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2025-09-09 00:41:25
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| library_name
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CISCai/Codestral-22B-v0.1-SOTA-GGUF
|
CISCai
| 2024-06-04T20:27:31Z | 42 | 0 | null |
[
"gguf",
"code",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"base_model:mistralai/Codestral-22B-v0.1",
"base_model:quantized:mistralai/Codestral-22B-v0.1",
"license:other",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-30T18:55:37Z |
---
inference: false
license: other
license_name: mnpl
license_link: https://mistral.ai/licenses/MNPL-0.1.md
tags:
- code
language:
- code
base_model: mistralai/Codestral-22B-v0.1
model_creator: Mistral AI
model_name: Codestral-22B-v0.1
model_type: mistral
datasets:
- m-a-p/CodeFeedback-Filtered-Instruction
quantized_by: CISC
---
# Codestral-22B-v0.1 - SOTA GGUF
- Model creator: [Mistral AI](https://huggingface.co/mistralai)
- Original model: [Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1)
<!-- description start -->
## Description
This repo contains State Of The Art quantized GGUF format model files for [Codestral-22B-v0.1](https://huggingface.co/mistralai/Codestral-22B-v0.1).
Quantization was done with an importance matrix that was trained for ~1M tokens (256 batches of 4096 tokens) of answers from the [CodeFeedback-Filtered-Instruction](https://huggingface.co/datasets/m-a-p/CodeFeedback-Filtered-Instruction) dataset.
The embedded chat template has been extended to support function calling via OpenAI-compatible `tools` parameter and Fill-in-Middle token metadata has been added, see [example](#simple-llama-cpp-python-example-fill-in-middle-code). NOTE: Mistral's FIM requires support for [SPM infill mode](https://github.com/abetlen/llama-cpp-python/pull/1492)!
<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Mistral v3
```
[AVAILABLE_TOOLS] [{"name": "function_name", "description": "Description", "parameters": {...}}, ...][/AVAILABLE_TOOLS][INST] {prompt}[/INST]
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv3 files are compatible with llama.cpp from February 27th 2024 onwards, as of commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307)
They are also compatible with many third party UIs and libraries provided they are built using a recent llama.cpp.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_IQ1_S - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.56 bits per weight (bpw)
* GGML_TYPE_IQ1_M - 1-bit quantization in super-blocks with an importance matrix applied, effectively using 1.75 bpw
* GGML_TYPE_IQ2_XXS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.06 bpw
* GGML_TYPE_IQ2_XS - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.31 bpw
* GGML_TYPE_IQ2_S - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.5 bpw
* GGML_TYPE_IQ2_M - 2-bit quantization in super-blocks with an importance matrix applied, effectively using 2.7 bpw
* GGML_TYPE_IQ3_XXS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.06 bpw
* GGML_TYPE_IQ3_XS - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.3 bpw
* GGML_TYPE_IQ3_S - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.44 bpw
* GGML_TYPE_IQ3_M - 3-bit quantization in super-blocks with an importance matrix applied, effectively using 3.66 bpw
* GGML_TYPE_IQ4_XS - 4-bit quantization in super-blocks with an importance matrix applied, effectively using 4.25 bpw
* GGML_TYPE_IQ4_NL - 4-bit non-linearly mapped quantization with an importance matrix applied, effectively using 4.5 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [Codestral-22B-v0.1.IQ1_S.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ1_S.gguf) | IQ1_S | 1 | 4.3 GB| 5.3 GB | smallest, significant quality loss - **TBD**: Waiting for [this issue](https://github.com/ggerganov/llama.cpp/issues/5996) to be resolved |
| [Codestral-22B-v0.1.IQ1_M.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ1_M.gguf) | IQ1_M | 1 | 4.8 GB| 5.8 GB | very small, significant quality loss |
| [Codestral-22B-v0.1.IQ2_XXS.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ2_XXS.gguf) | IQ2_XXS | 2 | 5.4 GB| 6.4 GB | very small, high quality loss |
| [Codestral-22B-v0.1.IQ2_XS.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ2_XS.gguf) | IQ2_XS | 2 | 6.0 GB| 7.0 GB | very small, high quality loss |
| [Codestral-22B-v0.1.IQ2_S.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ2_S.gguf) | IQ2_S | 2 | 6.4 GB| 7.4 GB | small, substantial quality loss |
| [Codestral-22B-v0.1.IQ2_M.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ2_M.gguf) | IQ2_M | 2 | 6.9 GB| 7.9 GB | small, greater quality loss |
| [Codestral-22B-v0.1.IQ3_XXS.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ3_XXS.gguf) | IQ3_XXS | 3 | 7.9 GB| 8.9 GB | very small, high quality loss |
| [Codestral-22B-v0.1.IQ3_XS.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ3_XS.gguf) | IQ3_XS | 3 | 8.4 GB| 9.4 GB | small, substantial quality loss |
| [Codestral-22B-v0.1.IQ3_S.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ3_S.gguf) | IQ3_S | 3 | 8.9 GB| 9.9 GB | small, greater quality loss |
| [Codestral-22B-v0.1.IQ3_M.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ3_M.gguf) | IQ3_M | 3 | 9.2 GB| 10.2 GB | medium, balanced quality - recommended |
| [Codestral-22B-v0.1.IQ4_XS.gguf](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.IQ4_XS.gguf) | IQ4_XS | 4 | 11.5 GB| 12.5 GB | small, substantial quality loss |
Generated importance matrix file: [Codestral-22B-v0.1.imatrix.dat](https://huggingface.co/CISCai/Codestral-22B-v0.1-SOTA-GGUF/blob/main/Codestral-22B-v0.1.imatrix.dat)
**Note**: the above RAM figures assume no GPU offloading with 4K context. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [0becb22](https://github.com/ggerganov/llama.cpp/commit/0becb22ac05b6542bd9d5f2235691aa1d3d4d307) or later.
```shell
./main -ngl 57 -m Codestral-22B-v0.1.IQ4_XS.gguf --color -c 32768 --temp 0 --repeat-penalty 1.1 -p "[AVAILABLE_TOOLS] {tools}[/AVAILABLE_TOOLS][INST] {prompt}[/INST]"
```
Change `-ngl 57` 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.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
If you are low on V/RAM try quantizing the K-cache with `-ctk q8_0` or even `-ctk q4_0` for big memory savings (depending on context size).
There is a similar option for V-cache (`-ctv`), however that is [not working yet](https://github.com/ggerganov/llama.cpp/issues/4425).
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 from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) module.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://llama-cpp-python.readthedocs.io/en/latest/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Prebuilt wheel with basic CPU support
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
# Prebuilt wheel with NVidia CUDA acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121 (or cu122 etc.)
# Prebuilt wheel with Metal GPU acceleration
pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/metal
# Build base version with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUDA=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
# Or with Vulkan acceleration
CMAKE_ARGS="-DLLAMA_VULKAN=on" pip install llama-cpp-python
# Or with Kompute acceleration
CMAKE_ARGS="-DLLAMA_KOMPUTE=on" pip install llama-cpp-python
# Or with SYCL acceleration
CMAKE_ARGS="-DLLAMA_SYCL=on -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx" 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_CUDA=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Chat Completion API
llm = Llama(model_path="./Codestral-22B-v0.1.IQ4_XS.gguf", n_gpu_layers=57, n_ctx=32768)
print(llm.create_chat_completion(
repeat_penalty = 1.1,
messages = [
{
"role": "user",
"content": "Pick a LeetCode challenge and solve it in Python."
}
]
))
```
#### Simple llama-cpp-python example fill-in-middle code
```python
from llama_cpp import Llama
# Completion API
prompt = "def add("
suffix = "\n return sum\n\n"
llm = Llama(model_path="./Codestral-22B-v0.1.IQ4_XS.gguf", n_gpu_layers=57, n_ctx=32768, spm_infill=True)
output = llm.create_completion(
temperature = 0.0,
repeat_penalty = 1.0,
prompt = prompt,
suffix = suffix
)
# Models sometimes repeat suffix in response, attempt to filter that
response = output["choices"][0]["text"]
response_stripped = response.rstrip()
unwanted_response_suffix = suffix.rstrip()
unwanted_response_length = len(unwanted_response_suffix)
filtered = False
if unwanted_response_suffix and response_stripped[-unwanted_response_length:] == unwanted_response_suffix:
response = response_stripped[:-unwanted_response_length]
filtered = True
print(f"Fill-in-Middle completion{' (filtered)' if filtered else ''}:\n\n{prompt}\033[32m{response}\033[0m{suffix}")
```
#### Simple llama-cpp-python example function calling code
```python
from llama_cpp import Llama
# Chat Completion API
llm = Llama(model_path="./Codestral-22B-v0.1.IQ4_XS.gguf", n_gpu_layers=57, n_ctx=32768)
print(llm.create_chat_completion(
temperature = 0.0,
repeat_penalty = 1.1,
messages = [
{
"role": "user",
"content": "In a physics experiment, you are given an object with a mass of 50 kilograms and a volume of 10 cubic meters. Can you use the 'calculate_density' function to determine the density of this object?"
},
{ # The tool_calls is from the response to the above with tool_choice active
"role": "assistant",
"content": None,
"tool_calls": [
{
"id": "call__0_calculate_density_cmpl-...",
"type": "function",
"function": {
"name": "calculate_density",
"arguments": '{"mass": "50", "volume": "10"}'
}
}
]
},
{ # The tool_call_id is from tool_calls and content is the result from the function call you made
"role": "tool",
"content": "5.0",
"tool_call_id": "call__0_calculate_density_cmpl-..."
}
],
tools=[{
"type": "function",
"function": {
"name": "calculate_density",
"description": "Calculates the density of an object.",
"parameters": {
"type": "object",
"properties": {
"mass": {
"type": "integer",
"description": "The mass of the object."
},
"volume": {
"type": "integer",
"description": "The volume of the object."
}
},
"required": [ "mass", "volume" ]
}
}
}],
#tool_choice={
# "type": "function",
# "function": {
# "name": "calculate_density"
# }
#}
))
```
<!-- README_GGUF.md-how-to-run end -->
<!-- original-model-card start -->
# Model Card for Codestral-22B-v0.1
Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried:
- As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications
- As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code)
## Installation
It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference).
```
pip install mistral_inference
```
## Download
```py
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
```
### Chat
After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment.
```
mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256
```
Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines:
```
Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number.
fn fibonacci(n: u32) -> u32 {
match n {
0 => 0,
1 => 1,
_ => fibonacci(n - 1) + fibonacci(n - 2),
}
}
fn main() {
let n = 10;
println!("The {}th Fibonacci number is: {}", n, fibonacci(n));
}
This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers.
