. Generate five response variations for each prompt from a subset of 20,000 using the LLM - to start, we used [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
2. Apply [PairRM](https://huggingface.co/llm-blender/PairRM) for response reranking.
3. Update the LLM by applying Direct Preference Optimization (DPO) on the top (chosen) and bottom (rejected) responses.
4. Use this LLM as the base model for the next iteration, repeating three times in total.
This overview provides a high-level summary of our approach.
We plan to release more detailed results and findings in the coming weeks on the [Snorkel blog.](https://snorkel.ai/blog/)
The prompt format follows the Mistral model:
```[INST] {prompt} [/INST]```
### Training recipe:
- The provided data is formatted to be compatible with the Hugging Face's [Zephyr recipe](https://github.com/huggingface/alignment-handbook/tree/main/recipes/zephyr-7b-beta).
We executed the n_th DPO iteration using the "train/test_iteration_{n}".
### Key Premises:
- **Specialization Requirement**: For most enterprise use cases, using LLMs "off-the-shelf" falls short of production quality, necessitating additional fine-tuning and alignment.
- **Ease of Model Building**: Creating ranking/scoring/classification models is simpler than developing high-quality, manually annotated datasets for long-form responses.
- **Alignment Recipe**: Using smaller but specialized teacher models (reward models) can incrementally align LLMs towards specific axes.
### Applications:
Unlike our customers, who have very specific use cases to align LLMs to,
the AlpacaEval 2.0 leaderboard measures the ability of LLMS to follow user instructions.
With this demonstration, we focus on the general approach to alignment.
Thus, we use a general-purpose reward model - the performant [PairRM model](https://huggingface.co/llm-blender/PairRM).
We use the [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) model as our base LLM.
For interest in building your **specialized internal reward models
that reflect your enterprises' needs**, please contact the Snorkel AI team or consider attending our
[**Enterprise LLM Summit: Building GenAI with Your Data on January 25, 2024**](https://snorkel.ai/event/enterprise-llm-summit/)
to learn more about "Programmatically scaling human preferences and alignment in GenAI".
### Result:
On [**Alpaca-Eval 2.0**](https://tatsu-lab.github.io/alpaca_eval/):
- The base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) scored **14.72**.
After applying the above methodology:
- This model scored **30.22** - ranked 3rd and the highest for an open-source base model at the time of publication.
- When post-processing the model outputs with PairRM-best-of-16, which involved generating 16 responses and selecting the highest-scoring response by PairRM, we scored **34.86** - ranked 2nd.
The best model on the leaderboard is "gpt-4-turbo", which is also the judge of optimal responses.
We recognize that the Alpaca-Eval 2.0 benchmark does not entirely capture the full range of capabilities and performances of LLMs.
However, in our current work, where the goal is to align with general "human preferences," Alpaca-Eval 2.0 serves as a suitable and representative benchmark.
Moving forward, we anticipate further contributions from the community regarding new alignment axes, and conduct evaluations using other appropriate benchmarks.
The Alpaca-Eval 2.0 evaluator, "gpt-4-turbo," exhibits a bias towards longer responses.
This tendency might also be present in our chosen reward model, resulting in our model producing lengthier responses after DPO iterations,
which can be among the factors to our higher ranks on the leaderboard.
Future work could include measures to control response length and other relevant metrics.
### Limitations:
The model is a quick demonstration that the LLMs can be programmatically aligned using smaller specialized reward models.
It does not have any moderation mechanisms.
We look forward to continuing to engage with the research community and our customers exploring optimal methods for getting models to respect guardrails,
allowing for deployment in environments requiring moderated outputs.
### Contemporary Work and Acknowledgements:
- The Mistral AI Team for developing and releasing the advanced Mistral-7B-Instruct-v0.2 model.
- The author of the [Direct Preference Optimization paper](https://arxiv.org/abs/2305.18290) for the innovative approach
- The author of the [Pairwise Reward Model for LLMs paper](https://arxiv.org/abs/2306.02561) for the powerful general-purpose reward model
- The HuggingFace team for the DPO implementation under [The Alignment Handbook](https://github.com/huggingface/alignment-handbook)
- We would also like to acknowledge contemporary work published independently on arXiv on 2024-01-18 by Meta & NYU (Yuan, et al) in a paper called [Self-Rewarding Language Models](https://arxiv.org/abs/2401.10020),
which proposes a similar general approach for creating alignment pairs from a larger set of candidate responses, but using the LLM as the reward model.
While this may work for general-purpose models, our experience has shown that task-specific reward models guided by SMEs are necessary for most
enterprise applications of LLMs for specific use cases, which is why we focus on the use of external reward models.
### The Snorkel AI Team
Hoang Tran, Chris Glaze, Braden Hancock
|
silk-road/Haruhi-Dialogue-Speaker-Extract_qwen18
|
silk-road
| 2024-01-26T03:29:34Z | 276 | 3 |
transformers
|
[
"transformers",
"safetensors",
"qwen",
"feature-extraction",
"text-generation-inference",
"custom_code",
"zh",
"en",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2024-01-26T01:09:21Z |
---
license: apache-2.0
language:
- zh
- en
tags:
- text-generation-inference
---
# Chat凉宫春日的对话抽取模型
我们希望有一个模型能够从小说的chunk中批量去提取摘要和对话
这个模型就是实现了这一点。模型使用了大约30k的中文小说数据和20k的英文小说数据进行训练，在qwen-1.8上进行了3个epoch的finetune。 原则上模型同时支持中文和英文小说的抽取
主项目链接 https://github.com/LC1332/Chat-Haruhi-Suzumiya
- [李鲁鲁](https://github.com/LC1332)完成了数据的收集，以及进一步将inference程序扩展到连续的chunks
- [刘崇寒](https://github.com/khazic)完成了模型的训练
- [米唯实](https://github.com/hhhwmws0117)测试并上传模型到hugging face
# Chat Haruhi Suzumiya's Dialogue Extraction Model
We hope to have a model that can extract summaries and dialogues in batches from chunks of novels.
This model achieves just that. It was trained using approximately 30k Chinese novels and 20k English novels, and was fine-tuned on qwen-1.8 for three epochs. In principle, the model supports extracting for both Chinese and English novels.
Main project link: https://github.com/LC1332/Chat-Haruhi-Suzumiya
# Inference Code
https://github.com/LC1332/Chat-Haruhi-Suzumiya/blob/main/notebook/Dialogue_Speaker_Extract_Test.ipynb
```python
from transformers import AutoTokenizer, AutoModel, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("silk-road/Haruhi-Dialogue-Speaker-Extract_qwen18", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("silk-road/Haruhi-Dialogue-Speaker-Extract_qwen18", device_map="auto", trust_remote_code=True)
sys_prompt = "给定input paragraph，抽取其中的对话，并输出为json格式 Let's think it step by step 1. summarize input paragraph into bullet format，存储在summary字段 2. 抽取每一句对话的内容 dialogue，判断每一句话的说话人 said by, 存储在conversations中"
text = "Your novel text"
response_str, history = model.chat(tokenizer, text, history=[], system=sys_prompt)
```
# Official Prompt
Chinese:
```
给定input paragraph，抽取其中的对话，并输出为json格式 Let's think it step by step 1. summarize input paragraph into bullet format，存储在summary字段 2. 抽取每一句对话的内容 dialogue，判断每一句话的说话人 said by, 存储在conversations中
```
English:
```
Given an input paragraph, extract the dialogues within it, and output them in JSON format.
Let's think about it step by step:
- Summarize the input paragraph into bullet points and store it in the 'summary' field.
- Extract the content of each dialogue ('dialogue'), identify the speaker for each sentence ('said by'), and store these in 'conversations'.
```
### TODO
- [x] 拓展到多chunks的inference
- [x] 提供英语的例子
- [ ] 提供一个多章节并行inference的例子
- [ ] 在json解析失败的时候尝试直接从raw字符串提取summary
- [ ] 在失败的时候额外尝试调用openai进行推理
### TODO
- [x] Expand to multi-chunk inference
- [x] Provide an English example
- [ ] Provide an example of multi-chapter parallel inference
- [ ] Try extracting summary directly from raw strings when JSON parsing fails
- [ ] Additionally attempt to use OpenAI for inference when failing
## Chinese Output Example
```
{'summary': '- 彭蠡不在家中，老刀感到担忧并等待着彭蠡回家的时间，同时观察周围环境和人们的消费行为，表现出内心的饥饿感和焦虑情绪。', 'conversations': [{'dialogue': '哎，你们知道那儿一盘回锅肉多少钱吗？', 'said_by': '小李'}, {'dialogue': '靠，菜里有沙子。', 'said_by': '小丁'}, {'dialogue': '人家那儿一盘回锅肉，就三百四。', 'said_by': '小李'}, {'dialogue': '什么玩意？这么贵。', 'said_by': '小丁'}, {'dialogue': '你吃不了这么多。', 'said_by': '小李'}]}
{'summary': '- 彭蠡在家等待彭蠡回家，表现出内心的饥饿感和焦虑情绪，同时对彭蠡的行为表示不满和失望。彭蠡则对老刀的行为表现出冷漠和不屑的态度。', 'conversations': [{'dialogue': '我没时间和你解释。我需要去第一空间，你告诉我怎么走。', 'said_by': '老刀'}, {'dialogue': '回我家说，要走也从那儿走。', 'said_by': '彭蠡'}, {'dialogue': '回家啦，回家啦。转换马上开始了。', 'said_by': '车上的人'}, {'dialogue': '你不告诉我为什么，我就不告诉你怎么走。', 'said_by': '彭蠡'}, {'dialogue': '你躲在垃圾道里？去第二空间？那你得等24小时啊。', 'said_by': '彭蠡'}, {'dialogue': '二十万块。等一礼拜也值啊。', 'said_by': '老刀'}, {'dialogue': '你就这么缺钱花？', 'said_by': '彭蠡'}, {'dialogue': '糖糖还有一年多该去幼儿园了。我来不及了。', 'said_by': '老刀'}, {'dialogue': '你别说了。', 'said_by': '彭蠡'}]}
{'summary': '- 彭蠡对彭蠡的行为表现出不满和失望，同时对老刀的行为表现出冷漠和不屑的态度。', 'conversations': [{'dialogue': '你真是作死，她又不是你闺女，犯得着吗。', 'said_by': '彭蠡'}, {'dialogue': '别说这些了。快告我怎么走。', 'said_by': '老刀'}, {'dialogue': '你可得知道，万一被抓着，可不只是罚款，得关上好几个月。', 'said_by': '彭蠡'}, {'dialogue': '你不是去过好多次吗？', 'said_by': '老刀'}, {'dialogue': '只有四次。第五次就被抓了。', 'said_by': '彭蠡'}, {'dialogue': '那也够了。我要是能去四次，抓一次也无所谓。', 'said_by': '老刀'}, {'dialogue': '别说了。你要是真想让我带你去，我就带你去。', 'said_by': '彭蠡'}]}
- 彭蠡不在家中，老刀感到担忧并等待着彭蠡回家的时间，同时观察周围环境和人们的消费行为，表现出内心的饥饿感和焦虑情绪。
小李 : 哎，你们知道那儿一盘回锅肉多少钱吗？
小丁 : 靠，菜里有沙子。
小李 : 人家那儿一盘回锅肉，就三百四。
小丁 : 什么玩意？这么贵。
小李 : 你吃不了这么多。
- 彭蠡在家等待彭蠡回家，表现出内心的饥饿感和焦虑情绪，同时对彭蠡的行为表示不满和失望。彭蠡则对老刀的行为表现出冷漠和不屑的态度。
老刀 : 我没时间和你解释。我需要去第一空间，你告诉我怎么走。
彭蠡 : 回我家说，要走也从那儿走。
车上的人 : 回家啦，回家啦。转换马上开始了。
彭蠡 : 你不告诉我为什么，我就不告诉你怎么走。
彭蠡 : 你躲在垃圾道里？去第二空间？那你得等24小时啊。
老刀 : 二十万块。等一礼拜也值啊。
彭蠡 : 你就这么缺钱花？
老刀 : 糖糖还有一年多该去幼儿园了。我来不及了。
彭蠡 : 你别说了。
- 彭蠡对彭蠡的行为表现出不满和失望，同时对老刀的行为表现出冷漠和不屑的态度。
彭蠡 : 你真是作死，她又不是你闺女，犯得着吗。
老刀 : 别说这些了。快告我怎么走。
彭蠡 : 你可得知道，万一被抓着，可不只是罚款，得关上好几个月。
老刀 : 你不是去过好多次吗？
彭蠡 : 只有四次。第五次就被抓了。
老刀 : 那也够了。我要是能去四次，抓一次也无所谓。
彭蠡 : 别说了。你要是真想让我带你去，我就带你去。
```
## English Output Example
```
{'summary': "Snow-covered Paris, Kimura's workshop, artist and viewer engaging in conversation.", 'conversations': [{'dialogue': 'You should hear the stories they tell of you at the café. If Émile is to be believed, you arrived here as an ukiyo-e courtesan, nothing more than paper wrapped around a porcelain bowl. A painter—he will not say which of us it was, of course—bought the bowl and the print along with it.', 'said_by': 'Artist'}, {'dialogue': 'And the painter pulled me from the print with the sheer force of his imagination, I’m sure. Émile is a novelist and can hardly be trusted to give an accurate account. The reality of my conception is vastly more mundane, I assure you…though it does involve a courtesan.', 'said_by': 'Woman'}, {'dialogue': 'A grain of truth makes for the best fiction. nude, but leave the jewelry and the shoes. I’ll paint you on the chaise. We’ll have three hours in the proper light, and I will pay you four francs.', 'said_by': 'Artist'}, {'dialogue': 'Victorine gets five!', 'said_by': 'Woman'}, {'dialogue': 'Victorine is a redhead.', 'said_by': 'Artist'}, {'dialogue': 'My name is Mariko, by the way, but everyone calls me Mari.', 'said_by': 'Mariko'}]}
{'summary': "Snow-covered Paris, Kimura's workshop, artist and viewer engaged in conversation. Artist and viewer engage in intimate conversation and interaction.", 'conversations': [{'dialogue': 'I’m on the chaise', 'said_by': 'Artist'}, {'dialogue': 'Bring your left hip forward. No, not that far. Bend the leg a bit more, yes. Turn your head to face the canvas.', 'said_by': 'Artist'}, {'dialogue': 'Like a Manet', 'said_by': 'Artist'}, {'dialogue': 'Don’t like a model that talks while you work, huh?', 'said_by': 'Artist'}, {'dialogue': 'I don’t like being compared to other artists.', 'said_by': 'Artist'}, {'dialogue': 'Then you must paint me so well that I forget about the others.', 'said_by': 'Artist'}, {'dialogue': 'Tilt your head into the light. And look at me intently. Intently. As though I were the one naked on the chaise.', 'said_by': 'Artist'}, {'dialogue': 'You did better than I would have expected.', 'said_by': 'Artist'}, {'dialogue': 'There are other poses I could show you, if you like?', 'said_by': 'Artist'}, {'dialogue': 'But the sooner I get started on this portrait, the better.', 'said_by': 'Artist'}]}
{'summary': "Kimura's workshop, artist and viewer engaging in intimate conversation and interaction. Kimura responds with a strong, cold embrace, leading to a passionate physical exchange. Afterward, the artist falls asleep, leaving the narrator feeling incomplete and longing.", 'num': 14, 'conversations': [{'dialogue': 'I could show you other poses.', 'said_by': 'Kimura'}, {'dialogue': 'Yes.', 'said_by': 'Kimura'}, {'dialogue': 'See you tomorrow?', 'said_by': 'Artist'}]}
Snow-covered Paris, Kimura's workshop, artist and viewer engaging in conversation.