```
### Fill-in-the-middle (FIM)
After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed:
```py
from mistral_inference.model import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.request import FIMRequest
tokenizer = MistralTokenizer.v3()
model = Transformer.from_folder("~/codestral-22B-240529")
prefix = """def add("""
suffix = """ return sum"""
request = FIMRequest(prompt=prefix, suffix=suffix)
tokens = tokenizer.encode_fim(request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])
middle = result.split(suffix)[0].strip()
print(middle)
```
Should give something along the following lines:
```
num1, num2):
# Add two numbers
sum = num1 + num2
# return the sum
```
## Limitations
The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## License
Codestral-22B-v0.1 is released under the `MNLP-0.1` license.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
|
mariadjadi/fine_tuned_mistral_legal_V2
|
mariadjadi
| 2024-06-04T20:20:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-30T18:50:40Z |
---
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]
|
mradermacher/phi3-stellargalaxy8-merged-GGUF
|
mradermacher
| 2024-06-04T20:12:47Z | 9 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:zachaman/phi3-stellargalaxy8-merged",
"base_model:quantized:zachaman/phi3-stellargalaxy8-merged",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-04T19:54:23Z |
---
base_model: zachaman/phi3-stellargalaxy8-merged
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/zachaman/phi3-stellargalaxy8-merged
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.IQ3_XS.gguf) | IQ3_XS | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.IQ3_S.gguf) | IQ3_S | 1.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q3_K_S.gguf) | Q3_K_S | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.IQ3_M.gguf) | IQ3_M | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q3_K_M.gguf) | Q3_K_M | 2.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.IQ4_XS.gguf) | IQ4_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q3_K_L.gguf) | Q3_K_L | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q4_K_S.gguf) | Q4_K_S | 2.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q4_K_M.gguf) | Q4_K_M | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q5_K_S.gguf) | Q5_K_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q6_K.gguf) | Q6_K | 3.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/phi3-stellargalaxy8-merged-GGUF/resolve/main/phi3-stellargalaxy8-merged.f16.gguf) | f16 | 7.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
research-dump/Llama-2-13b-chat-hf_mixed_sft_timeline_forward_no_instruction
|
research-dump
| 2024-06-04T20:08:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-03T11:20:49Z |
---
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]
|
tyzhu/find_marker_both_sent_train_400_eval_40_first_permute_Qwen_Qwen1.5-4B_3e-4_lora
|
tyzhu
| 2024-06-04T20:04:11Z | 3 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-4B",
"base_model:adapter:Qwen/Qwen1.5-4B",
"license:other",
"region:us"
] | null | 2024-06-04T14:22:57Z |
---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: find_marker_both_sent_train_400_eval_40_first_permute_Qwen_Qwen1.5-4B_3e-4_lora
results: []
library_name: peft
---
<!-- 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. -->
# find_marker_both_sent_train_400_eval_40_first_permute_Qwen_Qwen1.5-4B_3e-4_lora
This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3150
- Accuracy: 0.7659
## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.5824 | 0.9933 | 130 | 1.1797 | 0.6851 |
| 0.7977 | 1.9943 | 261 | 0.5359 | 0.7429 |
| 0.3387 | 2.9952 | 392 | 0.3361 | 0.7614 |
| 0.1537 | 3.9962 | 523 | 0.2855 | 0.7653 |
| 0.1389 | 4.9971 | 654 | 0.2712 | 0.7666 |
| 0.1383 | 5.9981 | 785 | 0.2502 | 0.7676 |
| 0.1252 | 6.9990 | 916 | 0.2457 | 0.7684 |
| 0.122 | 8.0 | 1047 | 0.2310 | 0.7694 |
| 0.1169 | 8.9933 | 1177 | 0.2316 | 0.7689 |
| 0.1167 | 9.9943 | 1308 | 0.2311 | 0.7699 |
| 0.1161 | 10.9952 | 1439 | 0.2159 | 0.7708 |
| 0.1126 | 11.9962 | 1570 | 0.2188 | 0.7694 |
| 0.1088 | 12.9971 | 1701 | 0.2270 | 0.7661 |
| 0.1104 | 13.9981 | 1832 | 0.2181 | 0.7677 |
| 0.1076 | 14.9990 | 1963 | 0.2135 | 0.7680 |
| 0.1069 | 16.0 | 2094 | 0.2219 | 0.7670 |
| 0.1048 | 16.9933 | 2224 | 0.2298 | 0.7668 |
| 0.1044 | 17.9943 | 2355 | 0.2341 | 0.7666 |
| 0.1061 | 18.9952 | 2486 | 0.2628 | 0.7660 |
| 0.1104 | 19.9962 | 2617 | 0.2712 | 0.7651 |
| 0.1111 | 20.9971 | 2748 | 0.2921 | 0.7652 |
| 0.1102 | 21.9981 | 2879 | 0.2700 | 0.7660 |
| 0.1049 | 22.9990 | 3010 | 0.2905 | 0.7662 |
| 0.1024 | 24.0 | 3141 | 0.2852 | 0.7664 |
| 0.1079 | 24.9933 | 3271 | 0.2418 | 0.7653 |
| 0.1066 | 25.9943 | 3402 | 0.2759 | 0.7662 |
| 0.1054 | 26.9952 | 3533 | 0.2958 | 0.7656 |
| 0.105 | 27.9962 | 3664 | 0.3109 | 0.7663 |
| 0.1066 | 28.9971 | 3795 | 0.3062 | 0.7660 |
| 0.1048 | 29.9981 | 3926 | 0.2714 | 0.7660 |
| 0.1043 | 30.9990 | 4057 | 0.2821 | 0.7662 |
| 0.1039 | 32.0 | 4188 | 0.2961 | 0.7661 |
| 0.1055 | 32.9933 | 4318 | 0.2942 | 0.7662 |
| 0.1045 | 33.9943 | 4449 | 0.3152 | 0.7659 |
| 0.1045 | 34.9952 | 4580 | 0.2828 | 0.7666 |
| 0.1038 | 35.9962 | 4711 | 0.2355 | 0.7662 |
| 0.102 | 36.9971 | 4842 | 0.2926 | 0.7664 |
| 0.103 | 37.9981 | 4973 | 0.2825 | 0.7660 |
| 0.1061 | 38.9990 | 5104 | 0.2899 | 0.7663 |
| 0.1064 | 40.0 | 5235 | 0.2930 | 0.7660 |
| 0.105 | 40.9933 | 5365 | 0.2806 | 0.7657 |
| 0.1038 | 41.9943 | 5496 | 0.2973 | 0.7664 |
| 0.1016 | 42.9952 | 5627 | 0.3379 | 0.7662 |
| 0.1046 | 43.9962 | 5758 | 0.3200 | 0.7655 |
| 0.1039 | 44.9971 | 5889 | 0.3151 | 0.7652 |
| 0.107 | 45.9981 | 6020 | 0.2969 | 0.7658 |
| 0.1059 | 46.9990 | 6151 | 0.3146 | 0.7659 |
| 0.1058 | 48.0 | 6282 | 0.3070 | 0.7656 |
| 0.103 | 48.9933 | 6412 | 0.3060 | 0.7660 |
| 0.1012 | 49.6657 | 6500 | 0.3150 | 0.7659 |
### Framework versions
- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Masioki/gttbsc_phi-freezed-best
|
Masioki
| 2024-06-04T20:03:51Z | 34 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"single-embedding-sentence-classifier",
"generated_from_trainer",
"en",
"dataset:asapp/slue-phase-2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-05-31T10:59:14Z |
---
tags:
- generated_from_trainer
model-index:
- name: gttbsc_phi-freezed-best
results:
- task:
type: dialogue act classification
dataset:
name: asapp/slue-phase-2
type: hvb
metrics:
- name: F1 macro E2E
type: F1 macro
value: 65.66
- name: F1 macro GT
type: F1 macro
value: 69.97
datasets:
- asapp/slue-phase-2
language:
- en
metrics:
- f1-macro
---
<!-- 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. -->
# gttbsc_phi-freezed-best
Ground truth text based multi-label DAC
## Model description
Backbone: [Phi 3 mini](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
Pooling: Weighted mean pooling
Multi-label classification head: 2 dense layers with two dropouts 0.3 and Tanh activation inbetween
## Training and evaluation data
Trained on ground truth.
Evaluated on ground truth (GT) and normalized [Whisper small](https://huggingface.co/openai/whisper-small) transcripts (E2E).
### 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: linear
- num_epochs: 20
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
rmalik95/lora_model_TEST
|
rmalik95
| 2024-06-04T20:02:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T20:02:17Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** rmalik95
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
aymanboufarhi/chat-bot2B-fstt
|
aymanboufarhi
| 2024-06-04T20:02:21Z | 141 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T19:59:30Z |
---
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]
|
candrews1971/ppo-LunarLander-v2
|
candrews1971
| 2024-06-04T19:54:05Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-04T19:53:47Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 287.79 +/- 15.14
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Reihaneh/wav2vec2_fy_common_voice_28
|
Reihaneh
| 2024-06-04T19:52:46Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T09:45:59Z |
---
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **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:**
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**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. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
LucasSantiago257/gemma-2b-8bits-gptq
|
LucasSantiago257
| 2024-06-04T19:48:54Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-06-04T19:39:16Z |
---
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]
|
r1208/Llama-3-Open-Ko-8B-Instruct-preview_8bit_128r
|
r1208
| 2024-06-04T19:48:09Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-06-04T19:34:15Z |
---
library_name: transformers
tags:
- trl
- sft
---
# 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]
|
Hevagog/a2c-PandaReachDense-v3
|
Hevagog
| 2024-06-04T19:45:11Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-04T19:40:50Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.17 +/- 0.07
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF
|
bartowski
| 2024-06-04T19:43:11Z | 81 | 0 | null |
[
"gguf",
"text-generation",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-06-04T19:24:32Z |
---
license: llama3
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Awanllm-Llama-3-8B-Dolfin-v1.0
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3070">b3070</a> for quantization.
Original model: https://huggingface.co/AwanLLM/Awanllm-Llama-3-8B-Dolfin-v1.0
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
<|eot_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q8_0.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q6_K.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q5_K_M.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q5_K_S.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q4_K_M.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q4_K_S.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-IQ4_XS.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q3_K_L.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q3_K_M.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-IQ3_M.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q3_K_S.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-IQ3_XS.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-IQ3_XXS.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-Q2_K.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-IQ2_M.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-IQ2_S.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [Awanllm-Llama-3-8B-Dolfin-v1.0-IQ2_XS.gguf](https://huggingface.co/bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF/blob/main/Awanllm-Llama-3-8B-Dolfin-v1.0-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF --include "Awanllm-Llama-3-8B-Dolfin-v1.0-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Awanllm-Llama-3-8B-Dolfin-v1.0-GGUF --include "Awanllm-Llama-3-8B-Dolfin-v1.0-Q8_0.gguf/*" --local-dir Awanllm-Llama-3-8B-Dolfin-v1.0-Q8_0
```
You can either specify a new local-dir (Awanllm-Llama-3-8B-Dolfin-v1.0-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
welsachy/bert-base-uncased-finetuned-depression
|
welsachy
| 2024-06-04T19:39:57Z | 110 | 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
| 2024-06-04T19:39:38Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: model_checkpoints
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_checkpoints
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6941
- Precision: 0.6667
- Recall: 0.6667
- F1: 0.6667
- Accuracy: 0.6667
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 225 | 0.7271 | 0.6589 | 0.6589 | 0.6589 | 0.6589 |
| No log | 2.0 | 450 | 0.6941 | 0.6667 | 0.6667 | 0.6667 | 0.6667 |
| 0.7284 | 3.0 | 675 | 0.7404 | 0.6656 | 0.6656 | 0.6656 | 0.6656 |
| 0.7284 | 4.0 | 900 | 0.8450 | 0.6622 | 0.6622 | 0.6622 | 0.6622 |
| 0.4293 | 5.0 | 1125 | 0.9263 | 0.6567 | 0.6567 | 0.6567 | 0.6567 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
Enpas/small-trsc-3
|
Enpas
| 2024-06-04T19:37:34Z | 15 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-06-03T19:18:11Z |
---
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
model-index:
- name: small-Cotrsc
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. -->
# small-Cotrsc
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0487
- eval_wer: 39.6655
- eval_runtime: 516.4929
- eval_samples_per_second: 0.67
- eval_steps_per_second: 0.085
- epoch: 0.4231
- step: 1200
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1200
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
mradermacher/Mixtral_AI_ARCHIVE-GGUF
|
mradermacher
| 2024-06-04T19:36:18Z | 23 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T19:10:44Z |
---
base_model: LeroyDyer/Mixtral_AI_ARCHIVE
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_ARCHIVE
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_ARCHIVE-GGUF/resolve/main/Mixtral_AI_ARCHIVE.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF
|
mradermacher
| 2024-06-04T19:34:11Z | 107 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B",
"powermove72/GK-inv-MoE-0.1",
"en",
"base_model:powermove72/FusionNotus-Gk-MoE-13b-slerp",
"base_model:quantized:powermove72/FusionNotus-Gk-MoE-13b-slerp",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T18:47:55Z |
---
base_model: powermove72/FusionNotus-Gk-MoE-13b-slerp
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- yunconglong/Truthful_DPO_TomGrc_FusionNet_7Bx2_MoE_13B
- powermove72/GK-inv-MoE-0.1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/powermove72/FusionNotus-Gk-MoE-13b-slerp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q3_K_S.gguf) | Q3_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.IQ3_M.gguf) | IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q3_K_L.gguf) | Q3_K_L | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q5_K_S.gguf) | Q5_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q5_K_M.gguf) | Q5_K_M | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q6_K.gguf) | Q6_K | 10.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/FusionNotus-Gk-MoE-13b-slerp-GGUF/resolve/main/FusionNotus-Gk-MoE-13b-slerp.Q8_0.gguf) | Q8_0 | 13.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
sally9805/bert-base-uncased-finetuned-news-1915
|
sally9805
| 2024-06-04T19:33:53Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"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"
] |
fill-mask
| 2024-03-01T05:46:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
base_model: bert-base-uncased
model-index:
- name: bert-base-uncased-finetuned-news-1915
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. -->
# bert-base-uncased-finetuned-news-1915
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2291
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.5271 | 1.0 | 12485 | 3.3132 |
| 3.4436 | 2.0 | 24970 | 3.2593 |
| 3.4489 | 3.0 | 37455 | 3.2353 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_0bpw_exl2
|
Zoyd
| 2024-06-04T19:32:49Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"roleplay",
"en",
"arxiv:2212.04089",
"base_model:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:merge:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:SanjiWatsuki/Kunoichi-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-7B",
"base_model:SanjiWatsuki/Silicon-Maid-7B",
"base_model:merge:SanjiWatsuki/Silicon-Maid-7B",
"base_model:Sao10K/Fimbulvetr-11B-v2",
"base_model:merge:Sao10K/Fimbulvetr-11B-v2",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:merge:mistralai/Mistral-7B-v0.1",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T18:30:09Z |
---
license: cc-by-4.0
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
- SanjiWatsuki/Kunoichi-7B
- SanjiWatsuki/Silicon-Maid-7B
- KatyTheCutie/LemonadeRP-4.5.3
- Sao10K/Fimbulvetr-11B-v2
library_name: transformers
tags:
- mergekit
- merge
- mistral
- text-generation
- roleplay
model-index:
- name: Smart-Lemon-Cookie-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.55
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 61.59
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.79
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 58.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **4.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_2bpw_exl2)**</center> | <center>3126 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_5bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_0bpw_exl2)**</center> | <center>4092 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_5bpw_exl2)**</center> | <center>4717 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_75bpw_exl2)**</center> | <center>5029 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_0bpw_exl2)**</center> | <center>5341 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_25bpw_exl2)**</center> | <center>5653 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-5_0bpw_exl2)**</center> | <center>6589 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2)**</center> | <center>7862 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_5bpw_exl2)**</center> | <center>8467 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-8_0bpw_exl2)**</center> | <center>9713 MB</center> | <center>8</center> |
# Chunky-Lemon-Cookie-11B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
GGUF quants:
* https://huggingface.co/backyardai/Chunky-Lemon-Cookie-11B-GGUF
* https://huggingface.co/mradermacher/Chunky-Lemon-Cookie-11B-GGUF
## Merge Details
### Merge Method
This model was merged using the following methods:
* passthrough
* [task arithmetic](https://arxiv.org/abs/2212.04089)
### Models Merged
The following models were included in the merge:
* [SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)
* [SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B)
* [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)
* [Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2)
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Configuration
The following YAML configurations were used to produce this model:
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 24]
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
name: Mistral-11B
---
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0, 24]
- sources:
- model: SanjiWatsuki/Silicon-Maid-7B
layer_range: [8, 24]
- sources:
- model: KatyTheCutie/LemonadeRP-4.5.3
layer_range: [24, 32]
merge_method: passthrough
dtype: float16
name: Big-Lemon-Cookie-11B
---
models:
- model: Big-Lemon-Cookie-11B
parameters:
weight: 0.85
- model: Sao10K/Fimbulvetr-11B-v2
parameters:
weight: 0.15
merge_method: task_arithmetic
base_model: Mistral-11B
dtype: float16
name: Chunky-Lemon-Cookie-11B
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_FallenMerick__Chunky-Lemon-Cookie-11B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.23|
|AI2 Reasoning Challenge (25-Shot)|69.62|
|HellaSwag (10-Shot) |86.55|
|MMLU (5-Shot) |65.35|
|TruthfulQA (0-shot) |61.59|
|Winogrande (5-shot) |79.79|
|GSM8k (5-shot) |58.45|
|
Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_5bpw_exl2
|
Zoyd
| 2024-06-04T19:32:40Z | 7 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"roleplay",
"en",
"arxiv:2212.04089",
"base_model:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:merge:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:SanjiWatsuki/Kunoichi-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-7B",
"base_model:SanjiWatsuki/Silicon-Maid-7B",
"base_model:merge:SanjiWatsuki/Silicon-Maid-7B",
"base_model:Sao10K/Fimbulvetr-11B-v2",
"base_model:merge:Sao10K/Fimbulvetr-11B-v2",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:merge:mistralai/Mistral-7B-v0.1",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T17:36:40Z |
---
license: cc-by-4.0
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
- SanjiWatsuki/Kunoichi-7B
- SanjiWatsuki/Silicon-Maid-7B
- KatyTheCutie/LemonadeRP-4.5.3
- Sao10K/Fimbulvetr-11B-v2
library_name: transformers
tags:
- mergekit
- merge
- mistral
- text-generation
- roleplay
model-index:
- name: Smart-Lemon-Cookie-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.55
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 61.59
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.79
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 58.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_2bpw_exl2)**</center> | <center>3126 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_5bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_0bpw_exl2)**</center> | <center>4092 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_5bpw_exl2)**</center> | <center>4717 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_75bpw_exl2)**</center> | <center>5029 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_0bpw_exl2)**</center> | <center>5341 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_25bpw_exl2)**</center> | <center>5653 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-5_0bpw_exl2)**</center> | <center>6589 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2)**</center> | <center>7862 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_5bpw_exl2)**</center> | <center>8467 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-8_0bpw_exl2)**</center> | <center>9713 MB</center> | <center>8</center> |
# Chunky-Lemon-Cookie-11B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
GGUF quants:
* https://huggingface.co/backyardai/Chunky-Lemon-Cookie-11B-GGUF
* https://huggingface.co/mradermacher/Chunky-Lemon-Cookie-11B-GGUF
## Merge Details
### Merge Method
This model was merged using the following methods:
* passthrough
* [task arithmetic](https://arxiv.org/abs/2212.04089)
### Models Merged
The following models were included in the merge:
* [SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)
* [SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B)
* [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)
* [Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2)
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Configuration
The following YAML configurations were used to produce this model:
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 24]
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
name: Mistral-11B
---
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0, 24]
- sources:
- model: SanjiWatsuki/Silicon-Maid-7B
layer_range: [8, 24]
- sources:
- model: KatyTheCutie/LemonadeRP-4.5.3
layer_range: [24, 32]
merge_method: passthrough
dtype: float16
name: Big-Lemon-Cookie-11B
---
models:
- model: Big-Lemon-Cookie-11B
parameters:
weight: 0.85
- model: Sao10K/Fimbulvetr-11B-v2
parameters:
weight: 0.15
merge_method: task_arithmetic
base_model: Mistral-11B
dtype: float16
name: Chunky-Lemon-Cookie-11B
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_FallenMerick__Chunky-Lemon-Cookie-11B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.23|
|AI2 Reasoning Challenge (25-Shot)|69.62|
|HellaSwag (10-Shot) |86.55|
|MMLU (5-Shot) |65.35|
|TruthfulQA (0-shot) |61.59|
|Winogrande (5-shot) |79.79|
|GSM8k (5-shot) |58.45|
|
Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-8_0bpw_exl2
|
Zoyd
| 2024-06-04T19:32:13Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"roleplay",
"en",
"arxiv:2212.04089",
"base_model:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:merge:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:SanjiWatsuki/Kunoichi-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-7B",
"base_model:SanjiWatsuki/Silicon-Maid-7B",
"base_model:merge:SanjiWatsuki/Silicon-Maid-7B",
"base_model:Sao10K/Fimbulvetr-11B-v2",
"base_model:merge:Sao10K/Fimbulvetr-11B-v2",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:merge:mistralai/Mistral-7B-v0.1",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T19:25:27Z |
---
license: cc-by-4.0
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
- SanjiWatsuki/Kunoichi-7B
- SanjiWatsuki/Silicon-Maid-7B
- KatyTheCutie/LemonadeRP-4.5.3
- Sao10K/Fimbulvetr-11B-v2
library_name: transformers
tags:
- mergekit
- merge
- mistral
- text-generation
- roleplay
model-index:
- name: Smart-Lemon-Cookie-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.55
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 61.59
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.79
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 58.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **8.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_2bpw_exl2)**</center> | <center>3126 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_5bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_0bpw_exl2)**</center> | <center>4092 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_5bpw_exl2)**</center> | <center>4717 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_75bpw_exl2)**</center> | <center>5029 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_0bpw_exl2)**</center> | <center>5341 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_25bpw_exl2)**</center> | <center>5653 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-5_0bpw_exl2)**</center> | <center>6589 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2)**</center> | <center>7862 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_5bpw_exl2)**</center> | <center>8467 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-8_0bpw_exl2)**</center> | <center>9713 MB</center> | <center>8</center> |
# Chunky-Lemon-Cookie-11B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
GGUF quants:
* https://huggingface.co/backyardai/Chunky-Lemon-Cookie-11B-GGUF
* https://huggingface.co/mradermacher/Chunky-Lemon-Cookie-11B-GGUF
## Merge Details
### Merge Method
This model was merged using the following methods:
* passthrough
* [task arithmetic](https://arxiv.org/abs/2212.04089)
### Models Merged
The following models were included in the merge:
* [SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)
* [SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B)
* [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)
* [Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2)
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Configuration
The following YAML configurations were used to produce this model:
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 24]
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
name: Mistral-11B
---
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0, 24]
- sources:
- model: SanjiWatsuki/Silicon-Maid-7B
layer_range: [8, 24]
- sources:
- model: KatyTheCutie/LemonadeRP-4.5.3
layer_range: [24, 32]
merge_method: passthrough
dtype: float16
name: Big-Lemon-Cookie-11B
---
models:
- model: Big-Lemon-Cookie-11B
parameters:
weight: 0.85
- model: Sao10K/Fimbulvetr-11B-v2
parameters:
weight: 0.15
merge_method: task_arithmetic
base_model: Mistral-11B
dtype: float16
name: Chunky-Lemon-Cookie-11B
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_FallenMerick__Chunky-Lemon-Cookie-11B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.23|
|AI2 Reasoning Challenge (25-Shot)|69.62|
|HellaSwag (10-Shot) |86.55|
|MMLU (5-Shot) |65.35|
|TruthfulQA (0-shot) |61.59|
|Winogrande (5-shot) |79.79|
|GSM8k (5-shot) |58.45|
|
Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_0bpw_exl2
|
Zoyd
| 2024-06-04T19:31:34Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"roleplay",
"en",
"arxiv:2212.04089",
"base_model:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:merge:KatyTheCutie/LemonadeRP-4.5.3",
"base_model:SanjiWatsuki/Kunoichi-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-7B",
"base_model:SanjiWatsuki/Silicon-Maid-7B",
"base_model:merge:SanjiWatsuki/Silicon-Maid-7B",
"base_model:Sao10K/Fimbulvetr-11B-v2",
"base_model:merge:Sao10K/Fimbulvetr-11B-v2",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:merge:mistralai/Mistral-7B-v0.1",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"3-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T17:46:22Z |
---
license: cc-by-4.0
language:
- en
base_model:
- mistralai/Mistral-7B-v0.1
- SanjiWatsuki/Kunoichi-7B
- SanjiWatsuki/Silicon-Maid-7B
- KatyTheCutie/LemonadeRP-4.5.3
- Sao10K/Fimbulvetr-11B-v2
library_name: transformers
tags:
- mergekit
- merge
- mistral
- text-generation
- roleplay
model-index:
- name: Smart-Lemon-Cookie-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 69.62
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.55
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 61.59
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 79.79
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 58.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=FallenMerick/Chunky-Lemon-Cookie-11B
name: Open LLM Leaderboard
---
**Exllamav2** quant (**exl2** / **3.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_2bpw_exl2)**</center> | <center>3126 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-2_5bpw_exl2)**</center> | <center>3469 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_0bpw_exl2)**</center> | <center>4092 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_5bpw_exl2)**</center> | <center>4717 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-3_75bpw_exl2)**</center> | <center>5029 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_0bpw_exl2)**</center> | <center>5341 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-4_25bpw_exl2)**</center> | <center>5653 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-5_0bpw_exl2)**</center> | <center>6589 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_0bpw_exl2)**</center> | <center>7862 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-6_5bpw_exl2)**</center> | <center>8467 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/FallenMerick_Chunky-Lemon-Cookie-11B-8_0bpw_exl2)**</center> | <center>9713 MB</center> | <center>8</center> |
# Chunky-Lemon-Cookie-11B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
GGUF quants:
* https://huggingface.co/backyardai/Chunky-Lemon-Cookie-11B-GGUF
* https://huggingface.co/mradermacher/Chunky-Lemon-Cookie-11B-GGUF
## Merge Details
### Merge Method
This model was merged using the following methods:
* passthrough
* [task arithmetic](https://arxiv.org/abs/2212.04089)
### Models Merged
The following models were included in the merge:
* [SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)
* [SanjiWatsuki/Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B)
* [KatyTheCutie/LemonadeRP-4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3)
* [Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2)
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Configuration
The following YAML configurations were used to produce this model:
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [0, 24]
- sources:
- model: mistralai/Mistral-7B-v0.1
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
name: Mistral-11B
---
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0, 24]
- sources:
- model: SanjiWatsuki/Silicon-Maid-7B
layer_range: [8, 24]
- sources:
- model: KatyTheCutie/LemonadeRP-4.5.3
layer_range: [24, 32]
merge_method: passthrough
dtype: float16
name: Big-Lemon-Cookie-11B
---
models:
- model: Big-Lemon-Cookie-11B
parameters:
weight: 0.85
- model: Sao10K/Fimbulvetr-11B-v2
parameters:
weight: 0.15
merge_method: task_arithmetic
base_model: Mistral-11B
dtype: float16
name: Chunky-Lemon-Cookie-11B
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_FallenMerick__Chunky-Lemon-Cookie-11B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.23|
|AI2 Reasoning Challenge (25-Shot)|69.62|
|HellaSwag (10-Shot) |86.55|
|MMLU (5-Shot) |65.35|
|TruthfulQA (0-shot) |61.59|
|Winogrande (5-shot) |79.79|
|GSM8k (5-shot) |58.45|
|
andrejikica/paligemma_vqav2
|
andrejikica
| 2024-06-04T19:19:03Z | 3 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/paligemma-3b-pt-224",
"base_model:adapter:google/paligemma-3b-pt-224",
"license:gemma",
"region:us"
] | null | 2024-06-02T23:16:41Z |
---
license: gemma
library_name: peft
tags:
- generated_from_trainer
base_model: google/paligemma-3b-pt-224
model-index:
- name: paligemma_vqav2
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. -->
# paligemma_vqav2
This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
LucasSantiago257/gemma-2b-4bits-gptq
|
LucasSantiago257
| 2024-06-04T19:16:02Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-06-04T19:09:08Z |
---
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]
|
mradermacher/Stheno-1.1-L2-13B-GGUF
|
mradermacher
| 2024-06-04T19:10:38Z | 2 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Sao10K/Stheno-1.1-L2-13B",
"base_model:quantized:Sao10K/Stheno-1.1-L2-13B",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T13:08:04Z |
---
base_model: Sao10K/Stheno-1.1-L2-13B
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Sao10K/Stheno-1.1-L2-13B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.1-L2-13B-GGUF/resolve/main/Stheno-1.1-L2-13B.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
rong4ivy/mentalhealth_LM
|
rong4ivy
| 2024-06-04T19:10:26Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-04T18:46:10Z |
---
license: apache-2.0
---
This large language model is primarily designed to assess the severity of **mental health** issues by analyzing text or speech inputs from users(speakers, writers, patients, etc.). The training dataset consists of diagnoses made by psychiatrists based on the text or speech from patients experiencing various degrees of mental health problems.
The model serves multiple purposes. For instance, it can assist doctors in diagnosing mental health conditions in patients/certain individuals, or facilitate self-diagnosis for individuals seeking to understand their own mental health, or analyze the psychological characteristics of characters in fictional narratives.
The performace of this model in the test dataset (30477 rows) is as follows: 'accuracy': 0.78, 'f1': 0.77.
This model is one part of my project on fine-tuning open-source LLMs to predict various human cognitive abilities (e.g., personality, attitude, mental status etc.).
The following test examples can used in the API bar, 1)"I was okay just a moment ago. I will learn how to be okay again.".
2) "There were days when she was unhappy; she did not know why, when it did not seem worthwhile to be glad or sorry, to be alive or dead; when life appeared to her like a grotesque pandemonium and humanity like worms struggling blindly toward inevitable annihilation".
3)"I hope to one day see a sea of people all wearing silver ribbons as a sign that they understand the secret battle and as a celebration of the victories made each day as we individually pull ourselves up out of our foxholes to see our scars heal and to remember what the sun looks like.".
The **output** assigns a label with values from **0 to 5** to classify the **severity** of mental health issues. A label of **0** signifies **minimal severity**, suggesting few or no symptoms of mental health problems.
Conversely, a label of **5** denotes **maximal severity**, reflecting serious mental health conditions that may require immediate and comprehensive intervention.