Artist : You should hear the stories they tell of you at the café. If Émile is to be believed, you arrived here as an ukiyo-e courtesan, nothing more than paper wrapped around a porcelain bowl. A painter—he will not say which of us it was, of course—bought the bowl and the print along with it.
Woman : And the painter pulled me from the print with the sheer force of his imagination, I’m sure. Émile is a novelist and can hardly be trusted to give an accurate account. The reality of my conception is vastly more mundane, I assure you…though it does involve a courtesan.
Artist : A grain of truth makes for the best fiction. nude, but leave the jewelry and the shoes. I’ll paint you on the chaise. We’ll have three hours in the proper light, and I will pay you four francs.
Woman : Victorine gets five!
Artist : Victorine is a redhead.
Mariko : My name is Mariko, by the way, but everyone calls me Mari.
Snow-covered Paris, Kimura's workshop, artist and viewer engaged in conversation. Artist and viewer engage in intimate conversation and interaction.
Artist : I’m on the chaise
Artist : Bring your left hip forward. No, not that far. Bend the leg a bit more, yes. Turn your head to face the canvas.
Artist : Like a Manet
Artist : Don’t like a model that talks while you work, huh?
Artist : I don’t like being compared to other artists.
Artist : Then you must paint me so well that I forget about the others.
Artist : Tilt your head into the light. And look at me intently. Intently. As though I were the one naked on the chaise.
Artist : You did better than I would have expected.
Artist : There are other poses I could show you, if you like?
Artist : But the sooner I get started on this portrait, the better.
Kimura's workshop, artist and viewer engaging in intimate conversation and interaction. Kimura responds with a strong, cold embrace, leading to a passionate physical exchange. Afterward, the artist falls asleep, leaving the narrator feeling incomplete and longing.
Kimura : I could show you other poses.
Kimura : Yes.
Artist : See you tomorrow?
```
|
CheriTangerine/Scoups
|
CheriTangerine
| 2024-01-26T03:28:03Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2024-01-26T03:25:42Z |
---
license: other
license_name: scoups
license_link: LICENSE
---
|
lmg-anon/vntl-13b-v0.1-qlora
|
lmg-anon
| 2024-01-26T03:06:14Z | 0 | 0 | null |
[
"safetensors",
"translation",
"ja",
"en",
"dataset:lmg-anon/VNTL-v2-1k",
"license:llama2",
"region:us"
] |
translation
| 2024-01-10T01:49:39Z |
---
license: llama2
datasets:
- lmg-anon/VNTL-v2-1k
language:
- ja
- en
pipeline_tag: translation
---
This is an experimental llama2 13B qlora created using the [VNTL-v2-1k](https://huggingface.co/datasets/lmg-anon/VNTL-v2-1k) dataset.
This fine-tune was done to see if 13B would give better results than 7B, but the improvements of it over the base model seem negligible to me, and the eval scores corroborate this.
This fine-tune still needs more testing though, I still have no way to automatically give a score to the models (other than the eval score), so I will probably look into that soon.
This is an prompt example:
```
<<START>>
Name: Uryuu Shingo (瓜生 新吾) | Gender: Male | Aliases: Onii-chan (お兄ちゃん)
Name: Uryuu Sakuno (瓜生 桜乃) | Gender: Female
<<JAPANESE>>
[桜乃]: 『……ごめん』
<<ENGLISH>> (fidelity = absolute)
[Sakuno]: 『... Sorry.』
<<JAPANESE>>
[新吾]: 「ううん、こう言っちゃなんだけど、迷子でよかったよ。桜乃は可愛いから、いろいろ心配しちゃってたんだぞ俺」
<<ENGLISH>> (fidelity = high)
```
The generated translation for that prompt, with temperature 0, is:
```
{TBD}
```
|
jtatman/tinydolphin-2.8_1b-fortuna-alpaca-v2
|
jtatman
| 2024-01-26T02:59:58Z | 80 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T02:53:42Z |
---
library_name: transformers
tags:
- unsloth
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
BanUrsus/gpt2-small-finetuned-codeparrot-ds_nlp-course-chapter7-section5
|
BanUrsus
| 2024-01-26T02:52:33Z | 99 | 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
| 2024-01-22T03:09:57Z |
---
license: mit
base_model: gpt2
tags:
- text-generation
- generated_from_trainer
model-index:
- name: gpt2-small-finetuned-codeparrot-ds_nlp-course-chapter7-section5
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. -->
# gpt2-small-finetuned-codeparrot-ds_nlp-course-chapter7-section5
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0606
## 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5691 | 0.08 | 5000 | 1.7463 |
| 1.6818 | 0.15 | 10000 | 1.5248 |
| 1.5328 | 0.23 | 15000 | 1.4226 |
| 1.4521 | 0.31 | 20000 | 1.3569 |
| 1.3944 | 0.38 | 25000 | 1.3030 |
| 1.3422 | 0.46 | 30000 | 1.2558 |
| 1.2976 | 0.54 | 35000 | 1.2129 |
| 1.2514 | 0.61 | 40000 | 1.1714 |
| 1.2089 | 0.69 | 45000 | 1.1321 |
| 1.1737 | 0.77 | 50000 | 1.0990 |
| 1.1427 | 0.84 | 55000 | 1.0758 |
| 1.1242 | 0.92 | 60000 | 1.0636 |
| 1.1142 | 1.0 | 65000 | 1.0606 |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.11.0+cu102
- Datasets 2.15.0
- Tokenizers 0.15.0
|
hoanghoavienvo/finetuning-sentiment-model-3000-samples
|
hoanghoavienvo
| 2024-01-26T02:51:59Z | 94 | 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
| 2024-01-26T02:45:25Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8692810457516339
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3406
- Accuracy: 0.8667
- F1: 0.8693
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.15.0
|
Nicolas852/q-FrozenLake-v1-4x4-noSlippery
|
Nicolas852
| 2024-01-26T02:51:11Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T02:51:06Z |
---
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="Nicolas852/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"])
```
|
tiagoblima/t5_base-qg-aap-test
|
tiagoblima
| 2024-01-26T02:37:46Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:tiagoblima/preprocessed-du-qg-squadv1_pt",
"base_model:unicamp-dl/ptt5-base-t5-vocab",
"base_model:finetune:unicamp-dl/ptt5-base-t5-vocab",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-17T10:21:32Z |
---
license: mit
base_model: unicamp-dl/ptt5-base-t5-vocab
tags:
- generated_from_trainer
datasets:
- tiagoblima/preprocessed-du-qg-squadv1_pt
model-index:
- name: t5_base-qg-aap-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. -->
# t5_base-qg-aap-test
This model is a fine-tuned version of [unicamp-dl/ptt5-base-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-base-t5-vocab) on the tiagoblima/preprocessed-du-qg-squadv1_pt dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0278
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 8.5434 |
| No log | 2.0 | 2 | 7.3013 |
| No log | 3.0 | 3 | 6.1993 |
| No log | 4.0 | 4 | 5.2898 |
| No log | 5.0 | 5 | 4.5226 |
| No log | 6.0 | 6 | 3.9202 |
| No log | 7.0 | 7 | 3.4436 |
| No log | 8.0 | 8 | 3.0408 |
| No log | 9.0 | 9 | 2.7138 |
| No log | 10.0 | 10 | 2.4436 |
| No log | 11.0 | 11 | 2.2130 |
| No log | 12.0 | 12 | 2.0190 |
| No log | 13.0 | 13 | 1.8451 |
| No log | 14.0 | 14 | 1.6746 |
| No log | 15.0 | 15 | 1.5047 |
| No log | 16.0 | 16 | 1.3376 |
| No log | 17.0 | 17 | 1.1800 |
| No log | 18.0 | 18 | 1.0434 |
| No log | 19.0 | 19 | 0.9442 |
| No log | 20.0 | 20 | 0.8739 |
| No log | 21.0 | 21 | 0.8163 |
| No log | 22.0 | 22 | 0.7629 |
| No log | 23.0 | 23 | 0.7118 |
| No log | 24.0 | 24 | 0.6618 |
| No log | 25.0 | 25 | 0.6104 |
| No log | 26.0 | 26 | 0.5597 |
| No log | 27.0 | 27 | 0.5112 |
| No log | 28.0 | 28 | 0.4657 |
| No log | 29.0 | 29 | 0.4243 |
| No log | 30.0 | 30 | 0.3873 |
| No log | 31.0 | 31 | 0.3529 |
| No log | 32.0 | 32 | 0.3209 |
| No log | 33.0 | 33 | 0.2918 |
| No log | 34.0 | 34 | 0.2667 |
| No log | 35.0 | 35 | 0.2436 |
| No log | 36.0 | 36 | 0.2215 |
| No log | 37.0 | 37 | 0.2004 |
| No log | 38.0 | 38 | 0.1808 |
| No log | 39.0 | 39 | 0.1637 |
| No log | 40.0 | 40 | 0.1484 |
| No log | 41.0 | 41 | 0.1357 |
| No log | 42.0 | 42 | 0.1252 |
| No log | 43.0 | 43 | 0.1159 |
| No log | 44.0 | 44 | 0.1079 |
| No log | 45.0 | 45 | 0.0997 |
| No log | 46.0 | 46 | 0.0922 |
| No log | 47.0 | 47 | 0.0858 |
| No log | 48.0 | 48 | 0.0802 |
| No log | 49.0 | 49 | 0.0749 |
| No log | 50.0 | 50 | 0.0703 |
| No log | 51.0 | 51 | 0.0660 |
| No log | 52.0 | 52 | 0.0626 |
| No log | 53.0 | 53 | 0.0596 |
| No log | 54.0 | 54 | 0.0573 |
| No log | 55.0 | 55 | 0.0555 |
| No log | 56.0 | 56 | 0.0539 |
| No log | 57.0 | 57 | 0.0521 |
| No log | 58.0 | 58 | 0.0506 |
| No log | 59.0 | 59 | 0.0496 |
| No log | 60.0 | 60 | 0.0485 |
| No log | 61.0 | 61 | 0.0472 |
| No log | 62.0 | 62 | 0.0460 |
| No log | 63.0 | 63 | 0.0445 |
| No log | 64.0 | 64 | 0.0432 |
| No log | 65.0 | 65 | 0.0421 |
| No log | 66.0 | 66 | 0.0409 |
| No log | 67.0 | 67 | 0.0396 |
| No log | 68.0 | 68 | 0.0385 |
| No log | 69.0 | 69 | 0.0375 |
| No log | 70.0 | 70 | 0.0365 |
| No log | 71.0 | 71 | 0.0358 |
| No log | 72.0 | 72 | 0.0350 |
| No log | 73.0 | 73 | 0.0344 |
| No log | 74.0 | 74 | 0.0338 |
| No log | 75.0 | 75 | 0.0334 |
| No log | 76.0 | 76 | 0.0329 |
| No log | 77.0 | 77 | 0.0326 |
| No log | 78.0 | 78 | 0.0321 |
| No log | 79.0 | 79 | 0.0317 |
| No log | 80.0 | 80 | 0.0314 |
| No log | 81.0 | 81 | 0.0310 |
| No log | 82.0 | 82 | 0.0306 |
| No log | 83.0 | 83 | 0.0303 |
| No log | 84.0 | 84 | 0.0299 |
| No log | 85.0 | 85 | 0.0297 |
| No log | 86.0 | 86 | 0.0294 |
| No log | 87.0 | 87 | 0.0292 |
| No log | 88.0 | 88 | 0.0290 |
| No log | 89.0 | 89 | 0.0288 |
| No log | 90.0 | 90 | 0.0287 |
| No log | 91.0 | 91 | 0.0285 |
| No log | 92.0 | 92 | 0.0284 |
| No log | 93.0 | 93 | 0.0282 |
| No log | 94.0 | 94 | 0.0281 |
| No log | 95.0 | 95 | 0.0281 |
| No log | 96.0 | 96 | 0.0280 |
| No log | 97.0 | 97 | 0.0279 |
| No log | 98.0 | 98 | 0.0279 |
| No log | 99.0 | 99 | 0.0278 |
| 0.8788 | 100.0 | 100 | 0.0278 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
jlbaker361/dcgan-wikiart100
|
jlbaker361
| 2024-01-26T02:37:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-25T03:37:42Z |
---
{}
---
Creative Adversarial Network
epochs: 50
dataset jlbaker361/wikiart-balanced100
n classes 27
batch_size 4
images where resized to 512
and then center cropped to: 512
used clip=False
discriminator parameters:
init_dim: 32
final_dim 512
generator parameters:
input noise_dim: 100
|
tjkmitl/PromptEmotionNewsModel_2
|
tjkmitl
| 2024-01-26T02:32:38Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf",
"base_model:finetune:openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf",
"license:apache-2.0",
"region:us"
] | null | 2024-01-26T02:32:28Z |
---
license: apache-2.0
base_model: openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf
tags:
- generated_from_trainer
model-index:
- name: PromptEmotionNewsModel_2
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. -->
# PromptEmotionNewsModel_2
This model is a fine-tuned version of [openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf](https://huggingface.co/openthaigpt/openthaigpt-1.0.0-beta-7b-chat-ckpt-hf) 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: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
JKuang96/cartpole
|
JKuang96
| 2024-01-26T02:24:44Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-26T02:24:35Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
EmbeddedLLM/Medusa2-Mistral-7B-Instruct-v0.2
|
EmbeddedLLM
| 2024-01-26T02:11:36Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"medusa",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T05:20:51Z |
---
license: apache-2.0
tags:
- medusa
---
# Model Description
This is a Medusa model for Mistral 7B Instruct v0.2.
This is trained using the latest Medusa 2 commit.
## Training:
* Dataset used is the self distillation dataset from Mistral 7B Instruct v0.2, temperature 0.3 with output token of 2048.
* It has been trained using axolotl fork as describe in Medusa 2 README.md
## Inference:
* To load the model please follow the instruction found in [Github](https://github.com/FasterDecoding/Medusa?tab=readme-ov-file)
|
graceneutrality/ppo-SnowballTarget
|
graceneutrality
| 2024-01-26T01:48:57Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-01-25T19:04:25Z |
---
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: graceneutrality/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
kanishka/smolm-autoreg-bpe-counterfactual-babylm-keys_to_pipps_2913-1e-4
|
kanishka
| 2024-01-26T01:45:29Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T03:08:57Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-keys_to_pipps_2913-1e-4
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. -->
# smolm-autoreg-bpe-counterfactual-babylm-keys_to_pipps_2913-1e-4
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3547
- Accuracy: 0.4065
## 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: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 4.0518 | 1.0 | 18629 | 4.2148 | 0.3103 |
| 3.5731 | 2.0 | 37258 | 3.7002 | 0.3628 |
| 3.3939 | 3.0 | 55887 | 3.5428 | 0.3792 |
| 3.2906 | 4.0 | 74516 | 3.4673 | 0.3886 |
| 3.2245 | 5.0 | 93145 | 3.4056 | 0.3939 |
| 3.1727 | 6.0 | 111774 | 3.4006 | 0.3969 |
| 3.1301 | 7.0 | 130403 | 3.3740 | 0.3995 |
| 3.0954 | 8.0 | 149032 | 3.3669 | 0.4005 |
| 3.0675 | 9.0 | 167661 | 3.3578 | 0.4022 |
| 3.0422 | 10.0 | 186290 | 3.3458 | 0.4028 |
| 3.011 | 11.0 | 204919 | 3.3474 | 0.4041 |
| 2.9957 | 12.0 | 223548 | 3.3452 | 0.4046 |
| 2.9729 | 13.0 | 242177 | 3.3344 | 0.4055 |
| 2.9501 | 14.0 | 260806 | 3.3348 | 0.4064 |
| 2.9303 | 15.0 | 279435 | 3.3385 | 0.4056 |
| 2.91 | 16.0 | 298064 | 3.3449 | 0.4063 |
| 2.8962 | 17.0 | 316693 | 3.3472 | 0.4060 |
| 2.8792 | 18.0 | 335322 | 3.3533 | 0.4061 |
| 2.867 | 19.0 | 353951 | 3.3526 | 0.4063 |
| 2.8451 | 20.0 | 372580 | 3.3547 | 0.4065 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
badokorach/afriqa_afroxlmr_lug_250124
|
badokorach
| 2024-01-26T01:33:13Z | 1 | 0 |
transformers
|
[
"transformers",
"tf",
"xlm-roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:masakhane/afriqa_afroxlmr_squad_v2",
"base_model:finetune:masakhane/afriqa_afroxlmr_squad_v2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-25T21:01:52Z |
---
license: mit
base_model: masakhane/afriqa_afroxlmr_squad_v2
tags:
- generated_from_keras_callback
model-index:
- name: badokorach/afriqa_afroxlmr_lug_250124
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# badokorach/afriqa_afroxlmr_lug_250124
This model is a fine-tuned version of [masakhane/afriqa_afroxlmr_squad_v2](https://huggingface.co/masakhane/afriqa_afroxlmr_squad_v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.8447
- Validation Loss: 0.0
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 1e-05, 'decay_steps': 3520, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.02}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4578 | 0.0 | 0 |
| 3.2460 | 0.0 | 1 |
| 3.1750 | 0.0 | 2 |
| 3.1099 | 0.0 | 3 |
| 3.0490 | 0.0 | 4 |
| 2.9633 | 0.0 | 5 |
| 2.8794 | 0.0 | 6 |
| 3.0420 | 0.0 | 7 |
| 2.6324 | 0.0 | 8 |
| 2.5253 | 0.0 | 9 |
| 2.4274 | 0.0 | 10 |
| 2.3290 | 0.0 | 11 |
| 2.2193 | 0.0 | 12 |
| 2.1263 | 0.0 | 13 |
| 2.0546 | 0.0 | 14 |
| 1.9944 | 0.0 | 15 |
| 1.9583 | 0.0 | 16 |
| 1.8896 | 0.0 | 17 |
| 1.8845 | 0.0 | 18 |
| 1.8447 | 0.0 | 19 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ntc-ai/SDXL-LoRA-slider.wearing-a-suit-and-tie
|
ntc-ai
| 2024-01-26T01:27:57Z | 124 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2024-01-26T01:27:49Z |
---
language:
- en
thumbnail: "images/evaluate/wearing a suit and tie.../wearing a suit and tie_17_3.0.png"
widget:
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_17_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_19_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_20_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_21_3.0.png
- text: wearing a suit and tie
output:
url: images/wearing a suit and tie_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "wearing a suit and tie"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - wearing a suit and tie (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/wearing a suit and tie_17_-3.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_17_0.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_17_3.0.png" width=256 height=256 /> |
| <img src="images/wearing a suit and tie_19_-3.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_19_0.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_19_3.0.png" width=256 height=256 /> |
| <img src="images/wearing a suit and tie_20_-3.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_20_0.0.png" width=256 height=256 /> | <img src="images/wearing a suit and tie_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
wearing a suit and tie
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.wearing-a-suit-and-tie', weight_name='wearing a suit and tie.safetensors', adapter_name="wearing a suit and tie")
# Activate the LoRA
pipe.set_adapters(["wearing a suit and tie"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, wearing a suit and tie"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1140+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
nisten/shqiponja-59b-v1
|
nisten
| 2024-01-26T01:00:40Z | 47 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"frankenstein",
"merge",
"conversational",
"base_model:jondurbin/nontoxic-bagel-34b-v0.2",
"base_model:finetune:jondurbin/nontoxic-bagel-34b-v0.2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-13T05:27:34Z |
---
base_model:
- jondurbin/nontoxic-bagel-34b-v0.2
tags:
- mergekit
- frankenstein
- merge
license: mit
---
# Shqiponja-59 V1
This is an untrained experimental 59B merged model.
Picked these two models specifically to compliment each others strengths.
### Models Merged
* NousResearch/Nous-Hermes-2-Yi-34B
* jondurbin/nontoxic-bagel-34b-v0.2
Merged using the Undi95 style passthrough merge method.
### The secret sauce
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 52]
model: /home/admin/nv1/nontoxic-bagel-34b-v0.2
- sources:
- layer_range: [8, 60]
model: /home/admin/nv1/Nous-Hermes-2-Yi-34B
```
# License MIT - Enjoy
|
alirzb/SeizureClassifier_Wav2Vec_43243531
|
alirzb
| 2024-01-26T00:56:36Z | 146 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-01-25T19:17:06Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SeizureClassifier_Wav2Vec_43243531
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. -->
# SeizureClassifier_Wav2Vec_43243531
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0063
- Accuracy: 0.9990
## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.13 | 1.0 | 339 | 0.1382 | 0.9616 |
| 0.0931 | 2.0 | 678 | 0.0613 | 0.9839 |
| 0.0265 | 3.0 | 1017 | 0.0248 | 0.9942 |
| 0.026 | 4.0 | 1357 | 0.0612 | 0.9900 |
| 0.0252 | 5.0 | 1696 | 0.0460 | 0.9894 |
| 0.0369 | 6.0 | 2035 | 0.0148 | 0.9968 |
| 0.0018 | 7.0 | 2374 | 0.0049 | 0.9990 |
| 0.0007 | 8.0 | 2714 | 0.0114 | 0.9981 |
| 0.0056 | 9.0 | 3053 | 0.0107 | 0.9987 |
| 0.0003 | 10.0 | 3392 | 0.0067 | 0.9990 |
| 0.0101 | 11.0 | 3731 | 0.0039 | 0.9994 |
| 0.0073 | 12.0 | 4071 | 0.0049 | 0.9994 |
| 0.0113 | 13.0 | 4410 | 0.0061 | 0.9990 |
| 0.0002 | 14.0 | 4749 | 0.0067 | 0.9990 |
| 0.0002 | 14.99 | 5085 | 0.0063 | 0.9990 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
motherduckdb/DuckDB-NSQL-7B-v0.1
|
motherduckdb
| 2024-01-26T00:45:52Z | 381 | 90 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:meta-llama/Llama-2-7b",
"base_model:finetune:meta-llama/Llama-2-7b",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T19:13:56Z |
---
license: llama2
inference:
parameters:
do_sample: false
max_length: 200
base_model: meta-llama/Llama-2-7b
widget:
- text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):"
example_title: "read test.csv"
- text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question.\n\n### Input:\n\n### Question:\ncreate a new table called tmp from test.csv\n\n### Response (use duckdb shorthand if possible):"
example_title: "get _amount columns"
- text: "### Instruction:\nYour task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.\n\n### Input:\nHere is the database schema that the SQL query will run on:\nCREATE TABLE rideshare (\n hvfhs_license_num varchar,\n dispatching_base_num varchar,\n originating_base_num varchar,\n request_datetime timestamp,\n on_scene_datetime timestamp,\n pickup_datetime timestamp,\n dropoff_datetime timestamp,\n trip_miles double,\n trip_time bigint,\n\n);\n\n### Question:\nget longest trip in december 2022\n\n### Response (use duckdb shorthand if possible):"
example_title: "taxi trips"
---
# DuckDB-NSQL-7B
## Model Description
NSQL is a family of autoregressive open-source large foundation models (FMs) designed specifically for SQL generation tasks.
In this repository we are introducing a new member of NSQL, DuckDB-NSQL. It's based on Meta's original [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b) and further pre-trained on a dataset of general SQL queries and then fine-tuned on a dataset composed of DuckDB text-to-SQL pairs.
## Training Data
200k DuckDB text-to-SQL pairs, synthetically generated using [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), guided by the DuckDB v0.9.2 documentation. And text-to-SQL pairs from [NSText2SQL](https://huggingface.co/datasets/NumbersStation/NSText2SQL) that were transpiled to DuckDB SQL using [sqlglot](https://github.com/tobymao/sqlglot).
## Evaluation Data
We evaluate our models on a DuckDB-specific benchmark that contains 75 text-to-SQL pairs. The benchmark is available [here](https://github.com/NumbersStationAI/DuckDB-NSQL/).
## Training Procedure
DuckDB-NSQL was trained using cross-entropy loss to maximize the likelihood of sequential inputs. For finetuning on text-to-SQL pairs, we only compute the loss over the SQL portion of the pair. The model is trained using 80GB A100s, leveraging data and model parallelism. We fine-tuned for 10 epochs.
## Intended Use and Limitations
The model was designed for text-to-SQL generation tasks from given table schema and natural language prompts. The model works best with the prompt format defined below and outputs.
In contrast to existing text-to-SQL models, the SQL generation is not contrained to `SELECT` statements, but can generate any valid DuckDB SQL statement, including statements for official DuckDB extensions.
## How to Use
Example 1:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)
text = """### Instruction:
Your task is to generate valid duckdb SQL to answer the following question.
### Input:
### Question:
create a new table called tmp from test.csv
### Response (use duckdb shorthand if possible):
"""
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=500)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
Example 2:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)
text = """### Instruction:
Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.
### Input:
Here is the database schema that the SQL query will run on:
CREATE TABLE taxi (
VendorID bigint,
tpep_pickup_datetime timestamp,
tpep_dropoff_datetime timestamp,
passenger_count double,
trip_distance double,
fare_amount double,
extra double,
tip_amount double,
tolls_amount double,
improvement_surcharge double,
total_amount double,
);
### Question:
get all columns ending with _amount from taxi table
### Response (use duckdb shorthand if possible):"""
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=500)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
Example 3:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")
model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", torch_dtype=torch.bfloat16)
text = """### Instruction:
Your task is to generate valid duckdb SQL to answer the following question, given a duckdb database schema.