**A larger value means that the situation is likely to be more serious**. Take care!
Please run the following code to test a new text:
```
import torch
from transformers import BertTokenizer, BertForSequenceClassification, AutoConfig
# Define the model path
model_path = "Kevintu/mentalhealth_LM"
# Load configuration, tokenizer, and model
config = AutoConfig.from_pretrained(model_path, num_labels=6, problem_type="single_label_classification")
tokenizer = BertTokenizer.from_pretrained(model_path, use_fast=True)
model = BertForSequenceClassification.from_pretrained(model_path, config=config, ignore_mismatched_sizes=True)
def predict_text(text, model, tokenizer):
# Encode the text using the tokenizer
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
# Forward pass, get logits
with torch.no_grad():
outputs = model(**inputs)
# Extract logits
logits = outputs.logits
# Convert logits to probabilities
probabilities = torch.softmax(logits, dim=-1)
max_probability, predicted_class_index = torch.max(probabilities, dim=-1)
return predicted_class_index.item(), max_probability.item(), probabilities.numpy()
# Example usage
text = "I was okay just a moment ago. I will learn how to be okay again."
predicted_class, max_prob, probs = predict_text(text, model, tokenizer)
print(f"Predicted class: {predicted_class}, Probability: {max_prob:.4f}")
##Output: "Predicted class: 2, Probability: 0.5194"
```
|
fabian-cmu/butterflies-diffusion
|
fabian-cmu
| 2024-06-04T19:00:30Z | 44 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2024-06-04T18:59:47Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('fabian-cmu/butterflies-diffusion')
image = pipeline().images[0]
image
```
|
tsavage68/UTI_M2_1000steps_1e8rate_03beta_CSFTDPO
|
tsavage68
| 2024-06-04T18:58:48Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:tsavage68/UTI_M2_1000steps_1e5rate_SFT",
"base_model:finetune:tsavage68/UTI_M2_1000steps_1e5rate_SFT",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T18:54:51Z |
---
license: apache-2.0
base_model: tsavage68/UTI_M2_1000steps_1e5rate_SFT
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: UTI_M2_1000steps_1e8rate_03beta_CSFTDPO
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. -->
# UTI_M2_1000steps_1e8rate_03beta_CSFTDPO
This model is a fine-tuned version of [tsavage68/UTI_M2_1000steps_1e5rate_SFT](https://huggingface.co/tsavage68/UTI_M2_1000steps_1e5rate_SFT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6733
- Rewards/chosen: 0.0076
- Rewards/rejected: -0.0329
- Rewards/accuracies: 0.8100
- Rewards/margins: 0.0405
- Logps/rejected: -44.2758
- Logps/chosen: -20.2691
- Logits/rejected: -3.8168
- Logits/chosen: -3.7448
## 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-08
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6936 | 0.3333 | 25 | 0.6942 | 0.0013 | 0.0032 | 0.4000 | -0.0020 | -44.1553 | -20.2903 | -3.8168 | -3.7448 |
| 0.6921 | 0.6667 | 50 | 0.6936 | 0.0023 | 0.0030 | 0.5 | -0.0007 | -44.1562 | -20.2868 | -3.8168 | -3.7448 |
| 0.6943 | 1.0 | 75 | 0.6955 | 0.0005 | 0.0049 | 0.4200 | -0.0044 | -44.1498 | -20.2931 | -3.8169 | -3.7449 |
| 0.6933 | 1.3333 | 100 | 0.6933 | 0.0014 | 0.0014 | 0.4200 | -0.0001 | -44.1614 | -20.2900 | -3.8168 | -3.7448 |
| 0.6886 | 1.6667 | 125 | 0.6920 | 0.0002 | -0.0022 | 0.4800 | 0.0024 | -44.1735 | -20.2938 | -3.8168 | -3.7448 |
| 0.6896 | 2.0 | 150 | 0.6887 | 0.0040 | -0.0053 | 0.5700 | 0.0092 | -44.1837 | -20.2814 | -3.8168 | -3.7448 |
| 0.6879 | 2.3333 | 175 | 0.6864 | 0.0033 | -0.0104 | 0.6200 | 0.0138 | -44.2009 | -20.2835 | -3.8168 | -3.7447 |
| 0.683 | 2.6667 | 200 | 0.6824 | 0.0048 | -0.0171 | 0.6700 | 0.0218 | -44.2230 | -20.2786 | -3.8168 | -3.7447 |
| 0.6815 | 3.0 | 225 | 0.6825 | 0.0042 | -0.0174 | 0.7100 | 0.0217 | -44.2242 | -20.2805 | -3.8169 | -3.7449 |
| 0.6791 | 3.3333 | 250 | 0.6800 | 0.0039 | -0.0229 | 0.7300 | 0.0268 | -44.2426 | -20.2815 | -3.8168 | -3.7448 |
| 0.6772 | 3.6667 | 275 | 0.6798 | 0.0062 | -0.0210 | 0.6900 | 0.0273 | -44.2362 | -20.2738 | -3.8167 | -3.7447 |
| 0.6753 | 4.0 | 300 | 0.6784 | 0.0059 | -0.0242 | 0.7400 | 0.0301 | -44.2468 | -20.2747 | -3.8169 | -3.7448 |
| 0.6821 | 4.3333 | 325 | 0.6767 | 0.0069 | -0.0268 | 0.7700 | 0.0336 | -44.2554 | -20.2717 | -3.8167 | -3.7447 |
| 0.6744 | 4.6667 | 350 | 0.6770 | 0.0060 | -0.0270 | 0.7100 | 0.0330 | -44.2561 | -20.2747 | -3.8169 | -3.7448 |
| 0.6741 | 5.0 | 375 | 0.6750 | 0.0088 | -0.0281 | 0.7300 | 0.0370 | -44.2598 | -20.2651 | -3.8168 | -3.7448 |
| 0.6738 | 5.3333 | 400 | 0.6753 | 0.0084 | -0.0281 | 0.7700 | 0.0365 | -44.2599 | -20.2667 | -3.8168 | -3.7447 |
| 0.6731 | 5.6667 | 425 | 0.6746 | 0.0074 | -0.0306 | 0.75 | 0.0379 | -44.2681 | -20.2701 | -3.8169 | -3.7448 |
| 0.6756 | 6.0 | 450 | 0.6755 | 0.0071 | -0.0289 | 0.7700 | 0.0360 | -44.2625 | -20.2710 | -3.8168 | -3.7448 |
| 0.6703 | 6.3333 | 475 | 0.6750 | 0.0093 | -0.0279 | 0.75 | 0.0371 | -44.2590 | -20.2637 | -3.8168 | -3.7448 |
| 0.6796 | 6.6667 | 500 | 0.6744 | 0.0074 | -0.0308 | 0.7800 | 0.0383 | -44.2689 | -20.2698 | -3.8168 | -3.7448 |
| 0.6676 | 7.0 | 525 | 0.6735 | 0.0091 | -0.0311 | 0.7800 | 0.0402 | -44.2699 | -20.2642 | -3.8168 | -3.7447 |
| 0.6744 | 7.3333 | 550 | 0.6738 | 0.0067 | -0.0330 | 0.7600 | 0.0397 | -44.2760 | -20.2721 | -3.8168 | -3.7448 |
| 0.6725 | 7.6667 | 575 | 0.6729 | 0.0083 | -0.0330 | 0.8000 | 0.0413 | -44.2761 | -20.2668 | -3.8168 | -3.7447 |
| 0.6739 | 8.0 | 600 | 0.6732 | 0.0080 | -0.0327 | 0.8100 | 0.0407 | -44.2751 | -20.2679 | -3.8168 | -3.7447 |
| 0.6675 | 8.3333 | 625 | 0.6748 | 0.0084 | -0.0291 | 0.7800 | 0.0375 | -44.2632 | -20.2665 | -3.8169 | -3.7448 |
| 0.6706 | 8.6667 | 650 | 0.6732 | 0.0087 | -0.0320 | 0.8100 | 0.0407 | -44.2728 | -20.2656 | -3.8168 | -3.7447 |
| 0.6718 | 9.0 | 675 | 0.6741 | 0.0086 | -0.0303 | 0.7800 | 0.0389 | -44.2671 | -20.2658 | -3.8168 | -3.7448 |
| 0.6715 | 9.3333 | 700 | 0.6743 | 0.0085 | -0.0300 | 0.8000 | 0.0385 | -44.2662 | -20.2662 | -3.8168 | -3.7447 |
| 0.6723 | 9.6667 | 725 | 0.6727 | 0.0066 | -0.0352 | 0.7700 | 0.0417 | -44.2834 | -20.2727 | -3.8168 | -3.7448 |
| 0.6715 | 10.0 | 750 | 0.6729 | 0.0067 | -0.0348 | 0.7700 | 0.0415 | -44.2822 | -20.2723 | -3.8168 | -3.7448 |
| 0.669 | 10.3333 | 775 | 0.6743 | 0.0074 | -0.0310 | 0.7600 | 0.0384 | -44.2694 | -20.2698 | -3.8168 | -3.7447 |
| 0.6738 | 10.6667 | 800 | 0.6729 | 0.0079 | -0.0336 | 0.8000 | 0.0415 | -44.2780 | -20.2682 | -3.8168 | -3.7448 |
| 0.6738 | 11.0 | 825 | 0.6735 | 0.0088 | -0.0312 | 0.8100 | 0.0400 | -44.2703 | -20.2653 | -3.8169 | -3.7448 |
| 0.6682 | 11.3333 | 850 | 0.6736 | 0.0079 | -0.0321 | 0.8100 | 0.0400 | -44.2730 | -20.2681 | -3.8168 | -3.7448 |
| 0.6787 | 11.6667 | 875 | 0.6733 | 0.0076 | -0.0329 | 0.8100 | 0.0405 | -44.2758 | -20.2691 | -3.8168 | -3.7448 |
| 0.6771 | 12.0 | 900 | 0.6733 | 0.0076 | -0.0329 | 0.8100 | 0.0405 | -44.2758 | -20.2691 | -3.8168 | -3.7448 |
| 0.6705 | 12.3333 | 925 | 0.6733 | 0.0076 | -0.0329 | 0.8100 | 0.0405 | -44.2758 | -20.2691 | -3.8168 | -3.7448 |
| 0.6727 | 12.6667 | 950 | 0.6733 | 0.0076 | -0.0329 | 0.8100 | 0.0405 | -44.2758 | -20.2691 | -3.8168 | -3.7448 |
| 0.6748 | 13.0 | 975 | 0.6733 | 0.0076 | -0.0329 | 0.8100 | 0.0405 | -44.2758 | -20.2691 | -3.8168 | -3.7448 |
| 0.6809 | 13.3333 | 1000 | 0.6733 | 0.0076 | -0.0329 | 0.8100 | 0.0405 | -44.2758 | -20.2691 | -3.8168 | -3.7448 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.0.0+cu117
- Datasets 2.19.2
- Tokenizers 0.19.1
|
r1208/Llama-3-Open-Ko-8B-Instruct-preview_4bit_64r
|
r1208
| 2024-06-04T18:55:22Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-06-04T18:24:09Z |
---
library_name: transformers
tags:
- trl
- sft
---
-- 4 bit
# 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]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
r1208/llama_3_ml_beam_all_11_1
|
r1208
| 2024-06-04T18:55:06Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-06-04T17:57:54Z |
---
library_name: transformers
tags:
- trl
- sft
---
--4bit
# 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]
|
JeswinMS4/scam-alert-distil-roberta
|
JeswinMS4
| 2024-06-04T18:51:40Z | 24 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-04T18:16:24Z |
---
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: scam-alert-distil-roberta
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. -->
# scam-alert-distil-roberta
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1213
- Accuracy: 0.9861
- F1: 0.9860
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log | 0.1577 | 100 | 0.0852 | 0.9861 | 0.9860 |
| No log | 0.3155 | 200 | 0.0690 | 0.9861 | 0.9858 |
| No log | 0.4732 | 300 | 0.0965 | 0.9841 | 0.9842 |
| No log | 0.6309 | 400 | 0.1015 | 0.9821 | 0.9818 |
| No log | 0.7886 | 500 | 0.0629 | 0.9861 | 0.9859 |
| No log | 0.9464 | 600 | 0.0788 | 0.9841 | 0.9839 |
| No log | 1.1041 | 700 | 0.0500 | 0.9880 | 0.9880 |
| No log | 1.2618 | 800 | 0.0778 | 0.9880 | 0.9879 |
| No log | 1.4196 | 900 | 0.0657 | 0.9880 | 0.9879 |
| No log | 1.5773 | 1000 | 0.1129 | 0.9841 | 0.9837 |
| No log | 1.7350 | 1100 | 0.1038 | 0.9880 | 0.9879 |
| No log | 1.8927 | 1200 | 0.0861 | 0.9880 | 0.9879 |
| No log | 2.0505 | 1300 | 0.1047 | 0.9841 | 0.9841 |
| No log | 2.2082 | 1400 | 0.0858 | 0.9900 | 0.9899 |
| No log | 2.3659 | 1500 | 0.0936 | 0.9880 | 0.9879 |
| No log | 2.5237 | 1600 | 0.0936 | 0.9861 | 0.9859 |
| No log | 2.6814 | 1700 | 0.0909 | 0.9861 | 0.9859 |
| No log | 2.8391 | 1800 | 0.1143 | 0.9841 | 0.9842 |
| No log | 2.9968 | 1900 | 0.0902 | 0.9880 | 0.9881 |
| No log | 3.1546 | 2000 | 0.0979 | 0.9841 | 0.9840 |
| No log | 3.3123 | 2100 | 0.0977 | 0.9861 | 0.9860 |
| No log | 3.4700 | 2200 | 0.0987 | 0.9861 | 0.9860 |
| No log | 3.6278 | 2300 | 0.1016 | 0.9861 | 0.9860 |
| No log | 3.7855 | 2400 | 0.1170 | 0.9861 | 0.9858 |
| No log | 3.9432 | 2500 | 0.1106 | 0.9861 | 0.9859 |
| 0.0267 | 4.1009 | 2600 | 0.1202 | 0.9861 | 0.9861 |
| 0.0267 | 4.2587 | 2700 | 0.1207 | 0.9841 | 0.9841 |
| 0.0267 | 4.4164 | 2800 | 0.1208 | 0.9841 | 0.9841 |
| 0.0267 | 4.5741 | 2900 | 0.1215 | 0.9841 | 0.9841 |
| 0.0267 | 4.7319 | 3000 | 0.1216 | 0.9841 | 0.9841 |
| 0.0267 | 4.8896 | 3100 | 0.1215 | 0.9841 | 0.9841 |
| 0.0267 | 5.0473 | 3200 | 0.1350 | 0.9861 | 0.9861 |
| 0.0267 | 5.2050 | 3300 | 0.1165 | 0.9880 | 0.9880 |
| 0.0267 | 5.3628 | 3400 | 0.1166 | 0.9880 | 0.9880 |
| 0.0267 | 5.5205 | 3500 | 0.1167 | 0.9880 | 0.9880 |
| 0.0267 | 5.6782 | 3600 | 0.1168 | 0.9880 | 0.9880 |
| 0.0267 | 5.8360 | 3700 | 0.1212 | 0.9861 | 0.9860 |
| 0.0267 | 5.9937 | 3800 | 0.1213 | 0.9861 | 0.9860 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
mradermacher/Stheno-1.2-L2-13B-GGUF
|
mradermacher
| 2024-06-04T18:51:32Z | 1 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Sao10K/Stheno-1.2-L2-13B",
"base_model:quantized:Sao10K/Stheno-1.2-L2-13B",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T13:38:23Z |
---
base_model: Sao10K/Stheno-1.2-L2-13B
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Sao10K/Stheno-1.2-L2-13B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Stheno-1.2-L2-13B-GGUF/resolve/main/Stheno-1.2-L2-13B.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Zephyrus-L1-33B-GGUF
|
mradermacher
| 2024-06-04T18:47:37Z | 3 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Sao10K/Zephyrus-L1-33B",
"base_model:quantized:Sao10K/Zephyrus-L1-33B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T14:19:28Z |
---
base_model: Sao10K/Zephyrus-L1-33B
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Sao10K/Zephyrus-L1-33B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-GGUF/resolve/main/Zephyrus-L1-33B.Q8_0.gguf) | Q8_0 | 34.7 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
tyzhu/lmind_nq_train6000_eval6489_v1_reciteonly_qa__home_aiops_zhuty_lm_indexer_data_tyzhu_lmi
|
tyzhu
| 2024-06-04T18:46:21Z | 0 | 0 | null |
[
"generated_from_trainer",
"region:us"
] | null | 2024-06-04T18:19:16Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: lmind_nq_train6000_eval6489_v1_reciteonly_qa__home_aiops_zhuty_lm_indexer_data_tyzhu_lmi
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. -->
# lmind_nq_train6000_eval6489_v1_reciteonly_qa__home_aiops_zhuty_lm_indexer_data_tyzhu_lmi
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1255
- Accuracy: 0.7637
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9058 | 1.0 | 187 | 0.6990 | 0.7896 |
| 0.65 | 2.0 | 375 | 0.6702 | 0.7947 |
| 0.5668 | 3.0 | 562 | 0.6754 | 0.7936 |
| 0.4786 | 4.0 | 750 | 0.7029 | 0.7898 |
| 0.4035 | 5.0 | 937 | 0.7543 | 0.7836 |
| 0.3288 | 6.0 | 1125 | 0.8310 | 0.7775 |
| 0.2672 | 7.0 | 1312 | 0.9077 | 0.7724 |
| 0.2107 | 8.0 | 1500 | 0.9633 | 0.7690 |
| 0.157 | 9.0 | 1687 | 1.0464 | 0.7666 |
| 0.1262 | 9.97 | 1870 | 1.1255 | 0.7637 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
LucasSantiago257/gemma-2b-2bits-gptq
|
LucasSantiago257
| 2024-06-04T18:45:03Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"2-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-05-27T17:54:21Z |
---
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]
|
DimkKozhemyako/dummy-model
|
DimkKozhemyako
| 2024-06-04T18:35:55Z | 129 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-05-18T14:26:38Z |
---
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]
|
moczard/rl_course_vizdoom_health_gathering_supreme
|
moczard
| 2024-06-04T18:34:53Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-04T18:34:44Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.39 +/- 5.05
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r moczard/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
arnavgrg/phi-2-codealpaca-5K-medusa-lora
|
arnavgrg
| 2024-06-04T18:31:03Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"region:us"
] | null | 2024-06-04T18:30:56Z |
---
library_name: peft
base_model: microsoft/phi-2
---
# 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 0.11.1
|
tringuyen-uit/MRC_ER_mdeberta-v3-base_syl_ViWikiFC
|
tringuyen-uit
| 2024-06-04T18:30:04Z | 22 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"question-answering",
"generated_from_trainer",
"base_model:microsoft/mdeberta-v3-base",
"base_model:finetune:microsoft/mdeberta-v3-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-05-28T17:12:33Z |
---
license: mit
base_model: microsoft/mdeberta-v3-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: MRC_ER_mdeberta-v3-base_syl_ViWikiFC
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. -->
# MRC_ER_mdeberta-v3-base_syl_ViWikiFC
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5168
- Exact Match: 0.7703
- F1: 0.7925
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|:------:|
| 0.6752 | 1.0 | 4185 | 2.1250 | 0.7421 | 0.7696 |
| 0.5728 | 2.0 | 8370 | 1.9436 | 0.7660 | 0.7865 |
| 0.4473 | 3.0 | 12555 | 2.1698 | 0.7569 | 0.7796 |
| 0.3166 | 4.0 | 16740 | 2.3835 | 0.7708 | 0.7945 |
| 0.2525 | 5.0 | 20925 | 2.5168 | 0.7703 | 0.7925 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
James520/Grammar_Check
|
James520
| 2024-06-04T18:29:25Z | 110 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-04T18:17:45Z |
---
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.4740733206272125
f1: 0.8560558021559924
precision: 0.7885514018691588
recall: 0.9361997226074896
auc: 0.8029694782091815
accuracy: 0.7823585810162992
|
EstebanKora/Voz-Bot
|
EstebanKora
| 2024-06-04T18:16:01Z | 146 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T18:07:16Z |
---
license: apache-2.0
---
|
ShenaoZ/SELM-Zephyr-7Bz-iter-1
|
ShenaoZ
| 2024-06-04T18:13:59Z | 10 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/DPO-Zephyr-7B",
"base_model:finetune:ShenaoZ/DPO-Zephyr-7B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T17:25:59Z |
---
license: mit
base_model: ShenaoZ/DPO-Zephyr-7B
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- updated
- original
model-index:
- name: SELM-Zephyr-7Bz-iter-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SELM-Zephyr-7Bz-iter-1
This model is a fine-tuned version of [ShenaoZ/DPO-Zephyr-7B](https://huggingface.co/ShenaoZ/DPO-Zephyr-7B) on the updated and the original datasets.
## 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: 4
- total_train_batch_size: 256
- 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
### Framework versions
- Transformers 4.40.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
|
Firemedic15/dqn-SpaceInvadersNoFrameskip-V4-DE
|
Firemedic15
| 2024-06-04T18:12:08Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-04T18:11:37Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 270.50 +/- 21.15
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Firemedic15 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Firemedic15 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Firemedic15
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
lhallee/ProteinVec
|
lhallee
| 2024-06-04T18:09:49Z | 7 | 1 |
transformers
|
[
"transformers",
"safetensors",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T01:54:01Z |
---
library_name: transformers
tags: []
---
## THIS PROTEIN VEC ADAPTATION WAS MODIFIED FROM https://github.com/tymor22/protein-vec
All credit for the original work goes to Tymor Hamamsy and the following authors of this paper https://www.biorxiv.org/content/10.1101/2023.11.26.568742v1
We have added a Huggingface compatible wrapper for the model in protvec.py
Please consider liking the model page and starring the github repo if you are going to use it :)
```
https://huggingface.co/lhallee/ProteinVec
https://github.com/lhallee/ProteinVecHuggingface
```
Clone and install
```
git clone https://github.com/lhallee/ProteinVecHuggingface.git
pip install torch pytorch_lightning transformers
```
To use from hugggingface
```
from transformers import T5Tokenizer
from protvec import ProteinVec, ProteinVecConfig
tokenizer = T5Tokenizer.from_pretrained('lhallee/ProteinVec')
model = ProteinVec.from_pretrained('lhallee/ProteinVec', config=ProteinVecConfig())
```
Embed a single sequence with ```embed```
```
model.to_eval()
model = model.cuda() # remove if cpu inference
embedding = model.embed('SEQWENCE').detach().cpu() # torch.tensor(1, 512)
```
Use a particular AspectVec by setting the ```inference_mask```
```
model.aspect_to_keys_dict # dictionary showing the aspects
### The model is set to ALL by default to use full ProteinVec
model.inference_mask = model.get_mask('EC') # for Enzyme Comission AspectVec
embedding = model.embed(...)
```
To half precision weights
```
model.to_half()
```
## The license for the protein vec code
### BSD 3-Clause License
Copyright (c) 2023, Tymor Hamamsy
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
3. Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
davelotito/donut_experiment_bayesian_trial_7
|
davelotito
| 2024-06-04T18:06:27Z | 50 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-06-04T16:45:42Z |
---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_bayesian_trial_7
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. -->
# donut_experiment_bayesian_trial_7
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3786
- Bleu: 0.0669
- Precisions: [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332]
- Brevity Penalty: 0.0870
- Length Ratio: 0.2905
- Translation Length: 473
- Reference Length: 1628
- Cer: 0.7532
- Wer: 0.8192
## 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: 3.540464175534869e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.5323 | 1.0 | 253 | 0.4204 | 0.0580 | [0.7710084033613446, 0.6778042959427207, 0.6132596685082873, 0.5639344262295082] | 0.0889 | 0.2924 | 476 | 1628 | 0.7617 | 0.8431 |
| 0.2487 | 2.0 | 506 | 0.3788 | 0.0609 | [0.8123667377398721, 0.7402912621359223, 0.6929577464788732, 0.6476510067114094] | 0.0845 | 0.2881 | 469 | 1628 | 0.7561 | 0.8279 |
| 0.1746 | 3.0 | 759 | 0.3551 | 0.0652 | [0.836864406779661, 0.7759036144578313, 0.729050279329609, 0.6843853820598007] | 0.0864 | 0.2899 | 472 | 1628 | 0.7541 | 0.8213 |
| 0.1191 | 4.0 | 1012 | 0.3690 | 0.0680 | [0.8547368421052631, 0.784688995215311, 0.7451523545706371, 0.7039473684210527] | 0.0883 | 0.2918 | 475 | 1628 | 0.7514 | 0.8192 |
| 0.1072 | 5.0 | 1265 | 0.3786 | 0.0669 | [0.8477801268498943, 0.7836538461538461, 0.7465181058495822, 0.7052980132450332] | 0.0870 | 0.2905 | 473 | 1628 | 0.7532 | 0.8192 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1
|
mascfree/llama-3-8b-Instruct-bnb-4bit-lora-64-64_grupo_elite_tesis-lora
|
mascfree
| 2024-06-04T18:03:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T18:03:36Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** mascfree
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
martinsinnona/visdecode_vega_5
|
martinsinnona
| 2024-06-04T17:52:11Z | 49 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pix2struct",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-06-04T17:29:01Z |
---
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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aymanboufarhi/2B-chat-bot-fstt
|
aymanboufarhi
| 2024-06-04T17:51:29Z | 142 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T12:23:19Z |
---
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]
|
Shalie/SukoyaKanaPonyXL
|
Shalie
| 2024-06-04T17:48:05Z | 2 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:AstraliteHeart/pony-diffusion-v6",
"base_model:adapter:AstraliteHeart/pony-diffusion-v6",
"license:other",
"region:us"
] |
text-to-image
| 2024-06-04T17:46:01Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya1st, ahoge, mole under eye, x hair
ornament, nurse cap, bandaged arm, bandages, white apron, white wrist cuffs,
white gloves, nurse dress, blush, hands on own cheeks, hands on own face,
looking at viewer, mouth hold, solo, cat cutout, couch, indoors, coy
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06382-2950229766-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya1st,
ahoge, mole un.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya5th, mole under eye, x hair
ornament, black choker, beret, grey jacket, white dress, open jacket, blush,
clenched teeth, crossed arms, hands up, looking at viewer, nose blush,
smile, solo, standing, tears, trembling, floating, full body, simple
background, white background, determined
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06403-1784443102-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya5th, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya5th, mole under eye, x hair
ornament, black choker, beret, grey jacket, white dress, open jacket, closed
mouth, holding, holding cup, holding plate, sitting, solo, colored pencil
(medium), indoors, painting (medium), slug, traditional media, water,
watercolor (medium), wet, relieved
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06401-3811754092-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya5th, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya5th, mole under eye, x hair
ornament, black choker, beret, grey jacket, white dress, open jacket, closed
mouth, cropped legs, hands in pockets, looking at viewer, solo, standing,
autumn, leaf, radiation symbol, star (symbol), tree, white background, wind,
depressed
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06399-4103591333-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya5th, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya4rd, mole under eye, x hair
ornament, black choker, purple dress, cleavage cutout, heart cutout, maid
headdress, purple gloves, corset, wrist cuffs blush, closed mouth, food in
mouth, half-closed eyes, looking at viewer, mouth hold, solo, v arms, bird,
floral background, flower, personification, rose, striped, happy
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06398-2804258537-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya4rd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya4rd, mole under eye, x hair
ornament, black choker, purple dress, cleavage cutout, heart cutout, maid
headdress, purple gloves, corset, wrist cuffs :d, blush, chibi, looking at
viewer, open mouth, smile, solo, standing, flower, stuffed animal, stuffed
rabbit, stuffed toy, white flower, hopeful
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06397-1381099730-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya4rd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya4rd, mole under eye, x hair
ornament, black choker, purple dress, cleavage cutout, heart cutout, maid
headdress, purple gloves, corset, wrist cuffs closed mouth, looking away,
looking to the side, solo, argyle, argyle background, bat (animal), heart,
confident
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06396-1998245858-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya4rd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya3rd, mole under eye, x hair
ornament, hairclip, school uniform, white shirt, black sailor collar,
serafuku, black skirt, glasses, bent over, cellphone, expressionless,
holding, holding bowl, holding cup, legs apart, solo, floating hair, from
above, full body, reflection, reflective water, sky, star (sky), starry sky,
striped, shocked
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06395-3109491790-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya3rd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya3rd, mole under eye, x hair
ornament, hairclip, school uniform, white shirt, black sailor collar,
serafuku, black skirt, glasses, closed mouth, finger to mouth, hand up,
looking at viewer, shushing, smile, solo, + +, drinking straw, grey
background, milk carton, polka dot background, signature, upper body,
thoughtful
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06394-1290925342-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya3rd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya3rd, mole under eye, x hair
ornament, hairclip, school uniform, white shirt, black sailor collar,
serafuku, black skirt, glasses, bathing, solo, day, from side, indoors,
photo (object), sleepy
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06393-1706537189-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya3rd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya2nd, mole under eye, x hair
ornament, black choker, black nails, hair flower, blue rose, eyepatch, mini
top hat, see-through, black dress, gothic lolita, lolita fashion, frills,
frilled dress, :/, looking at viewer, solo, blue sky, cloud, scenery, sky,
envious
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06391-2435279704-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya2nd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya2nd, mole under eye, x hair
ornament, black choker, black nails, hair flower, blue rose, eyepatch, mini
top hat, see-through, black dress, gothic lolita, lolita fashion, frills,
frilled dress, flower in mouth, holding, holding jewelry, looking away,
lying, mouth hold, on back, parted lips, profile, sitting, solo, afloat,
blurry, depth of field, food, fruit, grapes, leaf, partially submerged,
upside-down, water, wet, hungry
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06390-3121340074-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya2nd, mole
under eye.png
- text: >-
score_9, score_8_up, score_7_up, uncensored, source_anime, 1girl,
<lora:spsukoyaKanaXLPony-000003:1> sukoya2nd, mole under eye, x hair
ornament, black choker, black nails, hair flower, blue rose, eyepatch, mini
top hat, see-through, black dress, gothic lolita, lolita fashion, frills,
frilled dress, blush, d:, looking at viewer, one eye closed, open mouth,
sitting, solo, wariza, wavy mouth, box, gift, gift box, indoors, :q
parameters:
negative_prompt: 3d, monochrome, greyscale
output:
url: >-
images/06389-2836207797-score_9, score_8_up, score_7_up, uncensored,
source_anime, 1girl, _lora_spsukoyaKanaXLPony-000003_1_ sukoya2nd, mole
under eye.png
base_model: AstraliteHeart/pony-diffusion-v6
instance_prompt: null
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
---
# Sukoya Kana - Nijisanji
<Gallery />
## Model description
Sukoya Kana - Nijisanji!