### Input:
Here is the database schema that the SQL query will run on:
CREATE TABLE rideshare (
hvfhs_license_num varchar,
dispatching_base_num varchar,
originating_base_num varchar,
request_datetime timestamp,
on_scene_datetime timestamp,
pickup_datetime timestamp,
dropoff_datetime timestamp,
trip_miles double,
trip_time bigint,
);
### Question:
get longest trip in december 2022
### Response (use duckdb shorthand if possible):
"""
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=500)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
```
For more information (e.g., run with your local database), please find examples in [this repository](https://github.com/NumbersStationAI/DuckDB-NSQL).
|
mhammadkhan/peft-lora-starcoderbase-1b-llamaindex-copilot
|
mhammadkhan
| 2024-01-26T00:44:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T00:39:24Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
danielribeiro/google-play-sentiment-analysis
|
danielribeiro
| 2024-01-26T00:35:51Z | 24,499 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:neuralmind/bert-large-portuguese-cased",
"base_model:finetune:neuralmind/bert-large-portuguese-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-19T11:47:38Z |
---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: google-play-sentiment-analysis
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. -->
# google-play-sentiment-analysis
This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7830
- Accuracy: 0.6571
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8207 | 1.0 | 1200 | 0.7830 | 0.6571 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
michalzajac/adversarial_trainer
|
michalzajac
| 2024-01-26T00:33:07Z | 94 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T00:02:39Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: adversarial_trainer
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. -->
# adversarial_trainer
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 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: 1
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|
jeiku/General_Purpose_3B_GGUF
|
jeiku
| 2024-01-26T00:25:36Z | 16 | 2 | null |
[
"gguf",
"mergekit",
"merge",
"arxiv:2203.05482",
"base_model:jeiku/Everything_v3_128_StableLM",
"base_model:merge:jeiku/Everything_v3_128_StableLM",
"base_model:jeiku/Theory_of_Mind_128_StableLM",
"base_model:merge:jeiku/Theory_of_Mind_128_StableLM",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-01-25T23:59:59Z |
---
base_model:
- jeiku/Theory_of_Mind_128_StableLM
- jeiku/Everything_v3_128_StableLM
- jeiku/Gnosis_StableLM
tags:
- mergekit
- merge
---
# mooby
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* new1 + [jeiku/Theory_of_Mind_128_StableLM](https://huggingface.co/jeiku/Theory_of_Mind_128_StableLM)
* new1 + [jeiku/Everything_v3_128_StableLM](https://huggingface.co/jeiku/Everything_v3_128_StableLM)
* new1 + [jeiku/Gnosis_StableLM](https://huggingface.co/jeiku/Gnosis_StableLM)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: linear
models:
- model: new1+jeiku/Theory_of_Mind_128_StableLM
parameters:
weight: 1
- model: new1+jeiku/Everything_v3_128_StableLM
parameters:
weight: 1
- model: new1+jeiku/Gnosis_StableLM
parameters:
weight: 1
dtype: float16
```
|
natutaro/line-distilbert-base-japanese-finetuned-emotion
|
natutaro
| 2024-01-25T23:56:49Z | 120 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:line-corporation/line-distilbert-base-japanese",
"base_model:finetune:line-corporation/line-distilbert-base-japanese",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T12:06:47Z |
---
license: apache-2.0
base_model: line-corporation/line-distilbert-base-japanese
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: line-distilbert-base-japanese-finetuned-emotion
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. -->
# line-distilbert-base-japanese-finetuned-emotion
This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2180
- Accuracy: 0.92
- F1: 0.9200
## 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: 64
- eval_batch_size: 64
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.3356 | 1.0 | 125 | 0.2241 | 0.918 | 0.918 |
| 0.1974 | 2.0 | 250 | 0.2180 | 0.92 | 0.9200 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
fredhsu/fastai-dogcat
|
fredhsu
| 2024-01-25T23:53:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-25T23:52:23Z |
This is the model created following the fast.ai course as an example exported model. It was trained using fast.ai's vision learner on the Oxford pet image dataset.
|
mii-llm/maestrale-chat-v0.2-alpha-sft
|
mii-llm
| 2024-01-25T23:38:33Z | 2,762 | 6 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"sft",
"it",
"chatml",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T21:00:06Z |
---
tags:
- sft
- it
- mistral
- chatml
model-index:
- name: maestrale-chat-v0.2-alpha
results: []
license: cc-by-nc-4.0
language:
- it
prompt_template: >-
<|im_start|>system {system_message}<|im_end|> <|im_start|>user
{prompt}<|im_end|> <|im_start|>assistant
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/8eqbpHp.jpg" alt="Mii-LLM" 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-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://buy.stripe.com/8wM00Sf3vb3H3pmfYY">Want to contribute? Please donate! This will let us work on better datasets and models!</a></p>
</div>
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Maestrale chat alpha ༄
By @efederici and @mferraretto
## Model description
- **Language Model**: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus.
- **Fine-Tuning**: SFT performed on ~270k Italian convs/instructions for one epoch.
This model uses ChatML prompt format:
```
<|im_start|>system
Assisti sempre con cura, rispetto e verità. Rispondi con la massima utilità ma in modo sicuro. Evita contenuti dannosi, non etici, pregiudizievoli o negativi. Assicurati che le risposte promuovano equità e positività.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Usage:
```python
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GenerationConfig,
TextStreamer
)
import torch
torch.backends.cuda.matmul.allow_tf32 = True
tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.2-alpha")
model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.2-alpha", load_in_8bit=True, device_map="auto")
gen = GenerationConfig(
do_sample=True,
temperature=0.7,
repetition_penalty=1.2,
top_k=50,
top_p=0.95,
max_new_tokens=500,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.convert_tokens_to_ids("<|im_end|>")
)
messages = [
{"role": "system", "content": "Assisti sempre con cura, rispetto e verità. Rispondi con la massima utilità ma in modo sicuro. Evita contenuti dannosi, non etici, pregiudizievoli o negativi. Assicurati che le risposte promuovano equità e positività."},
{"role": "user", "content": "{prompt}"}
]
with torch.no_grad(), torch.backends.cuda.sdp_kernel(
enable_flash=True,
enable_math=False,
enable_mem_efficient=False
):
temp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(temp, return_tensors="pt").to("cuda")
streamer = TextStreamer(tokenizer, skip_prompt=True)
_ = model.generate(
**inputs,
streamer=streamer,
generation_config=gen
)
```
## Intended uses & limitations
It's an alpha version, it's not `aligned`. We are working on alignment data and evals.
|
microdev1/autotrain1
|
microdev1
| 2024-01-25T23:29:46Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-24T05:23:39Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
Kooten/EstopianMaid-13B-4bpw-exl2
|
Kooten
| 2024-01-25T23:19:09Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"roleplay",
"text-generation-inference",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T21:06:58Z |
---
license: apache-2.0
language:
- en
tags:
- roleplay
- text-generation-inference
---
# EstopianMaid-13B 4bpw
## Description
Exllama quant of [KatyTheCutie/EstopianMaid-13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/EstopianMaid-13B-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/EstopianMaid-13B-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/EstopianMaid-13B-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/EstopianMaid-13B-4bpw-exl2)
## Prompt format:
Unclear.. none were suggested and the merged models use different formats.
## Contact
Kooten on discord
|
arun100/whisper-base-bn-3
|
arun100
| 2024-01-25T23:13:16Z | 70 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"whisper-event",
"generated_from_trainer",
"bn",
"dataset:mozilla-foundation/common_voice_16_0",
"base_model:arun100/whisper-base-bn",
"base_model:finetune:arun100/whisper-base-bn",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-25T06:06:28Z |
---
language:
- bn
license: apache-2.0
base_model: arun100/whisper-base-bn
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base Bengali
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_16_0 bn
type: mozilla-foundation/common_voice_16_0
config: bn
split: test
args: bn
metrics:
- name: Wer
type: wer
value: 29.92358146984869
---
<!-- 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 Bengali
This model is a fine-tuned version of [arun100/whisper-base-bn](https://huggingface.co/arun100/whisper-base-bn) on the mozilla-foundation/common_voice_16_0 bn dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2151
- Wer: 29.9236
## 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-06
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2476 | 1.72 | 500 | 0.2751 | 36.1695 |
| 0.2284 | 3.43 | 1000 | 0.2622 | 35.1668 |
| 0.213 | 5.15 | 1500 | 0.2524 | 34.2022 |
| 0.2048 | 6.86 | 2000 | 0.2447 | 33.5266 |
| 0.1948 | 8.58 | 2500 | 0.2382 | 32.7495 |
| 0.1852 | 10.29 | 3000 | 0.2334 | 32.2322 |
| 0.1789 | 12.01 | 3500 | 0.2295 | 31.7244 |
| 0.1738 | 13.72 | 4000 | 0.2260 | 31.2341 |
| 0.166 | 15.44 | 4500 | 0.2236 | 30.9562 |
| 0.1629 | 17.15 | 5000 | 0.2214 | 30.8171 |
| 0.1636 | 18.87 | 5500 | 0.2194 | 30.4368 |
| 0.1578 | 20.58 | 6000 | 0.2181 | 30.2520 |
| 0.1628 | 22.3 | 6500 | 0.2170 | 30.1858 |
| 0.1566 | 24.01 | 7000 | 0.2161 | 30.0694 |
| 0.1564 | 25.73 | 7500 | 0.2156 | 29.9943 |
| 0.1545 | 27.44 | 8000 | 0.2153 | 29.9294 |
| 0.1548 | 29.16 | 8500 | 0.2151 | 29.9236 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
|
stevhliu/my_awesome_eli5_clm-model
|
stevhliu
| 2024-01-25T23:03:03Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-12T21:10:52Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-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_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8283
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9484 | 1.0 | 1321 | 3.8403 |
| 3.8458 | 2.0 | 2642 | 3.8302 |
| 3.8048 | 3.0 | 3963 | 3.8283 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
jorgefalcon/llama-2-7b-datetime-regex-patterns
|
jorgefalcon
| 2024-01-25T23:02:36Z | 3 | 3 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"regex",
"dataset:jorgefalcon/datetime-regex-patterns",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T22:05:19Z |
---
datasets:
- jorgefalcon/datetime-regex-patterns
tags:
- code
- regex
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Model aims to generate a regex pattern given a sample string. For now, it's only trained in non-standard iso datetimes.
## 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]
|
menutp/profanity-fr-65ac
|
menutp
| 2024-01-25T22:52:09Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"license:wtfpl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T22:19:22Z |
---
license: wtfpl
---
this model was trained on 95% of my dataset menutp/hate_speech-fr_mini and validated on the last 5% it reached 65% accuracy.
labels are to be interpreted as follow :
```
{
0: "neutral",
1: "toxic",
2: "severe toxic",
3: "obscene",
4: "threat",
5: "insult",
6: "identity hate",
7: "generally_offensive"
}
```
due to the lack of training data for the label between 1 and 6 only label 0 and 7 are to be trusted
|
varun-v-rao/t5-large-snli-model3
|
varun-v-rao
| 2024-01-25T22:45:57Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:google-t5/t5-large",
"base_model:finetune:google-t5/t5-large",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T18:24:34Z |
---
license: apache-2.0
base_model: t5-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: t5-large-snli-model3
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. -->
# t5-large-snli-model3
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2242
- Accuracy: 0.9267
## 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: 128
- eval_batch_size: 128
- seed: 71
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2808 | 1.0 | 4292 | 0.2214 | 0.9246 |
| 0.2491 | 2.0 | 8584 | 0.2190 | 0.9259 |
| 0.2213 | 3.0 | 12876 | 0.2242 | 0.9267 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Kooten/EstopianMaid-13B-6bpw-exl2
|
Kooten
| 2024-01-25T22:30:22Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"roleplay",
"text-generation-inference",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T21:06:37Z |
---
license: apache-2.0
language:
- en
tags:
- roleplay
- text-generation-inference
---
# EstopianMaid-13B 6bpw
## Description
Exllama quant of [KatyTheCutie/EstopianMaid-13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/EstopianMaid-13B-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/EstopianMaid-13B-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/EstopianMaid-13B-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/EstopianMaid-13B-4bpw-exl2)
## Prompt format:
Unclear.. none were suggested and the merged models use different formats.
## Contact
Kooten on discord
|
cloudyu/Truthful_DPO_cloudyu_Mixtral_34Bx2_MoE_60B
|
cloudyu
| 2024-01-25T22:15:41Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"DPO",
"RL-TUNED",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T16:04:49Z |
---
license: mit
tags:
- moe
- DPO
- RL-TUNED
---
* [DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer) with dataset jondurbin/truthy-dpo-v0.1 to improve [cloudyu/Mixtral_34Bx2_MoE_60B]
```
DPO Trainer
TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al., 2023.
```
* Metrics NOT test!
|
Kooten/EstopianMaid-13B-8bpw-exl2
|
Kooten
| 2024-01-25T22:06:05Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"roleplay",
"text-generation-inference",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-25T21:06:29Z |
---
license: apache-2.0
language:
- en
tags:
- roleplay
- text-generation-inference
---
# EstopianMaid-13B 8bpw
## Description
Exllama quant of [KatyTheCutie/EstopianMaid-13B](https://huggingface.co/KatyTheCutie/EstopianMaid-13B)
## Other quants:
EXL2: [8bpw](https://huggingface.co/Kooten/EstopianMaid-13B-8bpw-exl2), [6bpw](https://huggingface.co/Kooten/EstopianMaid-13B-6bpw-exl2), [5bpw](https://huggingface.co/Kooten/EstopianMaid-13B-5bpw-exl2), [4bpw](https://huggingface.co/Kooten/EstopianMaid-13B-4bpw-exl2)
## Prompt format:
Unclear.. none were suggested and the merged models use different formats.
## Contact
Kooten on discord
|
YouKnwMe/Mistral-7b-instruct-v0.2-private-edw2
|
YouKnwMe
| 2024-01-25T22:05:22Z | 0 | 0 | null |
[
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-25T22:03:03Z |
---
license: cc-by-nc-4.0
---
Description to load and test will be added soon. More details on training and data will be added aswell.
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
TBD
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
argilla/phi2-lora-distilabel-intel-orca-dpo-pairs
|
argilla
| 2024-01-25T21:45:09Z | 4 | 2 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"distilabel",
"argilla",
"text-generation",
"en",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] |
text-generation
| 2024-01-25T05:02:44Z |
---
license: mit
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
- distilabel
- argilla
base_model: microsoft/phi-2
model-index:
- name: phi2-lora-quantized-distilabel-intel-orca-dpo-pairs
results: []
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
language:
- en
pipeline_tag: text-generation
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi2-lora-quantized-distilabel-intel-orca-dpo-pairs
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on [distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs).
The full training notebook can be found [here](https://colab.research.google.com/drive/1PGMj7jlkJaCiSNNihA2NtpILsRgkRXrJ?usp=sharing).