Trained on 5 outfits, it has a trigger word corresponding to the appearance of the character and suggested prompts that summons related clothes and accesories.
Works well with 0.7-1.0 weight
## Trigger words
Debut Outfit:
`sukoya1st, ahoge, mole under eye, x hair ornament, nurse cap, bandaged arm, bandages, white apron, white wrist cuffs, white gloves, nurse dress`
Second Outfit:
`sukoya2nd, mole under eye, x hair ornament, black choker, black nails, hair flower, blue rose, eyepatch, mini top hat, see-through, black dress, gothic lolita, lolita fashion, frills, frilled dress`
Third Outfit:
`sukoya3rd, mole under eye, x hair ornament, hairclip, school uniform, white shirt, black sailor collar, serafuku, black skirt, glasses`
Fourth Outfit:
`sukoya4rd, mole under eye, x hair ornament, black choker, purple dress, cleavage cutout, heart cutout, maid headdress, purple gloves, corset, wrist cuffs`
Fifth Outfit:
`sukoya5th, mole under eye, x hair ornament, black choker, beret, grey jacket, white dress, open jacket`
## Download model
Weights for this model are available in Safetensors format.
[Download](/Shalie/SukoyaKanaPonyXL/tree/main) them in the Files & versions tab.
### License
This LoRA model is provided under the [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/) license.
## Restrictions:
- **Usage in Generation Services**: You are not allowed to use the model in any generation services without proper permission from the original creator.
- **Commercial Usage**: The sale of the model or any commercial usage is strictly prohibited without explicit written permission from the original creator.
|
MuhammadRozaq2001/idefics-9b-radVQA-smallEpoch
|
MuhammadRozaq2001
| 2024-06-04T17:48:04Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HuggingFaceM4/idefics-9b",
"base_model:adapter:HuggingFaceM4/idefics-9b",
"region:us"
] | null | 2024-06-04T17:08:55Z |
---
library_name: peft
base_model: HuggingFaceM4/idefics-9b
---
# 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 0.11.2.dev0
|
malteos/scincl
|
malteos
| 2024-06-04T17:45:02Z | 20,157 | 34 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"transformers",
"en",
"dataset:SciDocs",
"dataset:s2orc",
"arxiv:2202.06671",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
tags:
- feature-extraction
- sentence-transformers
- transformers
library_name: sentence-transformers
language: en
datasets:
- SciDocs
- s2orc
metrics:
- F1
- accuracy
- map
- ndcg
license: mit
---
## SciNCL
SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers.
It uses the citation graph neighborhood to generate samples for contrastive learning.
Prior to the contrastive training, the model is initialized with weights from [scibert-scivocab-uncased](https://huggingface.co/allenai/scibert_scivocab_uncased).
The underlying citation embeddings are trained on the [S2ORC citation graph](https://github.com/allenai/s2orc).
Paper: [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings (EMNLP 2022 paper)](https://arxiv.org/abs/2202.06671).
Code: https://github.com/malteos/scincl
PubMedNCL: Working with biomedical papers? Try [PubMedNCL](https://huggingface.co/malteos/PubMedNCL).
## How to use the pretrained model
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("malteos/scincl")
# Concatenate the title and abstract with the [SEP] token
papers = [
"BERT [SEP] We introduce a new language representation model called BERT",
"Attention is all you need [SEP] The dominant sequence transduction models are based on complex recurrent or convolutional neural networks",
]
# Inference
embeddings = model.encode(papers)
# Compute the (cosine) similarity between embeddings
similarity = model.similarity(embeddings[0], embeddings[1])
print(similarity.item())
# => 0.8440517783164978
```
### Transformers
```python
from transformers import AutoTokenizer, AutoModel
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('malteos/scincl')
model = AutoModel.from_pretrained('malteos/scincl')
papers = [{'title': 'BERT', 'abstract': 'We introduce a new language representation model called BERT'},
{'title': 'Attention is all you need', 'abstract': ' The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'}]
# concatenate title and abstract with [SEP] token
title_abs = [d['title'] + tokenizer.sep_token + (d.get('abstract') or '') for d in papers]
# preprocess the input
inputs = tokenizer(title_abs, padding=True, truncation=True, return_tensors="pt", max_length=512)
# inference
result = model(**inputs)
# take the first token ([CLS] token) in the batch as the embedding
embeddings = result.last_hidden_state[:, 0, :]
# calculate the similarity
embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
similarity = (embeddings[0] @ embeddings[1].T)
print(similarity.item())
# => 0.8440518379211426
```
## Triplet Mining Parameters
| **Setting** | **Value** |
|-------------------------|--------------------|
| seed | 4 |
| triples_per_query | 5 |
| easy_positives_count | 5 |
| easy_positives_strategy | 5 |
| easy_positives_k | 20-25 |
| easy_negatives_count | 3 |
| easy_negatives_strategy | random_without_knn |
| hard_negatives_count | 2 |
| hard_negatives_strategy | knn |
| hard_negatives_k | 3998-4000 |
## SciDocs Results
These model weights are the ones that yielded the best results on SciDocs (`seed=4`).
In the paper we report the SciDocs results as mean over ten seeds.
| **model** | **mag-f1** | **mesh-f1** | **co-view-map** | **co-view-ndcg** | **co-read-map** | **co-read-ndcg** | **cite-map** | **cite-ndcg** | **cocite-map** | **cocite-ndcg** | **recomm-ndcg** | **recomm-P@1** | **Avg** |
|-------------------|-----------:|------------:|----------------:|-----------------:|----------------:|-----------------:|-------------:|--------------:|---------------:|----------------:|----------------:|---------------:|--------:|
| Doc2Vec | 66.2 | 69.2 | 67.8 | 82.9 | 64.9 | 81.6 | 65.3 | 82.2 | 67.1 | 83.4 | 51.7 | 16.9 | 66.6 |
| fasttext-sum | 78.1 | 84.1 | 76.5 | 87.9 | 75.3 | 87.4 | 74.6 | 88.1 | 77.8 | 89.6 | 52.5 | 18 | 74.1 |
| SGC | 76.8 | 82.7 | 77.2 | 88 | 75.7 | 87.5 | 91.6 | 96.2 | 84.1 | 92.5 | 52.7 | 18.2 | 76.9 |
| SciBERT | 79.7 | 80.7 | 50.7 | 73.1 | 47.7 | 71.1 | 48.3 | 71.7 | 49.7 | 72.6 | 52.1 | 17.9 | 59.6 |
| SPECTER | 82 | 86.4 | 83.6 | 91.5 | 84.5 | 92.4 | 88.3 | 94.9 | 88.1 | 94.8 | 53.9 | 20 | 80 |
| SciNCL (10 seeds) | 81.4 | 88.7 | 85.3 | 92.3 | 87.5 | 93.9 | 93.6 | 97.3 | 91.6 | 96.4 | 53.9 | 19.3 | 81.8 |
| **SciNCL (seed=4)** | 81.2 | 89.0 | 85.3 | 92.2 | 87.7 | 94.0 | 93.6 | 97.4 | 91.7 | 96.5 | 54.3 | 19.6 | 81.9 |
Additional evaluations are available in the paper.
## License
MIT
|
Andhikuys/emotion_recog
|
Andhikuys
| 2024-06-04T17:44:03Z | 218 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-06-04T17:43:44Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: emotion_recog
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5375
---
<!-- 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. -->
# emotion_recog
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2377
- Accuracy: 0.5375
## 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: 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 10 | 1.6772 | 0.4062 |
| No log | 2.0 | 20 | 1.5802 | 0.4437 |
| No log | 3.0 | 30 | 1.4877 | 0.4875 |
| No log | 4.0 | 40 | 1.4649 | 0.475 |
| No log | 5.0 | 50 | 1.4092 | 0.5 |
| No log | 6.0 | 60 | 1.3454 | 0.5188 |
| No log | 7.0 | 70 | 1.3469 | 0.5312 |
| No log | 8.0 | 80 | 1.3010 | 0.5375 |
| No log | 9.0 | 90 | 1.2688 | 0.5563 |
| No log | 10.0 | 100 | 1.2854 | 0.5563 |
| No log | 11.0 | 110 | 1.2516 | 0.5437 |
| No log | 12.0 | 120 | 1.2819 | 0.5312 |
| No log | 13.0 | 130 | 1.2228 | 0.5875 |
| No log | 14.0 | 140 | 1.2250 | 0.5813 |
| No log | 15.0 | 150 | 1.2177 | 0.5563 |
| No log | 16.0 | 160 | 1.2172 | 0.55 |
| No log | 17.0 | 170 | 1.2198 | 0.6 |
| No log | 18.0 | 180 | 1.2341 | 0.5563 |
| No log | 19.0 | 190 | 1.2206 | 0.6 |
| No log | 20.0 | 200 | 1.1635 | 0.5813 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
m1b/2024_06_04_act_reachy2_teleop_remi_aug_60000
|
m1b
| 2024-06-04T17:42:51Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pytorch_model_hub_mixin",
"model_hub_mixin",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T17:42:50Z |
---
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
|
Hrishikesh11/llama-3-8b-Instruct-bnb-4bit-medical
|
Hrishikesh11
| 2024-06-04T17:41:17Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T16:44:08Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** Hrishikesh11
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rajtest/tinyllama-v1
|
rajtest
| 2024-06-04T17:33:54Z | 107 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/tinyllama-bnb-4bit",
"base_model:adapter:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"region:us"
] | null | 2024-05-30T13:06:08Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- unsloth
- generated_from_trainer
base_model: unsloth/tinyllama-bnb-4bit
model-index:
- name: tinyllama-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tinyllama-v1
This model is a fine-tuned version of [unsloth/tinyllama-bnb-4bit](https://huggingface.co/unsloth/tinyllama-bnb-4bit) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 3407
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
dhruvk29/Airavata-Q4_K_M-GGUF
|
dhruvk29
| 2024-06-04T17:30:52Z | 0 | 0 | null |
[
"gguf",
"multilingual",
"instruction-tuning",
"llama2",
"llama-cpp",
"gguf-my-repo",
"en",
"hi",
"dataset:ai4bharat/indic-instruct-data-v0.1",
"base_model:ai4bharat/Airavata",
"base_model:quantized:ai4bharat/Airavata",
"license:llama2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T17:30:41Z |
---
language:
- en
- hi
license: llama2
tags:
- multilingual
- instruction-tuning
- llama2
- llama-cpp
- gguf-my-repo
base_model: ai4bharat/Airavata
datasets:
- ai4bharat/indic-instruct-data-v0.1
model-index:
- name: Airavata
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 46.5
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ai4bharat/Airavata
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 69.26
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ai4bharat/Airavata
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 43.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ai4bharat/Airavata
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 40.62
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ai4bharat/Airavata
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ai4bharat/Airavata
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 4.02
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ai4bharat/Airavata
name: Open LLM Leaderboard
---
# dhruvk29/Airavata-Q4_K_M-GGUF
This model was converted to GGUF format from [`ai4bharat/Airavata`](https://huggingface.co/ai4bharat/Airavata) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ai4bharat/Airavata) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama --hf-repo dhruvk29/Airavata-Q4_K_M-GGUF --hf-file airavata-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo dhruvk29/Airavata-Q4_K_M-GGUF --hf-file airavata-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./main --hf-repo dhruvk29/Airavata-Q4_K_M-GGUF --hf-file airavata-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./server --hf-repo dhruvk29/Airavata-Q4_K_M-GGUF --hf-file airavata-q4_k_m.gguf -c 2048
```
|
tyzhu/squad_qa_baseline_v5_full_Qwen_Qwen1.5-4B_3e-5_lora
|
tyzhu
| 2024-06-04T17:28:13Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-4B",
"base_model:adapter:Qwen/Qwen1.5-4B",
"license:other",
"region:us"
] | null | 2024-06-04T14:54:16Z |
---
license: other
base_model: Qwen/Qwen1.5-4B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: squad_qa_baseline_v5_full_Qwen_Qwen1.5-4B_3e-5_lora
results: []
library_name: peft
---
<!-- 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. -->
# squad_qa_baseline_v5_full_Qwen_Qwen1.5-4B_3e-5_lora
This model is a fine-tuned version of [Qwen/Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8632
- Accuracy: 0.5660
## 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: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.9916 | 74 | 2.0550 | 0.5952 |
| 2.3403 | 1.9966 | 149 | 2.0411 | 0.5933 |
| 2.0198 | 2.9883 | 223 | 2.0403 | 0.5932 |
| 2.0198 | 3.9933 | 298 | 2.0647 | 0.5922 |
| 1.9239 | 4.9983 | 373 | 2.0999 | 0.5921 |
| 1.7309 | 5.9899 | 447 | 2.1973 | 0.5879 |
| 1.5254 | 6.9950 | 522 | 2.2753 | 0.5861 |
| 1.5254 | 8.0 | 597 | 2.4079 | 0.5819 |
| 1.2937 | 8.9916 | 671 | 2.5096 | 0.5775 |
| 1.0409 | 9.9966 | 746 | 2.6079 | 0.5739 |
| 0.8766 | 10.9883 | 820 | 2.7579 | 0.5718 |
| 0.8766 | 11.9933 | 895 | 2.8722 | 0.5688 |
| 0.721 | 12.9983 | 970 | 2.9797 | 0.5672 |
| 0.6011 | 13.9899 | 1044 | 3.0708 | 0.5662 |
| 0.5455 | 14.9950 | 1119 | 3.1660 | 0.5648 |
| 0.5455 | 16.0 | 1194 | 3.2479 | 0.5650 |
| 0.5003 | 16.9916 | 1268 | 3.2445 | 0.5655 |
| 0.4683 | 17.9966 | 1343 | 3.2800 | 0.5638 |
| 0.457 | 18.9883 | 1417 | 3.4280 | 0.5640 |
| 0.457 | 19.9933 | 1492 | 3.4113 | 0.5662 |
| 0.4441 | 20.9983 | 1567 | 3.4731 | 0.5637 |
| 0.4327 | 21.9899 | 1641 | 3.5407 | 0.5639 |
| 0.4308 | 22.9950 | 1716 | 3.4811 | 0.5640 |
| 0.4308 | 24.0 | 1791 | 3.5854 | 0.5642 |
| 0.4245 | 24.9916 | 1865 | 3.5206 | 0.5640 |
| 0.416 | 25.9966 | 1940 | 3.6091 | 0.5638 |
| 0.4173 | 26.9883 | 2014 | 3.5707 | 0.5643 |
| 0.4173 | 27.9933 | 2089 | 3.6671 | 0.5648 |
| 0.4117 | 28.9983 | 2164 | 3.6267 | 0.5631 |
| 0.409 | 29.9899 | 2238 | 3.6658 | 0.5604 |
| 0.4085 | 30.9950 | 2313 | 3.6984 | 0.5621 |
| 0.4085 | 32.0 | 2388 | 3.6584 | 0.5660 |
| 0.403 | 32.9916 | 2462 | 3.5848 | 0.5626 |
| 0.404 | 33.9966 | 2537 | 3.6365 | 0.5631 |
| 0.4013 | 34.9883 | 2611 | 3.7047 | 0.5647 |
| 0.4013 | 35.9933 | 2686 | 3.7735 | 0.5643 |
| 0.3987 | 36.9983 | 2761 | 3.6867 | 0.5657 |
| 0.3951 | 37.9899 | 2835 | 3.7349 | 0.5662 |
| 0.3971 | 38.9950 | 2910 | 3.7173 | 0.5643 |
| 0.3971 | 40.0 | 2985 | 3.8004 | 0.5643 |
| 0.3939 | 40.9916 | 3059 | 3.8041 | 0.5636 |
| 0.3912 | 41.9966 | 3134 | 3.8263 | 0.5648 |
| 0.3941 | 42.9883 | 3208 | 3.7954 | 0.5646 |
| 0.3941 | 43.9933 | 3283 | 3.8001 | 0.5637 |
| 0.3878 | 44.9983 | 3358 | 3.8438 | 0.5634 |
| 0.3879 | 45.9899 | 3432 | 3.8626 | 0.5631 |
| 0.3907 | 46.9950 | 3507 | 3.7882 | 0.5645 |
| 0.3907 | 48.0 | 3582 | 3.8001 | 0.5622 |
| 0.3864 | 48.9916 | 3656 | 3.7201 | 0.5609 |
| 0.3871 | 49.5812 | 3700 | 3.8632 | 0.5660 |
### Framework versions
- PEFT 0.5.0
- Transformers 4.40.2
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
zzy2524/french_double_negation_model
|
zzy2524
| 2024-06-04T17:22:51Z | 194 | 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
| 2024-06-04T16:58:14Z |
---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: french_double_negation_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. -->
# french_double_negation_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0315
- F1: 1.0
- Roc Auc: 1.0
- Accuracy: 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---:|:-------:|:--------:|
| No log | 1.0 | 153 | 0.0539 | 1.0 | 1.0 | 1.0 |
| No log | 2.0 | 306 | 0.0315 | 1.0 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
Niggendar/bluephoenixponymix_v10
|
Niggendar
| 2024-06-04T17:18:10Z | 122 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-06-04T13:35:11Z |
---
library_name: diffusers
---
# 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 🧨 diffusers 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]
|
coderbojack/google-gemma-2b-1717520428
|
coderbojack
| 2024-06-04T17:10:55Z | 143 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T17:00:29Z |
---
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]
|
omikhailovskii/ppo-Huggy
|
omikhailovskii
| 2024-06-04T17:05:51Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-06-04T17:04:23Z |
---
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: omikhailovskii/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
shirleyah/q30_explicit
|
shirleyah
| 2024-06-04T17:02:03Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2024-06-04T16:44:56Z |
---
license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: q30_explicit
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. -->
# q30_explicit
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
geeknix/llama-simple-7B-4-june-adapters
|
geeknix
| 2024-06-04T16:57:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"en",
"arxiv:2307.09288",
"license:llama2",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T16:14:36Z |
---
extra_gated_heading: You need to share contact information with Meta to access this model
extra_gated_prompt: >-
### LLAMA 2 COMMUNITY LICENSE AGREEMENT
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"Llama 2" means the foundational large language models and software and
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"Llama Materials" means, collectively, Meta's proprietary Llama 2 and
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5. Sexual solicitation
6. Any other criminal activity
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials
7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or
development of activities that present a risk of death or bodily harm to
individuals, including use of Llama 2 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 2 related
to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Llama 2 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems
that could lead to a violation of this Policy through one of the following
means:
* Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com)
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: >-
The information you provide will be collected, stored, processed and shared in
accordance with the [Meta Privacy
Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
license: llama2
library_name: transformers
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)|
|70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|
|
0xWe11es/camel-llama2-h256-w1
|
0xWe11es
| 2024-06-04T16:56:35Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T16:43:16Z |
---
license: apache-2.0
---
|
flammenai/Mahou-1.2b-mistral-7B-GGUF
|
flammenai
| 2024-06-04T16:56:29Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"dataset:flammenai/MahouMix-v1",
"dataset:flammenai/FlameMix-DPO-v1",
"base_model:flammenai/Mahou-1.2b-mistral-7B",
"base_model:quantized:flammenai/Mahou-1.2b-mistral-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-04T14:43:24Z |
---
library_name: transformers
license: apache-2.0
base_model:
- nbeerbower/Mahou-1.2b-mistral-7B
datasets:
- flammenai/MahouMix-v1
- flammenai/FlameMix-DPO-v1
---
# Mahou-1.2b-mistral-7B
Mahou is designed to provide short messages in a conversational context. It is capable of casual conversation and character roleplay.
### Chat Format
This model has been trained to use ChatML format.
```
<|im_start|>system
{{system}}<|im_end|>
<|im_start|>{{char}}
{{message}}<|im_end|>
<|im_start|>{{user}}
{{message}}<|im_end|>
```
### Roleplay Format
- Speech without quotes.
- Actions in `*asterisks*`
```
*leans against wall cooly* so like, i just casted a super strong spell at magician academy today, not gonna lie, felt badass.
```
### SillyTavern Settings
1. Use ChatML for the Context Template.
2. Enable Instruct Mode.
3. Use the [Mahou preset](https://huggingface.co/datasets/flammenai/Mahou-ST-ChatML-Instruct/raw/main/Mahou.json).
4. *Recommended* Additonal stopping strings: `["\n", "<|", "</"]`
### Method
DPO finetuned using an A100 on Google Colab.
[Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)
### Configuration
LoRA, model, and training settings:
```python
# LoRA configuration
peft_config = LoraConfig(
r=16,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
)
# Model to fine-tune
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
load_in_4bit=True
)
model.config.use_cache = False
# Reference model
ref_model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
load_in_4bit=True
)
# Training arguments
training_args = TrainingArguments(
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
learning_rate=5e-5,
lr_scheduler_type="cosine",
max_steps=200,
save_strategy="no",
logging_steps=1,
output_dir=new_model,
optim="paged_adamw_32bit",
warmup_steps=100,
bf16=True,
report_to="wandb",
)
# Create DPO trainer
dpo_trainer = DPOTrainer(
model,
ref_model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
peft_config=peft_config,
beta=0.1,
force_use_ref_model=True
)
# Fine-tune model with DPO
dpo_trainer.train()
```
|
hdve/google-gemma-7b-1717519870
|
hdve
| 2024-06-04T16:54:03Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T16:51:12Z |
---
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]
|
Jingmei/PMC_LLAMA2_7B_trainer_lora
|
Jingmei
| 2024-06-04T16:52:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:chaoyi-wu/PMC_LLAMA_7B",
"base_model:adapter:chaoyi-wu/PMC_LLAMA_7B",
"license:apache-2.0",
"region:us"
] | null | 2024-05-31T20:38:05Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: chaoyi-wu/PMC_LLAMA_7B
model-index:
- name: PMC_LLAMA2_7B_trainer_lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/noc-lab/PMC_LLAMA2_7B_trainer_lora/runs/qm80zll8)
# PMC_LLAMA2_7B_trainer_lora
This model is a fine-tuned version of [chaoyi-wu/PMC_LLAMA_7B](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 123
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 8
- total_train_batch_size: 1152
- total_eval_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
### Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.42.0.dev0
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
chreh/math-lora-llama-3-8B
|
chreh
| 2024-06-04T16:51:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-28T20:33:51Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** chreh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2
|
Zoyd
| 2024-06-04T16:49:54Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T16:36:05Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: What's the difference between a banana and a strawberry?
---
**Exllamav2** quant (**exl2** / **5.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2)**</center> | <center>1217 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_5bpw_exl2)**</center> | <center>1342 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_0bpw_exl2)**</center> | <center>1558 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_5bpw_exl2)**</center> | <center>1774 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_75bpw_exl2)**</center> | <center>1882 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2)**</center> | <center>1990 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_25bpw_exl2)**</center> | <center>2099 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2)**</center> | <center>2423 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2)**</center> | <center>2870 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2)**</center> | <center>3089 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2)**</center> | <center>3620 MB</center> | <center>8</center> |
# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified
Credit to [u/Anduin1357](https://www.reddit.com/user/Anduin1357/) on reddit for the name who [wrote this comment](https://www.reddit.com/r/LocalLLaMA/comments/1cmh6ru/comment/l31zkan/)
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
## What's this?
Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.
## Summary
This is [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.
|
Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2
|
Zoyd
| 2024-06-04T16:49:04Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T16:45:41Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: What's the difference between a banana and a strawberry?
---
**Exllamav2** quant (**exl2** / **6.5 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2)**</center> | <center>1217 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_5bpw_exl2)**</center> | <center>1342 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_0bpw_exl2)**</center> | <center>1558 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_5bpw_exl2)**</center> | <center>1774 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_75bpw_exl2)**</center> | <center>1882 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2)**</center> | <center>1990 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_25bpw_exl2)**</center> | <center>2099 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2)**</center> | <center>2423 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2)**</center> | <center>2870 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2)**</center> | <center>3089 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2)**</center> | <center>3620 MB</center> | <center>8</center> |
# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified
Credit to [u/Anduin1357](https://www.reddit.com/user/Anduin1357/) on reddit for the name who [wrote this comment](https://www.reddit.com/r/LocalLLaMA/comments/1cmh6ru/comment/l31zkan/)
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
## What's this?
Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.
## Summary
This is [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.
|
Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2
|
Zoyd
| 2024-06-04T16:48:58Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T16:33:34Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: What's the difference between a banana and a strawberry?
---
**Exllamav2** quant (**exl2** / **4.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2)**</center> | <center>1217 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_5bpw_exl2)**</center> | <center>1342 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_0bpw_exl2)**</center> | <center>1558 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_5bpw_exl2)**</center> | <center>1774 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_75bpw_exl2)**</center> | <center>1882 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2)**</center> | <center>1990 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_25bpw_exl2)**</center> | <center>2099 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2)**</center> | <center>2423 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2)**</center> | <center>2870 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2)**</center> | <center>3089 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2)**</center> | <center>3620 MB</center> | <center>8</center> |
# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified
Credit to [u/Anduin1357](https://www.reddit.com/user/Anduin1357/) on reddit for the name who [wrote this comment](https://www.reddit.com/r/LocalLLaMA/comments/1cmh6ru/comment/l31zkan/)
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
## What's this?
Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.
## Summary
This is [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.
|
Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2
|
Zoyd
| 2024-06-04T16:48:28Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T16:42:02Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: What's the difference between a banana and a strawberry?
---
**Exllamav2** quant (**exl2** / **8.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2)**</center> | <center>1217 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_5bpw_exl2)**</center> | <center>1342 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_0bpw_exl2)**</center> | <center>1558 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_5bpw_exl2)**</center> | <center>1774 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_75bpw_exl2)**</center> | <center>1882 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2)**</center> | <center>1990 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_25bpw_exl2)**</center> | <center>2099 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2)**</center> | <center>2423 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2)**</center> | <center>2870 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2)**</center> | <center>3089 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2)**</center> | <center>3620 MB</center> | <center>8</center> |
# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified
Credit to [u/Anduin1357](https://www.reddit.com/user/Anduin1357/) on reddit for the name who [wrote this comment](https://www.reddit.com/r/LocalLLaMA/comments/1cmh6ru/comment/l31zkan/)
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
## What's this?
Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.
## Summary
This is [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.
|
Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2
|
Zoyd
| 2024-06-04T16:48:22Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T16:39:24Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: What's the difference between a banana and a strawberry?