It achieves the following results on the evaluation set:
- Loss: 0.4537
- Rewards/chosen: -0.0837
- Rewards/rejected: -1.2628
- Rewards/accuracies: 0.8301
- Rewards/margins: 1.1791
- Logps/rejected: -224.8409
- Logps/chosen: -203.2228
- Logits/rejected: 0.4773
- Logits/chosen: 0.3062
## Model description
The adapter was fine-tuned on a Google Colab A100 GPU using DPO and the [distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs). In order to scale LoRa approached for LLMs, I recommend looking at [predibase/lorax](https://github.com/predibase/lorax).
You can play around with the model shown below. We load the LoRa adapter and bits_n_bytes config (only when CUDA is available).
```python
import torch
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig
)
from peft import PeftModel
# template used for fine-tune
# template = """\
# Instruct: {instruction}\n
# Output: {response}"""
if torch.cuda.is_available():
device = torch.device("cuda")
print(f"Using {torch.cuda.get_device_name(0)}")
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type='nf4',
bnb_4bit_compute_dtype='float16',
bnb_4bit_use_double_quant=False,
)
elif torch.backends.mps.is_available():
device = torch.device("mps")
bnb_config = None
else:
device = torch.device("cpu")
bnb_config = None
print("No GPU available, using CPU instead.")
config = PeftConfig.from_pretrained("davidberenstein1957/phi2-lora-quantized-distilabel-intel-orca-dpo-pairs")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype=torch.float16, quantization_config=bnb_config)
model = PeftModel.from_pretrained(model, "davidberenstein1957/phi2-lora-quantized-distilabel-intel-orca-dpo-pairs").to(device)
prompt = "Instruct: What is the capital of France? \nOutput:""
inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs)
text = tokenizer.batch_decode(outputs)[0]
```
## Intended uses & limitations
This is a LoRa adapter fine-tine for phi-2 and not a full fine-tune of the model. Additionally, I did not spend time updating parameters.
## Training and evaluation data
The adapter was fine-tuned on a Google Colab A100 GPU using DPO and the [distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs). The full training notebook can be found [here](https://colab.research.google.com/drive/1PGMj7jlkJaCiSNNihA2NtpILsRgkRXrJ?usp=sharing). Underneath, there are some configs for the adapter and the trainer.
```python
peft_config = LoraConfig(
lora_alpha=16,
lora_dropout=0.5,
r=32,
target_modules=['k_proj', 'q_proj', 'v_proj', 'fc1', 'fc2'],
bias="none",
task_type="CAUSAL_LM",
)
```
```python
training_arguments = TrainingArguments(
output_dir=f"./{model_name}",
evaluation_strategy="steps",
do_eval=True,
optim="paged_adamw_8bit",
per_device_train_batch_size=2,
gradient_accumulation_steps=16,
per_device_eval_batch_size=2,
log_level="debug",
save_steps=20,
logging_steps=20,
learning_rate=1e-5,
eval_steps=20,
num_train_epochs=1, # Modified for tutorial purposes
max_steps=100,
warmup_steps=20,
lr_scheduler_type="linear",
)
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6853 | 0.06 | 20 | 0.6701 | 0.0133 | -0.0368 | 0.6905 | 0.0501 | -212.5803 | -202.2522 | 0.3853 | 0.2532 |
| 0.6312 | 0.12 | 40 | 0.5884 | 0.0422 | -0.2208 | 0.8138 | 0.2630 | -214.4207 | -201.9638 | 0.4254 | 0.2816 |
| 0.547 | 0.19 | 60 | 0.5146 | 0.0172 | -0.5786 | 0.8278 | 0.5958 | -217.9983 | -202.2132 | 0.4699 | 0.3110 |
| 0.4388 | 0.25 | 80 | 0.4893 | -0.0808 | -1.0789 | 0.8293 | 0.9981 | -223.0014 | -203.1934 | 0.5158 | 0.3396 |
| 0.4871 | 0.31 | 100 | 0.4818 | -0.1298 | -1.2346 | 0.8297 | 1.1048 | -224.5586 | -203.6837 | 0.5133 | 0.3340 |
| 0.4863 | 0.37 | 120 | 0.4723 | -0.1230 | -1.1718 | 0.8301 | 1.0488 | -223.9305 | -203.6159 | 0.4910 | 0.3167 |
| 0.4578 | 0.44 | 140 | 0.4666 | -0.1257 | -1.1772 | 0.8301 | 1.0515 | -223.9844 | -203.6428 | 0.4795 | 0.3078 |
| 0.4587 | 0.5 | 160 | 0.4625 | -0.0746 | -1.1272 | 0.8301 | 1.0526 | -223.4841 | -203.1310 | 0.4857 | 0.3139 |
| 0.4688 | 0.56 | 180 | 0.4595 | -0.0584 | -1.1194 | 0.8297 | 1.0610 | -223.4062 | -202.9692 | 0.4890 | 0.3171 |
| 0.4189 | 0.62 | 200 | 0.4579 | -0.0666 | -1.1647 | 0.8297 | 1.0982 | -223.8598 | -203.0511 | 0.4858 | 0.3138 |
| 0.4392 | 0.68 | 220 | 0.4564 | -0.0697 | -1.1915 | 0.8301 | 1.1219 | -224.1278 | -203.0823 | 0.4824 | 0.3110 |
| 0.4659 | 0.75 | 240 | 0.4554 | -0.0826 | -1.2245 | 0.8301 | 1.1419 | -224.4574 | -203.2112 | 0.4761 | 0.3052 |
| 0.4075 | 0.81 | 260 | 0.4544 | -0.0823 | -1.2328 | 0.8301 | 1.1504 | -224.5403 | -203.2089 | 0.4749 | 0.3044 |
| 0.4015 | 0.87 | 280 | 0.4543 | -0.0833 | -1.2590 | 0.8301 | 1.1757 | -224.8026 | -203.2188 | 0.4779 | 0.3067 |
| 0.4365 | 0.93 | 300 | 0.4539 | -0.0846 | -1.2658 | 0.8301 | 1.1812 | -224.8702 | -203.2313 | 0.4780 | 0.3067 |
| 0.4589 | 1.0 | 320 | 0.4537 | -0.0837 | -1.2628 | 0.8301 | 1.1791 | -224.8409 | -203.2228 | 0.4773 | 0.3062 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
dagoberts-revenge/vocal-paws
|
dagoberts-revenge
| 2024-01-25T21:33:53Z | 0 | 1 | null |
[
"conversational",
"en",
"dataset:litagin/moe-speech",
"region:us"
] |
text-generation
| 2024-01-25T21:31:37Z |
---
datasets:
- litagin/moe-speech
language:
- en
pipeline_tag: conversational
---
|
simonycl/data_selection_Llama-2-7b-hf-sharegpt-lora-KMenasMedianDeita-0.05_lora-epoch_4
|
simonycl
| 2024-01-25T21:23:33Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-01-25T21:23:22Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
hpourmodheji/torch-ppo-LunarLander-v2
|
hpourmodheji
| 2024-01-25T21:14:50Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-21T04:18:35Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -152.39 +/- 57.62
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'hpourmodheji/torch-ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Luizinftr/TESTE
|
Luizinftr
| 2024-01-25T21:04:37Z | 0 | 0 | null |
[
"code",
"image-to-3d",
"br",
"dataset:LDJnr/Capybara",
"license:apache-2.0",
"region:us"
] |
image-to-3d
| 2024-01-25T21:01:34Z |
---
license: apache-2.0
datasets:
- LDJnr/Capybara
language:
- br
metrics:
- bertscore
pipeline_tag: image-to-3d
tags:
- code
---
|
zaq-hack/Orion-14B-LongChat-bpw600-h6-exl2
|
zaq-hack
| 2024-01-25T21:01:29Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"orion",
"text-generation",
"code",
"model",
"llm",
"custom_code",
"en",
"zh",
"ja",
"ko",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-25T16:58:27Z |
---
language:
- en
- zh
- ja
- ko
metrics:
- accuracy
pipeline_tag: text-generation
tags:
- code
- model
- llm
---
<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<div align="center">
<img src="./assets/imgs/orion_start.PNG" alt="logo" width="50%" />
</div>
<div align="center">
<h1>
Orion-14B
</h1>
</div>
<div align="center">
<div align="center">
<b>🌐English</b> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-LongChat/blob/main/README_zh.md" target="_blank">🇨🇳中文</a> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-LongChat/blob/main/README_ja.md" target="_blank">🇯🇵日本語</a> | <a href="https://huggingface.co/OrionStarAI/Orion-14B-LongChat/blob/main/README_ko.md" target="_blank">🇰🇷한국어</a>
</div>
<h4 align="center">
<p>
🤗 <a href="https://huggingface.co/OrionStarAI" target="_blank">HuggingFace Mainpage</a> | 🤖 <a href="https://modelscope.cn/organization/OrionStarAI" target="_blank">ModelScope Mainpage</a><br>🎬 <a href="https://huggingface.co/spaces/OrionStarAI/Orion-14B-App-Demo" target="_blank">HuggingFace Demo</a> | 🎫 <a href="https://modelscope.cn/studios/OrionStarAI/Orion-14B-App-Demo/summary" target="_blank">ModelScope Demo</a><br>😺 <a href="https://github.com/OrionStarAI/Orion" target="_blank">GitHub</a><br>📖 <a href="https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf" target="_blank">Tech Report</a>
<p>
</h4>
</div>
# Table of Contents
- [📖 Model Introduction](#model-introduction)
- [🔗 Model Download](#model-download)
- [🔖 Model Benchmark](#model-benchmark)
- [📊 Model Inference](#model-inference)[<img src="./assets/imgs/vllm.png" alt="vllm" height="20"/>](#vllm) [<img src="./assets/imgs/llama_cpp.png" alt="llamacpp" height="20"/>](#llama-cpp)
- [📜 Declarations & License](#declarations-license)
- [🥇 Company Introduction](#company-introduction)
<a name="model-introduction"></a><br>
# 1. Model Introduction
- Orion-14B series models are open-source multilingual large language models trained from scratch by OrionStarAI. The base model is trained on 2.5T multilingual corpus, including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. For details, please refer to [tech report](https://github.com/OrionStarAI/Orion/blob/master/doc/Orion14B_v3.pdf).
- The Orion-14B series models exhibit the following features:
- Among models with 20B-parameter scale level, Orion-14B-Base model shows outstanding performance in comprehensive evaluations.
- Strong multilingual capabilities, significantly outperforming in Japanese and Korean testsets.
- The fine-tuned models demonstrate strong adaptability, excelling in human-annotated blind tests.
- The long-chat version supports extremely long texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k.
- The quantized versions reduce model size by 70%, improve inference speed by 30%, with performance loss less than 1%.
<table style="border-collapse: collapse; width: 100%;">
<tr>
<td style="border: none; padding: 10px; box-sizing: border-box;">
<img src="./assets/imgs/opencompass_en.png" alt="opencompass" style="width: 100%; height: auto;">
</td>
<td style="border: none; padding: 10px; box-sizing: border-box;">
<img src="./assets/imgs/model_cap_en.png" alt="modelcap" style="width: 100%; height: auto;">
</td>
</tr>
</table>
- Orion-14B series models including:
- **Orion-14B-Base:** A multilingual large language foundational model with 14 billion parameters, pretrained on a diverse dataset of 2.5 trillion tokens.
- **Orion-14B-Chat:** A chat-model fine-tuned on a high-quality corpus aims to provide an excellence interactive experience for users in the large model community.
- **Orion-14B-LongChat:** The long-context version excels at handling extremely lengthy texts, performing exceptionally well at a token length of 200k and can support up to a maximum of 320k.
- **Orion-14B-Chat-RAG:** A chat-model fine-tuned on a custom retrieval augmented generation dataset, achieving superior performance in retrieval augmented generation tasks.
- **Orion-14B-Chat-Plugin:** A chat-model specifically tailored for plugin and function calling tasks, ideal for agent-related scenarios where the LLM acts as a plugin and function call system.
- **Orion-14B-Base-Int4:** A quantized base model utilizing 4-bit integer weights. It significantly reduces the model size by 70% and increases the inference speed by 30% while incurring a minimal performance loss of only 1%.
- **Orion-14B-Chat-Int4:** A quantized chat model utilizing 4-bit integer weights.
<a name="model-download"></a><br>
# 2. Model Download
Model release and download links are provided in the table below:
| Model Name | HuggingFace Download Links | ModelScope Download Links |
|-------------------------|-----------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------|
| ⚾Orion-14B-Base | [Orion-14B-Base](https://huggingface.co/OrionStarAI/Orion-14B-Base) | [Orion-14B-Base](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base/summary) |
| 😛Orion-14B-Chat | [Orion-14B-Chat](https://huggingface.co/OrionStarAI/Orion-14B-Chat) | [Orion-14B-Chat](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat/summary) |
| 📃Orion-14B-LongChat | [Orion-14B-LongChat](https://huggingface.co/OrionStarAI/Orion-14B-LongChat) | [Orion-14B-LongChat](https://modelscope.cn/models/OrionStarAI/Orion-14B-LongChat/summary) |
| 🔎Orion-14B-Chat-RAG | [Orion-14B-Chat-RAG](https://huggingface.co/OrionStarAI/Orion-14B-Chat-RAG) | [Orion-14B-Chat-RAG](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-RAG/summary) |
| 🔌Orion-14B-Chat-Plugin | [Orion-14B-Chat-Plugin](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Plugin) | [Orion-14B-Chat-Plugin](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Plugin/summary) |
| 💼Orion-14B-Base-Int4 | [Orion-14B-Base-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Base-Int4) | [Orion-14B-Base-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Base-Int4/summary) |
| 📦Orion-14B-Chat-Int4 | [Orion-14B-Chat-Int4](https://huggingface.co/OrionStarAI/Orion-14B-Chat-Int4) | [Orion-14B-Chat-Int4](https://modelscope.cn/models/OrionStarAI/Orion-14B-Chat-Int4/summary) |
<a name="model-benchmark"></a><br>
# 3. Model Benchmarks
## 3.1. Base Model Orion-14B-Base Benchmarks
### 3.1.1. LLM evaluation results on examination and professional knowledge
| Model | C-Eval | CMMLU | MMLU | AGIEval | Gaokao | BBH |
|--------------------|----------|----------|----------|----------|----------|----------|
| LLaMA2-13B | 41.4 | 38.4 | 55.0 | 30.9 | 18.2 | 45.6 |
| Skywork-13B | 59.1 | 61.4 | 62.7 | 43.6 | 56.1 | 48.3 |
| Baichuan2-13B | 59.0 | 61.3 | 59.5 | 37.4 | 45.6 | 49.0 |
| QWEN-14B | 71.7 | 70.2 | 67.9 | 51.9 | **62.5** | 53.7 |
| InternLM-20B | 58.8 | 59.0 | 62.1 | 44.6 | 45.5 | 52.5 |
| **Orion-14B-Base** | **72.9** | **70.6** | **69.9** | **54.7** | 62.1 | **56.5** |
### 3.1.2. LLM evaluation results on language understanding and common knowledge
| Model |RACE-middle|RACE-high |HellaSwag | PIQA | Lambada | WSC |
|--------------------|----------|----------|----------|----------|----------|----------|
| LLaMA 2-13B | 63.0 | 58.9 | 77.5 | 79.8 | 76.5 | 66.3 |
| Skywork-13B | 87.6 | 84.1 | 73.7 | 78.3 | 71.8 | 66.3 |
| Baichuan 2-13B | 68.9 | 67.2 | 70.8 | 78.1 | 74.1 | 66.3 |
| QWEN-14B | 93.0 | 90.3 | **80.2** | 79.8 | 71.4 | 66.3 |
| InternLM-20B | 86.4 | 83.3 | 78.1 | **80.3** | 71.8 | 68.3 |
| **Orion-14B-Base** | **93.2** | **91.3** | 78.5 | 79.5 | **78.8** | **70.2** |
### 3.1.3. LLM evaluation results of OpenCompass testsets
| Model | Average | Examination | Language | Knowledge | Understanding | Reasoning |
|------------------|----------|----------|----------|----------|----------|----------|
| LLaMA 2-13B | 47.3 | 45.2 | 47.0 | 58.3 | 50.9 | 43.6 |
| Skywork-13B | 53.6 | 61.1 | 51.3 | 52.7 | 64.5 | 45.2 |
| Baichuan 2-13B | 49.4 | 51.8 | 47.5 | 48.9 | 58.1 | 44.2 |
| QWEN-14B | 62.4 | 71.3 | 52.67 | 56.1 | 68.8 | 60.1 |
| InternLM-20B | 59.4 | 62.5 | 55.0 | **60.1** | 67.3 | 54.9 |
|**Orion-14B-Base**| **64.3** | **71.4** | **55.0** | 60.0 | **71.9** | **61.6** |
### 3.1.4. Comparison of LLM performances on Japanese testsets
| Model |**Average**| JCQA | JNLI | MARC | JSQD | JQK | XLS | XWN | MGSM |
|--------------------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
| PLaMo-13B | 52.3 | 56.7 | 42.8 | 95.8 | 70.6 | 71.0 | 8.70 | 70.5 | 2.40 |
| WebLab-10B | 50.7 | 66.6 | 53.7 | 82.1 | 62.9 | 56.2 | 10.0 | 72.0 | 2.40 |
| ELYZA-jp-7B | 48.8 | 71.7 | 25.3 | 86.6 | 70.8 | 64.1 | 2.50 | 62.1 | 7.20 |
| StableLM-jp-7B | 51.1 | 33.4 | 43.3 | **96.7** | 70.6 | 78.1 | 10.7 | 72.8 | 2.80 |
| LLaMA 2-13B | 46.3 | 75.0 | 47.6 | 38.8 | 76.1 | 67.7 | 18.1 | 63.2 | 10.4 |
| Baichuan 2-13B | 57.1 | 73.7 | 31.3 | 91.6 | 80.5 | 63.3 | 18.6 | 72.2 | 25.2 |
| QWEN-14B | 65.8 | 85.9 | 60.7 | 97.0 | 83.3 | 71.8 | 18.8 | 70.6 | 38.0 |
| Yi-34B | 67.1 | 83.8 | 61.2 | 95.2 | **86.1** | 78.5 | **27.2** | 69.2 | 35.2 |
| **Orion-14B-Base** | **69.1** | **88.2** | **75.8** | 94.1 | 75.7 | **85.1** | 17.3 | **78.8** | **38.0** |
### 3.1.5. Comparison of LLM performances on Korean testsets. n = 0 and n = 5 stand for n-shot prompts used in the evaluation
|Model | **Average**<br>n=0 n=5 | HellaSwag<br>n=0 n=5 | COPA<br> n=0 n=5 | BooIQ<br>n=0 n=5 | SentiNeg<br>n=0 n=5|
|------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
| KoGPT | 53.0 70.1 | 55.9 58.3 | 73.5 72.9 | 45.1 59.8 | 37.5 89.4 |
| Polyglot-ko-13B | 69.6 73.7 |**59.5** **63.1**|**79.4** **81.1**| 48.2 60.4 | 91.2 90.2 |
| LLaMA 2-13B | 46.7 63.7 | 41.3 44.0 | 59.3 63.8 | 34.9 73.8 | 51.5 73.4 |
| Baichuan 2-13B | 52.1 58.7 | 39.2 39.6 | 60.6 60.6 | 58.4 61.5 | 50.3 72.9 |
| QWEN-14B | 53.8 73.7 | 45.3 46.8 | 64.9 68.9 | 33.4 83.5 | 71.5 95.7 |
| Yi-34B | 54.2 72.1 | 44.6 44.7 | 58.0 60.6 | 65.9 90.2 | 48.3 92.9 |
|**Orion-14B-Chat**|**74.5** **79.6**| 47.0 49.6 | 77.7 79.4 |**81.6** **90.7**|**92.4** **98.7**|
### 3.1.6. Multilingual evaluation
| Model | Train Lang | Japanese | Korean | Chinese | English |
|--------------------|------------|----------|----------|----------|----------|
| PLaMo-13B | En,Jp | 52.3 | * | * | * |
| Weblab-10B | En,Jp | 50.7 | * | * | * |
| ELYZA-jp-7B | En,Jp | 48.8 | * | * | * |
| StableLM-jp-7B | En,Jp | 51.1 | * | * | * |
| KoGPT-6B | En,Ko | * | 70.1 | * | * |
| Polyglot-ko-13B | En,Ko | * | 70.7 | * | * |
| Baichuan2-13B | Multi | 57.1 | 58.7 | 50.8 | 57.1 |
| Qwen-14B | Multi | 65.8 | 73.7 | 64.5 | 65.4 |
| Llama2-13B | Multi | 46.3 | 63.7 | 41.4 | 55.3 |
| Yi-34B | Multi | 67.1 | 72.2 | 58.7 | **68.8** |
| **Orion-14B-Chat** | Multi | **69.1** | **79.5** | **67.9** | 67.3 |
## 3.2. Chat Model Orion-14B-Chat Benchmarks
### 3.2.1. Chat model subjective evaluation of MTBench
| Model | First-Turn | Second-Turn | **Average** |
|----------------------|----------|----------|----------|
| Baichuan2-13B-Chat | 7.05 | 6.47 | 6.76 |
| Qwen-14B-Chat | 7.30 | 6.62 | 6.96 |
| Llama2-13B-Chat | 7.10 | 6.20 | 6.65 |
| InternLM-20B-Chat | 7.03 | 5.93 | 6.48 |
| **Orion-14B-Chat** | **7.68** | **7.07** | **7.37** |
\* use vllm for inference
### 3.2.2. Chat model subjective evaluation of AlignBench
| Model | Math. | Logi. | Basic. | Chi. | Comp. | Writ. | Role. | Prof. |**Avg.**|
|--------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|
| Baichuan2-13B-Chat | 3.76 | 4.07 | 6.22 | 6.05 | 7.11 | 6.97 | 6.75 | 6.43 | 5.25 |
| Qwen-14B-Chat |**4.91**|**4.71**|**6.90**| 6.36 | 6.74 | 6.64 | 6.59 | 6.56 |**5.72**|
| Llama2-13B-Chat | 3.05 | 3.79 | 5.43 | 4.40 | 6.76 | 6.63 | 6.99 | 5.65 | 4.70 |
| InternLM-20B-Chat | 3.39 | 3.92 | 5.96 | 5.50 |**7.18**| 6.19 | 6.49 | 6.22 | 4.96 |
| **Orion-14B-Chat** | 4.00 | 4.24 | 6.18 |**6.57**| 7.16 |**7.36**|**7.16**|**6.99**| 5.51 |
\* use vllm for inference
## 3.3. LongChat Model Orion-14B-LongChat Benchmarks
### 3.3.1. LongChat evaluation of LongBench
| Model | NarrativeQA|MultiFieldQA-en|MultiFieldQA-zh| DuReader | QMSum | VCSUM | TREC | TriviaQA | LSHT |RepoBench-P|
|--------------------------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|-----------|
| GPT-3.5-Turbo-16k | **23.60** | **52.30** | **61.20** | 28.70 | 23.40 | **16.00** | 68.00 | **91.40** | 29.20 | 53.60 |
| LongChat-v1.5-7B-32k | 16.90 | 41.40 | 29.10 | 19.50 | 22.70 | 9.90 | 63.50 | 82.30 | 23.20 | 55.30 |
| Vicuna-v1.5-7B-16k | 19.40 | 38.50 | 43.00 | 19.30 | 22.80 | 15.10 | 71.50 | 86.20 | 28.80 | 43.50 |
| Yi-6B-200K | 14.11 | 36.74 | 22.68 | 14.01 | 20.44 | 8.08 | 72.00 | 86.61 | 38.00 | **63.29** |
| Orion-14B-LongChat | 19.47 | 48.11 | 55.84 | **37.02** | **24.87** | 15.44 | **77.00** | 89.12 | **45.50** | 54.31 |
## 3.4. Chat RAG Model Benchmarks
### 3.4.1. LLM evaluation results of self-built RAG testsets
|Model|Effectiveness of Response(Keyword)|*Effectiveness of Response（subjective evaluation）|Quoting Ability|Fallback Ability|*AutoQA|*Data Extraction|
|---------------------|------|------|------|------|------|------|
| Baichuan2-13B-Chat | 85 | 76 | 1 | 0 | 69 | 51 |
| Qwen-14B-Chat | 79 | 77 | 75 | 47 | 68 | 72 |
| Qwen-72B-Chat(Int4) | 87 | 89 | 90 | 32 | 67 | 76 |
| GPT-4 | 91 | 94 | 96 | 95 | 75 | 86 |
| Orion-14B-Chat-RAG | 86 | 87 | 91 | 97 | 73 | 71 |
\* means manual assessment
## 3.5. Chat Plugin Model Orion-14B-Chat-Plugin Benchmarks
### 3.5.1. LLM evaluation results of self-built plugin testsets
|Model |Intent Recognition with Full Params |Intent Recognition with Missing Params |Non-Plugin Invocation Recognition |
|-----------------------|--------|-----------|--------|
| Baichuan2-13B-Chat | 25 | 0 | 0 |
| Qwen-14B-Chat | 55 | 0 | 50 |
| GPT-4 | **95** | 52.38 | 70 |
| Orion-14B-Chat-Plugin | 92.5 | **60.32** | **90** |
## 3.6. Quantized Model Orion-14B-Base-Int4 Benchmarks
### 3.6.1. Comparison of before and after quantization
|Model |Size(GB)|Inference Speed(tokens/s)|C-Eval|CMMLU|MMLU|RACE|HellaSwag|
|-------------------------|-------|-----|------|------|------|------|------|
| OrionStar-14B-Base | 28.0 | 135 | 72.8 | 70.6 | 70.0 | 93.3 | 78.5 |
| OrionStar-14B-Base-Int4 | 8.3 | 178 | 71.8 | 69.8 | 69.2 | 93.1 | 78.0 |
<a name="model-inference"></a><br>
# 4. Model Inference
Model weights, source code, and configuration needed for inference are published on Hugging Face, and the download link
is available in the table at the beginning of this document. We demonstrate various inference methods here, and the
program will automatically download the necessary resources from Hugging Face.
## 4.1. Python Code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
tokenizer = AutoTokenizer.from_pretrained("OrionStarAI/Orion-14B", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("OrionStarAI/Orion-14B", device_map="auto",
torch_dtype=torch.bfloat16, trust_remote_code=True)
model.generation_config = GenerationConfig.from_pretrained("OrionStarAI/Orion-14B")
messages = [{"role": "user", "content": "Hello, what is your name? "}]
response = model.chat(tokenizer, messages, streaming=False)
print(response)
```
In the above Python code, the model is loaded with `device_map='auto'` to utilize all available GPUs. To specify the
device, you can use something like `export CUDA_VISIBLE_DEVICES=0,1` (using GPUs 0 and 1).
## 4.2. Command Line Tool
```shell
CUDA_VISIBLE_DEVICES=0 python cli_demo.py
```
This command-line tool is designed for chat scenarios, and thus, it does not support calling the base model.
## 4.3. Direct Script Inference
```shell
# base model
CUDA_VISIBLE_DEVICES=0 python demo/text_generation_base.py --model OrionStarAI/Orion-14B --tokenizer OrionStarAI/Orion-14B --prompt hello
# chat model
CUDA_VISIBLE_DEVICES=0 python demo/text_generation.py --model OrionStarAI/Orion-14B-Chat --tokenizer OrionStarAI/Orion-14B-Chat --prompt hi
```
<a name="vllm"></a><br>
## 4.4. Inference by vllm
- Project URL<br>
https://github.com/vllm-project/vllm
- Pull Request<br>
https://github.com/vllm-project/vllm/pull/2539
<a name="llama-cpp"></a><br>
## 4.5. Inference by llama.cpp
- Project URL<br>
https://github.com/ggerganov/llama.cpp
- Pull Request<br>
https://github.com/ggerganov/llama.cpp/pull/5118
- How to convert to GGUF model
```shell
python convert-hf-to-gguf.py path/to/Orion-14B-Chat --outfile chat.gguf
```
- How to run generation
```shell
./main --frequency-penalty 0.5 --frequency-penalty 0.5 --top-k 5 --top-p 0.9 -m chat.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
```
## 4.6 Example Output
### 4.6.1. Casual Chat
`````
User: Hello
Orion-14B: Hello! How can I help you today? If you have any questions or need assistance, feel free to ask.