---
**Exllamav2** quant (**exl2** / **6.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2)**</center> | <center>1217 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_5bpw_exl2)**</center> | <center>1342 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_0bpw_exl2)**</center> | <center>1558 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_5bpw_exl2)**</center> | <center>1774 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_75bpw_exl2)**</center> | <center>1882 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2)**</center> | <center>1990 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_25bpw_exl2)**</center> | <center>2099 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2)**</center> | <center>2423 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2)**</center> | <center>2870 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2)**</center> | <center>3089 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2)**</center> | <center>3620 MB</center> | <center>8</center> |
# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified
Credit to [u/Anduin1357](https://www.reddit.com/user/Anduin1357/) on reddit for the name who [wrote this comment](https://www.reddit.com/r/LocalLLaMA/comments/1cmh6ru/comment/l31zkan/)
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
## What's this?
Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.
## Summary
This is [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.
|
Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2
|
Zoyd
| 2024-06-04T16:48:01Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T16:08:34Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-medium-4k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: What's the difference between a banana and a strawberry?
---
**Exllamav2** quant (**exl2** / **2.2 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_2bpw_exl2)**</center> | <center>1217 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-2_5bpw_exl2)**</center> | <center>1342 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_0bpw_exl2)**</center> | <center>1558 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_5bpw_exl2)**</center> | <center>1774 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-3_75bpw_exl2)**</center> | <center>1882 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_0bpw_exl2)**</center> | <center>1990 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-4_25bpw_exl2)**</center> | <center>2099 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-5_0bpw_exl2)**</center> | <center>2423 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_0bpw_exl2)**</center> | <center>2870 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-6_5bpw_exl2)**</center> | <center>3089 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/failspy_Phi-3-mini-4k-geminified-8_0bpw_exl2)**</center> | <center>3620 MB</center> | <center>8</center> |
# Phi-3-mini-128k-instruct- ~~abliterated-v3~~ -geminified
Credit to [u/Anduin1357](https://www.reddit.com/user/Anduin1357/) on reddit for the name who [wrote this comment](https://www.reddit.com/r/LocalLLaMA/comments/1cmh6ru/comment/l31zkan/)
[My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb)
## What's this?
Well, after my abliterated models, I figured I should cover all the possible ground of such work and introduce a model that acts like the polar opposite of them. This is the result of that, and I feel it lines it up in performance to a certain search engine's AI model series.
## Summary
This is [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
This model has been orthogonalized to act more like certain rhymes-with-Shmemini models.
|
Likich/tinyllama-finetune-qualcoding_1000_prompt6
|
Likich
| 2024-06-04T16:40:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T16:40:39Z |
---
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]
|
Firemedic15/Tsxi-V3
|
Firemedic15
| 2024-06-04T16:39:58Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-04T16:39:54Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Tsxi-V3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.68
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Firemedic15/Tsxi-V3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
iloncka/exp_5_new_bg_simple-subs_1_v_5_vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k_ep_60
|
iloncka
| 2024-06-04T16:31:24Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2024-06-04T13:22:12Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Likich/gemmainstruct-finetune-qualcoding_1000_prompt6
|
Likich
| 2024-06-04T16:29:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T16:29:04Z |
---
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]
|
MohammadKhosravi/llama-3-8b-Instruct-bnb-4bit-Galilo-v.1.0
|
MohammadKhosravi
| 2024-06-04T16:28:30Z | 6 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"gguf",
"llama",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:adapter:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-04T15:54:05Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- unsloth
- generated_from_trainer
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
model-index:
- name: llama-3-8b-Instruct-bnb-4bit-Galilo-v.1.0
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. -->
# llama-3-8b-Instruct-bnb-4bit-Galilo-v.1.0
This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit) 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- 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: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 0
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
iloncka/exp_5_objects-subs_1_v_5_vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k_ep_60
|
iloncka
| 2024-06-04T16:25:35Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2024-06-04T12:46:29Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
yuzhe-123/zephyr-7b-sft-full
|
yuzhe-123
| 2024-06-04T16:25:05Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrachat_200k",
"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
| 2024-06-02T09:09:19Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrachat_200k
model-index:
- name: zephyr-7b-sft-full
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-sft-full
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9367
## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9203 | 1.0 | 1090 | 0.9367 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0
- Datasets 2.19.2
- Tokenizers 0.19.1
|
magnifi/phi-3-mini-4k-instruct-attribute-output-4-0603-epoch7-v4mod4-0.002_fulldata
|
magnifi
| 2024-06-04T16:18:01Z | 79 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T16:16:12Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
manbull/Qwen-Qwen1.5-0.5B-1717517619
|
manbull
| 2024-06-04T16:14:25Z | 143 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T16:13:40Z |
---
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]
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## Glossary [optional]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RefalMachine/ruadapt_llama3_bpe_extended_part1-2_vo_1e4_no_wd_bs256
|
RefalMachine
| 2024-06-04T16:12:28Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-03T18:40:39Z |
---
library_name: transformers
license: llama3
---
# 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]
|
PaDaS-Lab/Llama3-8B-SPARQL-annotated
|
PaDaS-Lab
| 2024-06-04T16:11:03Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T15:54:43Z |
---
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]
|
iloncka/exp_5_new_bg_simple-subs_1_v_5_eva02_tiny_patch14_224.mim_in22k_ep_60
|
iloncka
| 2024-06-04T16:10:54Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2024-06-03T12:46:48Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Daytona-health-ml/dalai-llama-v1
|
Daytona-health-ml
| 2024-06-04T16:08:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T13:48:23Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: meta-llama/Llama-2-7b-hf
widget:
- messages:
- role: user
content: What is your favorite condiment?
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)
```
|
m1b/2024_06_04_act_reachy2_teleop_remi_aug_35K
|
m1b
| 2024-06-04T16:07:45Z | 51 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pytorch_model_hub_mixin",
"model_hub_mixin",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T16:07:18Z |
---
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed]
|
lalacelik/BirdClef-wav2vec
|
lalacelik
| 2024-06-04T16:07:15Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base-960h",
"base_model:finetune:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-05-31T13:14:43Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: BirdClef-wav2vec
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. -->
# BirdClef-wav2vec
This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the BirdCLEF24 dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.0204
- Loss: 4.6406
## 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: 3
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:-----:|:--------:|:---------------:|
| 4.6298 | 1.0 | 8153 | 0.0204 | 4.6469 |
| 4.649 | 2.0 | 16306 | 0.0204 | 4.6439 |
| 4.6759 | 3.0 | 24459 | 0.0204 | 4.6406 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
exlleysantos/verball-question-answer
|
exlleysantos
| 2024-06-04T16:06:38Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T11:01:53Z |
---
tags:
- generated_from_trainer
model-index:
- name: verball-question-answer
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. -->
# verball-question-answer
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.15.1
|
RIyacoool/ssss
|
RIyacoool
| 2024-06-04T16:01:33Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-06-04T16:01:33Z |
---
license: apache-2.0
---
|
Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-5_0bpw_exl2
|
Zoyd
| 2024-06-04T15:57:02Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T15:19:33Z |
---
base_model:
- Nitral-AI/Poppy-1.35-Phase1
- Nitral-AI/Pp-72xra1
library_name: transformers
tags:
- mergekit
- merge
license: other
language:
- en
---
**Exllamav2** quant (**exl2** / **5.0 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_2bpw_exl2)**</center> | <center>3250 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_5bpw_exl2)**</center> | <center>3478 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_0bpw_exl2)**</center> | <center>3894 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_5bpw_exl2)**</center> | <center>4311 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_75bpw_exl2)**</center> | <center>4518 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_0bpw_exl2)**</center> | <center>4727 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_25bpw_exl2)**</center> | <center>4935 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-5_0bpw_exl2)**</center> | <center>5559 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_0bpw_exl2)**</center> | <center>6489 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_5bpw_exl2)**</center> | <center>6909 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2)**</center> | <center>8123 MB</center> | <center>8</center> |
# "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences.
# Note: This variant is an attempt to get something closer to 0.72 while maintaining the improvements of 1.30.
# : [Presets in repo folder](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets).
# If you want to use vision functionality: You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). And need to load the specified **mmproj** file: [Llava MMProj](https://huggingface.co/Nitral-AI/Llama-3-Update-2.0-mmproj-model-f16).
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Nitral-AI/Pp-72xra1
layer_range: [0, 32]
- model: Nitral-AI/Poppy-1.35-Phase1
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Pp-72xra1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
bblain/deberta-v3-large-ocean-clf
|
bblain
| 2024-06-04T15:56:59Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-06-04T15:50:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
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<!-- This should link to a Dataset Card if possible. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_5bpw_exl2
|
Zoyd
| 2024-06-04T15:56:02Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T15:41:30Z |
---
base_model:
- Nitral-AI/Poppy-1.35-Phase1
- Nitral-AI/Pp-72xra1
library_name: transformers
tags:
- mergekit
- merge
license: other
language:
- en
---
**Exllamav2** quant (**exl2** / **6.5 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_2bpw_exl2)**</center> | <center>3250 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_5bpw_exl2)**</center> | <center>3478 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_0bpw_exl2)**</center> | <center>3894 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_5bpw_exl2)**</center> | <center>4311 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_75bpw_exl2)**</center> | <center>4518 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_0bpw_exl2)**</center> | <center>4727 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_25bpw_exl2)**</center> | <center>4935 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-5_0bpw_exl2)**</center> | <center>5559 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_0bpw_exl2)**</center> | <center>6489 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_5bpw_exl2)**</center> | <center>6909 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2)**</center> | <center>8123 MB</center> | <center>8</center> |
# "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences.
# Note: This variant is an attempt to get something closer to 0.72 while maintaining the improvements of 1.30.
# : [Presets in repo folder](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets).
# If you want to use vision functionality: You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). And need to load the specified **mmproj** file: [Llava MMProj](https://huggingface.co/Nitral-AI/Llama-3-Update-2.0-mmproj-model-f16).
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Nitral-AI/Pp-72xra1
layer_range: [0, 32]
- model: Nitral-AI/Poppy-1.35-Phase1
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Pp-72xra1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_5bpw_exl2
|
Zoyd
| 2024-06-04T15:55:42Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T14:31:09Z |
---
base_model:
- Nitral-AI/Poppy-1.35-Phase1
- Nitral-AI/Pp-72xra1
library_name: transformers
tags:
- mergekit
- merge
license: other
language:
- en
---
**Exllamav2** quant (**exl2** / **2.5 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_2bpw_exl2)**</center> | <center>3250 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_5bpw_exl2)**</center> | <center>3478 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_0bpw_exl2)**</center> | <center>3894 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_5bpw_exl2)**</center> | <center>4311 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_75bpw_exl2)**</center> | <center>4518 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_0bpw_exl2)**</center> | <center>4727 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_25bpw_exl2)**</center> | <center>4935 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-5_0bpw_exl2)**</center> | <center>5559 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_0bpw_exl2)**</center> | <center>6489 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_5bpw_exl2)**</center> | <center>6909 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2)**</center> | <center>8123 MB</center> | <center>8</center> |
# "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences.
# Note: This variant is an attempt to get something closer to 0.72 while maintaining the improvements of 1.30.
# : [Presets in repo folder](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets).
# If you want to use vision functionality: You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). And need to load the specified **mmproj** file: [Llava MMProj](https://huggingface.co/Nitral-AI/Llama-3-Update-2.0-mmproj-model-f16).
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Nitral-AI/Pp-72xra1
layer_range: [0, 32]
- model: Nitral-AI/Poppy-1.35-Phase1
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Pp-72xra1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
hdve/google-gemma-2b-1717516351
|
hdve
| 2024-06-04T15:54:48Z | 143 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-06-04T15:52:32Z |
---
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. -->
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## More Information [optional]
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## Model Card Contact
[More Information Needed]
|
Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_75bpw_exl2
|
Zoyd
| 2024-06-04T15:54:46Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] |
text-generation
| 2024-06-04T14:54:44Z |
---
base_model:
- Nitral-AI/Poppy-1.35-Phase1
- Nitral-AI/Pp-72xra1
library_name: transformers
tags:
- mergekit
- merge
license: other
language:
- en
---
**Exllamav2** quant (**exl2** / **3.75 bpw**) made with ExLlamaV2 v0.1.3
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_2bpw_exl2)**</center> | <center>3250 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-2_5bpw_exl2)**</center> | <center>3478 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_0bpw_exl2)**</center> | <center>3894 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_5bpw_exl2)**</center> | <center>4311 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-3_75bpw_exl2)**</center> | <center>4518 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_0bpw_exl2)**</center> | <center>4727 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-4_25bpw_exl2)**</center> | <center>4935 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-5_0bpw_exl2)**</center> | <center>5559 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_0bpw_exl2)**</center> | <center>6489 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-6_5bpw_exl2)**</center> | <center>6909 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/Nitral-AI_Poppy_Porpoise-1.4-L3-8B-8_0bpw_exl2)**</center> | <center>8123 MB</center> | <center>8</center> |
# "Poppy Porpoise" is a cutting-edge AI roleplay assistant based on the Llama 3 8B model, specializing in crafting unforgettable narrative experiences. With its advanced language capabilities, Poppy expertly immerses users in an interactive and engaging adventure, tailoring each adventure to their individual preferences.
# Note: This variant is an attempt to get something closer to 0.72 while maintaining the improvements of 1.30.
# : [Presets in repo folder](https://huggingface.co/Nitral-AI/Poppy_Porpoise-1.0-L3-8B/tree/main/Porpoise_1.0-Presets).
# If you want to use vision functionality: You must use the latest versions of [Koboldcpp](https://github.com/LostRuins/koboldcpp). And need to load the specified **mmproj** file: [Llava MMProj](https://huggingface.co/Nitral-AI/Llama-3-Update-2.0-mmproj-model-f16).
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Nitral-AI/Pp-72xra1
layer_range: [0, 32]
- model: Nitral-AI/Poppy-1.35-Phase1
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Pp-72xra1
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
tanchcliff/openai-whisper-large-v2-LORA-colab
|
tanchcliff
| 2024-06-04T15:53:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-04T15:53:34Z |
---
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]
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|
dasayantan/q-Taxi
|
dasayantan
| 2024-06-04T15:50:35Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-06-04T15:50:32Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.46 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dasayantan/q-Taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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