User: What functions do you have?
Orion-14B: As an AI language model, I have a wide range of functions and capabilities, including but not limited to:
Answering general knowledge questions: I can provide information on various topics such as history, science, geography, and more.
Providing definitions and explanations: I can explain complex concepts and provide definitions for words and phrases.
Assisting with tasks: I can help with simple tasks like setting reminders, converting units of measurement, and even ordering food from restaurants.
Generating text: I can write short stories, essays, articles, and other types of content based on prompts given to me.
Translating languages: I can translate text from one language to another.
Chatting: I can engage in conversation with users and respond to their queries in a conversational manner.
Summarizing long texts: I can summarize longer texts like articles, books, and research papers.
Generating random numbers and names: I can generate random numbers within a specified range and also suggest names based on certain criteria.
Playing games: I can play simple games like Hangman, Trivia, and more.
Providing recommendations: I can recommend movies, music, books, and other forms of entertainment based on user preferences.
These are just some of the many functions that I am capable of performing. If you have a specific task in mind, feel free to ask me about it.
User: Tell me a joke.
Orion-14B: Sure, here's a classic one-liner: Why don't scientists trust atoms? Because they make up everything.
`````
### 4.6.2. Japanese & Korean Chat
`````
User：自己を紹介してください
Orion-14B：こんにちは、私の名前はChatMaxで、OrionStarによって開発されたAIアシスタントです。私はユーザーに正確で、客観的で、有用な回答を提供し、彼らが問題を解決し、情報を取得するのを助けることができます。
User：자기소개를 해주세요.
Orion-14B：안녕하세요, 제 이름은 ChatMax입니다. OrionStar에서 개발한 AI 어시스턴트입니다. 저는 사용자들에게 정확하고 객관적이며 유용한 답변을 제공하여 문제를 해결하고 정보를 얻는 데 도움을 줄 수 있습니다.
`````
<a name="declarations-license"></a><br>
# 5. Declarations, License
## 5.1. Declarations
We strongly urge all users not to use the Orion-14B model for any activities that may harm national or social security or violate the law.
Additionally, we request users not to use the Orion-14B model for internet services without proper security review and filing.
We hope all users abide by this principle to ensure that technological development takes place in a regulated and legal environment.
We have done our best to ensure the compliance of the data used in the model training process. However, despite our
significant efforts, unforeseen issues may still arise due to the complexity of the model and data. Therefore, if any
problems arise due to the use of the Orion-14B open-source model, including but not limited to data security
issues, public opinion risks, or any risks and issues arising from the model being misled, abused, disseminated, or
improperly utilized, we will not assume any responsibility.
## 5.2. License
Community use of the Orion-14B series models
- For code, please comply with [Apache License Version 2.0](./LICENSE)<br>
- For model, please comply with [【Orion-14B Series】 Models Community License Agreement](./ModelsCommunityLicenseAgreement)
<a name="company-introduction"></a><br>
# 6. Company Introduction
OrionStar is a leading global service robot solutions company, founded in September 2016. OrionStar is dedicated to
using artificial intelligence technology to create the next generation of revolutionary robots, allowing people to break
free from repetitive physical labor and making human work and life more intelligent and enjoyable. Through technology,
OrionStar aims to make society and the world a better place.
OrionStar possesses fully self-developed end-to-end artificial intelligence technologies, such as voice interaction and
visual navigation. It integrates product development capabilities and technological application capabilities. Based on
the Orion robotic arm platform, it has launched products such as OrionStar AI Robot Greeting, AI Robot Greeting Mini,
Lucki, Coffee Master, and established the open platform OrionOS for Orion robots. Following the philosophy of "Born for
Truly Useful Robots", OrionStar empowers more people through AI technology.
**The core strengths of OrionStar lies in possessing end-to-end AI application capabilities,** including big data preprocessing, large model pretraining, fine-tuning, prompt engineering, agent, etc. With comprehensive end-to-end model training capabilities, including systematic data processing workflows and the parallel model training capability of hundreds of GPUs, it has been successfully applied in various industry scenarios such as government affairs, cloud services, international e-commerce, and fast-moving consumer goods.
Companies with demands for deploying large-scale model applications are welcome to contact us.<br>
**Enquiry Hotline: 400-898-7779**<br>
**E-mail: ai@orionstar.com**
<div align="center">
<img src="./assets/imgs/wechat_group.jpg" alt="wechat" width="40%" />
</div>
|
GLaDOSIsHere/Under_testing_models
|
GLaDOSIsHere
| 2024-01-25T21:01:11Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2024-01-25T20:49:08Z |
---
license: openrail
---
This "new model" is for Voice Models that need to be tested, and trained depending on the situation... Or just abandon them for months and come back.
|
melll-uff/pt-br_diffcse
|
melll-uff
| 2024-01-25T20:52:40Z | 18 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"pt",
"dataset:loremipsum3658/sick-br",
"dataset:assin",
"dataset:assin2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-01-25T20:37:00Z |
---
license: apache-2.0
datasets:
- loremipsum3658/sick-br
- assin
- assin2
language:
- pt
library_name: transformers
---
# Pt-br DiffCSE
## Usage: https://github.com/danieljunior/PtBr-DiffCSE
|
FurongZou/distilbert-base-uncased-finetuned-ner
|
FurongZou
| 2024-01-25T20:51:34Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-25T20:14:58Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-ner
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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0841
- eval_precision: 0.8833
- eval_recall: 0.9042
- eval_f1: 0.8936
- eval_accuracy: 0.9762
- eval_runtime: 115.4849
- eval_samples_per_second: 28.142
- eval_steps_per_second: 1.766
- epoch: 0.57
- step: 500
## 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: 3
### Framework versions
- Transformers 4.37.1
- Pytorch 2.0.0+cpu
- Datasets 2.16.1
- Tokenizers 0.15.1
|
zehralx/qa_model
|
zehralx
| 2024-01-25T20:41:52Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-25T20:41:38Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_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_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8829
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0766 | 1.0 | 1318 | 0.9572 |
| 0.8205 | 2.0 | 2636 | 0.8743 |
| 0.6698 | 3.0 | 3954 | 0.8829 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
melll-uff/pt-br_simcse
|
melll-uff
| 2024-01-25T20:41:38Z | 44 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"pt",
"dataset:loremipsum3658/sick-br",
"dataset:assin",
"dataset:assin2",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-01-25T20:34:43Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- loremipsum3658/sick-br
- assin
- assin2
license: apache-2.0
language:
- pt
library_name: sentence-transformers
---
# Pt-br SimCSE
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers): https://www.sbert.net/examples/unsupervised_learning/SimCSE/README.html
|
ptoro/EvolCodeLlama-7b
|
ptoro
| 2024-01-25T20:38:16Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-01-25T19:16:14Z |
---
license: llama2
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: EvolCodeLlama-7b
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. -->
[<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)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: codellama/CodeLlama-7b-hf
base_model_config: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true
hub_model_id: EvolCodeLlama-7b
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: ptoro/Evol-Instruct-Python-1k-testing
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.02
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 2048
sample_packing: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
</details><br>
# EvolCodeLlama-7b
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3828
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3627 | 0.01 | 1 | 0.5027 |
| 0.3412 | 0.03 | 4 | 0.5026 |
| 0.3806 | 0.07 | 8 | 0.5023 |
| 0.392 | 0.1 | 12 | 0.5018 |
| 0.4141 | 0.14 | 16 | 0.4999 |
| 0.3433 | 0.17 | 20 | 0.4954 |
| 0.3702 | 0.21 | 24 | 0.4851 |
| 0.2948 | 0.24 | 28 | 0.4682 |
| 0.3387 | 0.28 | 32 | 0.4499 |
| 0.2437 | 0.31 | 36 | 0.4331 |
| 0.2526 | 0.35 | 40 | 0.4221 |
| 0.2721 | 0.38 | 44 | 0.4146 |
| 0.2292 | 0.42 | 48 | 0.4089 |
| 0.1986 | 0.45 | 52 | 0.4028 |
| 0.3258 | 0.48 | 56 | 0.3983 |
| 0.3509 | 0.52 | 60 | 0.3950 |
| 0.2697 | 0.55 | 64 | 0.3926 |
| 0.2646 | 0.59 | 68 | 0.3907 |
| 0.3979 | 0.62 | 72 | 0.3900 |
| 0.2737 | 0.66 | 76 | 0.3880 |
| 0.2271 | 0.69 | 80 | 0.3865 |
| 0.247 | 0.73 | 84 | 0.3847 |
| 0.3112 | 0.76 | 88 | 0.3824 |
| 0.2724 | 0.8 | 92 | 0.3820 |
| 0.207 | 0.83 | 96 | 0.3814 |
| 0.3492 | 0.87 | 100 | 0.3810 |
| 0.2474 | 0.9 | 104 | 0.3802 |
| 0.4037 | 0.94 | 108 | 0.3785 |
| 0.2295 | 0.97 | 112 | 0.3773 |
| 0.2689 | 1.0 | 116 | 0.3760 |
| 0.2546 | 1.02 | 120 | 0.3753 |
| 0.1916 | 1.05 | 124 | 0.3768 |
| 0.2458 | 1.09 | 128 | 0.3758 |
| 0.2155 | 1.12 | 132 | 0.3768 |
| 0.2341 | 1.16 | 136 | 0.3773 |
| 0.1909 | 1.19 | 140 | 0.3793 |
| 0.1911 | 1.23 | 144 | 0.3759 |
| 0.2096 | 1.26 | 148 | 0.3761 |
| 0.2353 | 1.29 | 152 | 0.3772 |
| 0.2606 | 1.33 | 156 | 0.3773 |
| 0.1485 | 1.36 | 160 | 0.3778 |
| 0.1807 | 1.4 | 164 | 0.3749 |
| 0.2294 | 1.43 | 168 | 0.3770 |
| 0.216 | 1.47 | 172 | 0.3759 |
| 0.1791 | 1.5 | 176 | 0.3727 |
| 0.2605 | 1.54 | 180 | 0.3733 |
| 0.2838 | 1.57 | 184 | 0.3738 |
| 0.2632 | 1.61 | 188 | 0.3694 |
| 0.1839 | 1.64 | 192 | 0.3686 |
| 0.1939 | 1.68 | 196 | 0.3690 |
| 0.2413 | 1.71 | 200 | 0.3699 |
| 0.1494 | 1.74 | 204 | 0.3689 |
| 0.2782 | 1.78 | 208 | 0.3695 |
| 0.2314 | 1.81 | 212 | 0.3696 |
| 0.2499 | 1.85 | 216 | 0.3691 |
| 0.1976 | 1.88 | 220 | 0.3672 |
| 0.2587 | 1.92 | 224 | 0.3660 |
| 0.2598 | 1.95 | 228 | 0.3658 |
| 0.2686 | 1.99 | 232 | 0.3666 |
| 0.216 | 2.01 | 236 | 0.3673 |
| 0.1261 | 2.04 | 240 | 0.3723 |
| 0.1938 | 2.08 | 244 | 0.3811 |
| 0.1906 | 2.11 | 248 | 0.3869 |
| 0.1375 | 2.15 | 252 | 0.3829 |
| 0.228 | 2.18 | 256 | 0.3796 |
| 0.2524 | 2.22 | 260 | 0.3789 |
| 0.118 | 2.25 | 264 | 0.3809 |
| 0.2224 | 2.29 | 268 | 0.3834 |
| 0.1477 | 2.32 | 272 | 0.3847 |
| 0.2095 | 2.35 | 276 | 0.3849 |
| 0.1919 | 2.39 | 280 | 0.3820 |
| 0.1916 | 2.42 | 284 | 0.3804 |
| 0.1625 | 2.46 | 288 | 0.3788 |
| 0.2054 | 2.49 | 292 | 0.3794 |
| 0.1605 | 2.53 | 296 | 0.3810 |
| 0.1564 | 2.56 | 300 | 0.3819 |
| 0.196 | 2.6 | 304 | 0.3822 |
| 0.1975 | 2.63 | 308 | 0.3830 |
| 0.1406 | 2.67 | 312 | 0.3833 |
| 0.2754 | 2.7 | 316 | 0.3830 |
| 0.1544 | 2.74 | 320 | 0.3829 |
| 0.1733 | 2.77 | 324 | 0.3830 |
| 0.1862 | 2.81 | 328 | 0.3832 |
| 0.1634 | 2.84 | 332 | 0.3829 |
| 0.1966 | 2.87 | 336 | 0.3830 |
| 0.1306 | 2.91 | 340 | 0.3831 |
| 0.1444 | 2.94 | 344 | 0.3828 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Dallyana/daya24mile_asr
|
Dallyana
| 2024-01-25T20:33:56Z | 2 | 0 |
espnet
|
[
"espnet",
"automatic-speech-recognition",
"ja",
"dataset:reazon-research/reazonspeech",
"license:apache-2.0",
"region:us"
] |
automatic-speech-recognition
| 2024-01-25T19:58:11Z |
---
license: apache-2.0
datasets:
- reazon-research/reazonspeech
language:
- ja
library_name: espnet
tags:
- automatic-speech-recognition
---
# reazonspeech-espnet-v1
`reazonspeech-espnet-v1` is an ESPnet model trained for Japanese automatic speech recognition (ASR).
- This model was trained on 15,000 hours of ReazonSpeech corpus.
- Make sure that your audio file is sampled at 16khz when using this model.
For more details, please visit [the official project page.](https://research.reazon.jp/projects/ReazonSpeech/)
|
swapandeep39/dogbooth
|
swapandeep39
| 2024-01-25T20:28:15Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:finetune:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-25T19:10:03Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - swapandeep39/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
|
cezeozue/distilbert-base-uncased-finetuned-clinc
|
cezeozue
| 2024-01-25T20:23:25Z | 11 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-23T20:04:17Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
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. -->
# distilbert-base-uncased-finetuned-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7730
- Accuracy: 0.9161
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 318 | 3.2776 | 0.7287 |
| 3.7835 | 2.0 | 636 | 1.8647 | 0.8358 |
| 3.7835 | 3.0 | 954 | 1.1524 | 0.8977 |
| 1.6878 | 4.0 | 1272 | 0.8547 | 0.9129 |
| 0.8994 | 5.0 | 1590 | 0.7730 | 0.9161 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
jeiku/RPGodzilla_3.43B
|
jeiku
| 2024-01-25T20:16:27Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm_epoch",
"text-generation",
"mergekit",
"merge",
"conversational",
"custom_code",
"arxiv:2203.05482",
"base_model:jeiku/Bluemoon_cleaned_StableLM",
"base_model:merge:jeiku/Bluemoon_cleaned_StableLM",
"base_model:jeiku/Everything_v3_128_StableLM",
"base_model:merge:jeiku/Everything_v3_128_StableLM",
"base_model:jeiku/LimaRP_StableLM",
"base_model:merge:jeiku/LimaRP_StableLM",
"base_model:jeiku/No_Robots_Alpaca_StableLM",
"base_model:merge:jeiku/No_Robots_Alpaca_StableLM",
"base_model:jeiku/PIPPA_128_StableLM",
"base_model:merge:jeiku/PIPPA_128_StableLM",
"base_model:jeiku/RPGPT_StableLM",
"base_model:merge:jeiku/RPGPT_StableLM",
"base_model:jeiku/Theory_of_Mind_128_StableLM",
"base_model:merge:jeiku/Theory_of_Mind_128_StableLM",
"base_model:jeiku/Theory_of_Mind_RP_128_StableLM",
"base_model:merge:jeiku/Theory_of_Mind_RP_128_StableLM",
"base_model:jeiku/Toxic_DPO_StableLM",
"base_model:merge:jeiku/Toxic_DPO_StableLM",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-01-25T20:04:11Z |
---
base_model:
- jeiku/Bluemoon_cleaned_StableLM
- jeiku/Everything_v3_128_StableLM
- jeiku/Theory_of_Mind_128_StableLM
- jeiku/Theory_of_Mind_RP_128_StableLM
- jeiku/LimaRP_StableLM
- jeiku/No_Robots_Alpaca_StableLM
- jeiku/RPGPT_StableLM
- jeiku/Toxic_DPO_StableLM
- jeiku/PIPPA_128_StableLM
- jeiku/Gnosis_StableLM
tags:
- mergekit
- merge
---
# snek
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* snek + [jeiku/Bluemoon_cleaned_StableLM](https://huggingface.co/jeiku/Bluemoon_cleaned_StableLM)
* snek + [jeiku/Everything_v3_128_StableLM](https://huggingface.co/jeiku/Everything_v3_128_StableLM)
* snek + [jeiku/Theory_of_Mind_128_StableLM](https://huggingface.co/jeiku/Theory_of_Mind_128_StableLM)
* snek + [jeiku/Theory_of_Mind_RP_128_StableLM](https://huggingface.co/jeiku/Theory_of_Mind_RP_128_StableLM)
* snek + [jeiku/LimaRP_StableLM](https://huggingface.co/jeiku/LimaRP_StableLM)
* snek + [jeiku/No_Robots_Alpaca_StableLM](https://huggingface.co/jeiku/No_Robots_Alpaca_StableLM)
* snek + [jeiku/RPGPT_StableLM](https://huggingface.co/jeiku/RPGPT_StableLM)
* snek + [jeiku/Toxic_DPO_StableLM](https://huggingface.co/jeiku/Toxic_DPO_StableLM)
* snek + [jeiku/PIPPA_128_StableLM](https://huggingface.co/jeiku/PIPPA_128_StableLM)
* snek + [jeiku/Gnosis_StableLM](https://huggingface.co/jeiku/Gnosis_StableLM)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: linear
models:
- model: snek+jeiku/Theory_of_Mind_128_StableLM
parameters:
weight: 1
- model: snek+jeiku/Everything_v3_128_StableLM
parameters:
weight: 1
- model: snek+jeiku/Gnosis_StableLM
parameters:
weight: 1
- model: snek+jeiku/Toxic_DPO_StableLM
parameters:
weight: 1
- model: snek+jeiku/No_Robots_Alpaca_StableLM
parameters:
weight: 1
- model: snek+jeiku/Theory_of_Mind_RP_128_StableLM
parameters:
weight: 1
- model: snek+jeiku/Bluemoon_cleaned_StableLM
parameters:
weight: 1
- model: snek+jeiku/RPGPT_StableLM
parameters:
weight: 1
- model: snek+jeiku/LimaRP_StableLM
parameters:
weight: 1
- model: snek+jeiku/PIPPA_128_StableLM
parameters:
weight: 1
dtype: float16
```
|
itsdhanoob/pixelcopter
|
itsdhanoob
| 2024-01-25T19:50:58Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-25T19:50:45Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 43.50 +/- 38.15
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
|
asun17904/imdb-t5-base-a2b2
|
asun17904
| 2024-01-25T19:32:09Z | 1 | 0 |
pytorch
|
[
"pytorch",
"t5",
"en",
"license:mit",
"region:us"
] | null | 2024-01-25T06:26:05Z |
---
language: en
license: mit
library_name: pytorch
---
# Knowledge Continuity Regularized Network
Trainer Hyperparameters:
- `lr` = 5e-05
- `per_device_batch_size` = 8
- `gradient_accumulation_steps` = 2
- `weight_decay` = 1e-09
- `seed` = 42
Regularization Hyperparameters
- `numerical stability denominator constant` = 0.01
- `lambda` = 0.02
- `alpha` = 2.0
- `beta` = 2.0
Extended Logs:
|eval_loss|eval_accuracy|epoch|
|--|--|--|
|11.926|0.939|1.0|
|11.887|0.941|2.0|
|11.913|0.941|3.0|
|11.912|0.940|4.0|
|11.878|0.942|5.0|
|11.784|0.945|6.0|
|11.718|0.947|7.0|
|11.742|0.946|8.0|
|11.763|0.945|9.0|
|11.736|0.946|10.0|
|11.728|0.946|11.0|
|11.790|0.944|12.0|
|11.811|0.944|13.0|
|11.784|0.945|14.0|
|11.894|0.941|15.0|
|11.739|0.946|16.0|
|11.722|0.947|17.0|
|11.959|0.940|18.0|
|11.813|0.944|19.0|
|11.733|0.946|20.0|
|11.687|0.948|21.0|
|11.707|0.947|22.0|
|11.700|0.947|23.0|
|11.691|0.948|24.0|
|11.827|0.943|25.0|
|11.720|0.947|26.0|
|11.698|0.947|27.0|
|11.692|0.948|28.0|
|11.707|0.947|29.0|
|11.706|0.947|30.0|
|11.711|0.947|31.0|
|11.721|0.947|32.0|
|11.687|0.948|33.0|
|11.728|0.946|34.0|
|11.678|0.948|35.0|
|11.691|0.948|36.0|
|11.692|0.948|37.0|
|11.684|0.948|38.0|
|11.743|0.946|39.0|
|11.631|0.950|40.0|
|11.707|0.947|41.0|
|11.652|0.949|42.0|
|11.688|0.948|43.0|
|11.647|0.949|44.0|
|11.639|0.949|45.0|
|11.660|0.949|46.0|
|11.652|0.949|47.0|
|11.648|0.949|48.0|
|11.652|0.949|49.0|
|
alirzb/SeizureClassifier_Wav2Vec_43243498
|
alirzb
| 2024-01-25T19:27:54Z | 146 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-01-25T18:44:33Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SeizureClassifier_Wav2Vec_43243498
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. -->
# SeizureClassifier_Wav2Vec_43243498
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0520
- Accuracy: 0.9901
## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1121 | 0.99 | 44 | 0.9842 | 0.8144 |
| 0.6303 | 1.99 | 88 | 0.5874 | 0.8861 |
| 0.4605 | 2.98 | 132 | 0.3826 | 0.9406 |
| 0.323 | 4.0 | 177 | 0.2791 | 0.9530 |
| 0.2435 | 4.99 | 221 | 0.3828 | 0.8688 |
| 0.2354 | 5.99 | 265 | 0.1321 | 0.9752 |
| 0.2491 | 6.98 | 309 | 0.1552 | 0.9653 |
| 0.1116 | 8.0 | 354 | 0.1540 | 0.9579 |
| 0.0934 | 8.99 | 398 | 0.1053 | 0.9827 |
| 0.0774 | 9.99 | 442 | 0.1016 | 0.9777 |
| 0.0553 | 10.98 | 486 | 0.1856 | 0.9530 |
| 0.0368 | 12.0 | 531 | 0.1151 | 0.9728 |
| 0.017 | 12.99 | 575 | 0.0516 | 0.9876 |
| 0.0153 | 13.99 | 619 | 0.0540 | 0.9901 |
| 0.0144 | 14.92 | 660 | 0.0520 | 0.9901 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
xuykin/va-er
|
xuykin
| 2024-01-25T19:13:16Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-25T19:10:01Z |
---
license: creativeml-openrail-m
---
|
manhvh2601/whisper_data_self_tiny
|
manhvh2601
| 2024-01-25T19:07:57Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"VHM20191956-SpeechToText",
"generated_from_trainer",
"vi",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-23T15:00:59Z |
---
language:
- vi
license: apache-2.0
base_model: openai/whisper-small
tags:
- VHM20191956-SpeechToText
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Vietnamese Vu Hoang Manh
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 Vietnamese Vu Hoang Manh
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Vietnamese ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0549
- Wer: 8.9064
## 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: 0.0003
- 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: 50
- training_steps: 5250
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1693 | 1.0 | 105 | 0.1621 | 224.6900 |
| 0.1317 | 2.0 | 210 | 0.0764 | 26.2683 |
| 0.0868 | 3.0 | 315 | 0.1650 | 26.3247 |
| 0.0645 | 4.0 | 420 | 0.0592 | 43.7993 |
| 0.0578 | 5.0 | 525 | 0.0755 | 41.0372 |
| 0.0342 | 6.0 | 630 | 0.0640 | 29.2559 |
| 0.0325 | 7.0 | 735 | 0.0514 | 13.1342 |
| 0.0397 | 8.0 | 840 | 0.0587 | 37.8241 |
| 0.0307 | 9.0 | 945 | 0.0628 | 32.1308 |
| 0.0215 | 10.0 | 1050 | 0.0742 | 23.8444 |
| 0.0268 | 11.0 | 1155 | 0.0710 | 35.6257 |
| 0.009 | 12.0 | 1260 | 0.0539 | 11.9504 |
| 0.0084 | 13.0 | 1365 | 0.0705 | 16.2345 |
| 0.0158 | 14.0 | 1470 | 0.0489 | 20.6877 |
| 0.0122 | 15.0 | 1575 | 0.0582 | 10.3720 |
| 0.0139 | 16.0 | 1680 | 0.0620 | 17.1928 |
| 0.0121 | 17.0 | 1785 | 0.0734 | 16.0654 |
| 0.0055 | 18.0 | 1890 | 0.0719 | 32.6381 |
| 0.0044 | 19.0 | 1995 | 0.0614 | 83.5964 |
| 0.0083 | 20.0 | 2100 | 0.0930 | 22.3224 |
| 0.0081 | 21.0 | 2205 | 0.0872 | 14.2616 |
| 0.0112 | 22.0 | 2310 | 0.0417 | 17.4183 |
| 0.0076 | 23.0 | 2415 | 0.0698 | 13.6979 |
| 0.0043 | 24.0 | 2520 | 0.0428 | 9.1319 |
| 0.0005 | 25.0 | 2625 | 0.0466 | 9.5829 |
| 0.0002 | 26.0 | 2730 | 0.0597 | 11.2176 |
| 0.0 | 27.0 | 2835 | 0.0527 | 10.0338 |
| 0.0 | 28.0 | 2940 | 0.0521 | 9.4138 |
| 0.0 | 29.0 | 3045 | 0.0523 | 9.4138 |
| 0.0 | 30.0 | 3150 | 0.0526 | 9.3574 |
| 0.0 | 31.0 | 3255 | 0.0527 | 9.4138 |
| 0.0 | 32.0 | 3360 | 0.0529 | 9.3574 |
| 0.0 | 33.0 | 3465 | 0.0531 | 9.3010 |
| 0.0 | 34.0 | 3570 | 0.0533 | 9.2446 |
| 0.0 | 35.0 | 3675 | 0.0535 | 9.1883 |
| 0.0 | 36.0 | 3780 | 0.0537 | 9.1319 |
| 0.0 | 37.0 | 3885 | 0.0538 | 9.0755 |
| 0.0 | 38.0 | 3990 | 0.0540 | 9.0192 |
| 0.0 | 39.0 | 4095 | 0.0541 | 9.0192 |
| 0.0 | 40.0 | 4200 | 0.0542 | 9.0192 |
| 0.0 | 41.0 | 4305 | 0.0543 | 9.0192 |
| 0.0 | 42.0 | 4410 | 0.0544 | 9.0192 |
| 0.0 | 43.0 | 4515 | 0.0545 | 8.9064 |
| 0.0 | 44.0 | 4620 | 0.0546 | 8.9064 |
| 0.0 | 45.0 | 4725 | 0.0547 | 8.9064 |
| 0.0 | 46.0 | 4830 | 0.0548 | 8.9064 |
| 0.0 | 47.0 | 4935 | 0.0549 | 8.9064 |
| 0.0 | 48.0 | 5040 | 0.0549 | 8.9064 |
| 0.0 | 49.0 | 5145 | 0.0549 | 8.7937 |
| 0.0 | 50.0 | 5250 | 0.0549 | 8.9064 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
xuykin/neg
|
xuykin
| 2024-01-25T19:07:30Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-25T19:04:45Z |
---
license: creativeml-openrail-m
---
|
Rootreck/so-vits-svc-4.0-Scrap_Mechanic
|
Rootreck
| 2024-01-25T18:58:04Z | 0 | 0 | null |
[
"Scrap Mechanic Survival",
"Scrap Mechanic",
"en",
"region:us"
] | null | 2023-12-28T06:42:58Z |
---
language:
- en
tags:
- Scrap Mechanic Survival
- Scrap Mechanic
---
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.