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kacieli/bart-large-cnn | kacieli | "2024-02-23T04:14:19Z" | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-02-22T06:04:59Z" | ---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn
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. -->
# bart-large-cnn
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2783
- Rouge1: 30.8678
- Rouge2: 14.3602
- Rougel: 25.3974
- Rougelsum: 28.9927
## 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: 5.6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| No log | 1.0 | 50 | 3.3345 | 27.661 | 11.692 | 22.9317 | 25.4531 |
| No log | 2.0 | 100 | 3.1512 | 31.6643 | 13.9022 | 24.8236 | 30.3283 |
| No log | 3.0 | 150 | 3.2783 | 30.8678 | 14.3602 | 25.3974 | 28.9927 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
somnathsingh31/llava-1.5-7b-hf-ft-museum | somnathsingh31 | "2024-05-03T04:15:26Z" | 4 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"base_model:adapter:llava-hf/llava-1.5-7b-hf",
"region:us"
] | null | "2024-04-30T12:09:56Z" | ---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: llava-hf/llava-1.5-7b-hf
model-index:
- name: llava-1.5-7b-hf-ft-museum-updated
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. -->
# llava-1.5-7b-hf-ft-museum-updated
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on the The Metropolitan
Museum of Art(https://www.metmuseum.org/art/collection) dataset.
## Model description
This project designed to enrich museum experiences by enabling users to explore and comprehend museum artifacts through image-based queries.
## Intended uses & limitations
**Intended uses:**
1. Enhance museum visitor experiences through interactive exploration via image-based queries.
2. Aid researchers and curators in retrieving information from museum artifacts.
3. Democratize access to museum knowledge by catering to a broader audience.
**Limitation:**
The model is fine tuned on a subset of data from the Met's collection that pertains to Indian heritage.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.4e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- 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
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 |
facebook/dpr-ctx_encoder-single-nq-base | facebook | "2022-12-21T15:16:53Z" | 77,578 | 24 | transformers | [
"transformers",
"pytorch",
"tf",
"dpr",
"en",
"dataset:nq_open",
"arxiv:2004.04906",
"arxiv:1702.08734",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"region:us"
] | null | "2022-03-02T23:29:05Z" | ---
language: en
license: cc-by-nc-4.0
tags:
- dpr
datasets:
- nq_open
inference: false
---
# `dpr-ctx_encoder-single-nq-base`
## Table of Contents
- [Model Details](#model-details)
- [How To Get Started With the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [Training](#training)
- [Evaluation](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications](#technical-specifications)
- [Citation Information](#citation-information)
- [Model Card Authors](#model-card-authors)
## Model Details
**Model Description:** [Dense Passage Retrieval (DPR)](https://github.com/facebookresearch/DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-ctx_encoder-single-nq-base` is the Context Encoder trained using the [Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open) ([Lee et al., 2019](https://aclanthology.org/P19-1612/); [Kwiatkowski et al., 2019](https://aclanthology.org/Q19-1026/)).
- **Developed by:** See [GitHub repo](https://github.com/facebookresearch/DPR) for model developers
- **Model Type:** BERT-based encoder
- **Language(s):** [CC-BY-NC-4.0](https://github.com/facebookresearch/DPR/blob/main/LICENSE), also see [Code of Conduct](https://github.com/facebookresearch/DPR/blob/main/CODE_OF_CONDUCT.md)
- **License:** English
- **Related Models:**
- [`dpr-question-encoder-single-nq-base`](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base)
- [`dpr-reader-single-nq-base`](https://huggingface.co/facebook/dpr-reader-single-nq-base)
- [`dpr-ctx_encoder-multiset-base`](https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base)
- [`dpr-question_encoder-multiset-base`](https://huggingface.co/facebook/dpr-question_encoder-multiset-base)
- [`dpr-reader-multiset-base`](https://huggingface.co/facebook/dpr-reader-multiset-base)
- **Resources for more information:**
- [Research Paper](https://arxiv.org/abs/2004.04906)
- [GitHub Repo](https://github.com/facebookresearch/DPR)
- [Hugging Face DPR docs](https://huggingface.co/docs/transformers/main/en/model_doc/dpr)
- [BERT Base Uncased Model Card](https://huggingface.co/bert-base-uncased)
## How to Get Started with the Model
Use the code below to get started with the model.
```python
>>> from transformers import DPRContextEncoder, DPRContextEncoderTokenizer
>>> tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
>>> model = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-single-nq-base")
>>> input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"]
>>> embeddings = model(input_ids).pooler_output
```
## Uses
#### Direct Use
`dpr-ctx_encoder-single-nq-base`, [`dpr-question-encoder-single-nq-base`](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base), and [`dpr-reader-single-nq-base`](https://huggingface.co/facebook/dpr-reader-single-nq-base) can be used for the task of open-domain question answering.
#### Misuse and Out-of-scope Use
The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model.
## Risks, Limitations and Biases
**CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propogate historical and current stereotypes.**
Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
## Training
#### Training Data
This model was trained using the [Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open) ([Lee et al., 2019](https://aclanthology.org/P19-1612/); [Kwiatkowski et al., 2019](https://aclanthology.org/Q19-1026/)). The model authors write that:
> [The dataset] was designed for end-to-end question answering. The questions were mined from real Google search queries and the answers were spans in Wikipedia articles identified by annotators.
#### Training Procedure
The training procedure is described in the [associated paper](https://arxiv.org/pdf/2004.04906.pdf):
> Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low-dimensional and continuous space, such that it can retrieve efficiently the top k passages relevant to the input question for the reader at run-time.
> Our dense passage retriever (DPR) uses a dense encoder EP(·) which maps any text passage to a d- dimensional real-valued vectors and builds an index for all the M passages that we will use for retrieval. At run-time, DPR applies a different encoder EQ(·) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to the question vector.
The authors report that for encoders, they used two independent BERT ([Devlin et al., 2019](https://aclanthology.org/N19-1423/)) networks (base, un-cased) and use FAISS ([Johnson et al., 2017](https://arxiv.org/abs/1702.08734)) during inference time to encode and index passages. See the paper for further details on training, including encoders, inference, positive and negative passages, and in-batch negatives.
## Evaluation
The following evaluation information is extracted from the [associated paper](https://arxiv.org/pdf/2004.04906.pdf).
#### Testing Data, Factors and Metrics
The model developers report the performance of the model on five QA datasets, using the top-k accuracy (k ∈ {20, 100}). The datasets were [NQ](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), [CuratedTREC (TREC)](https://huggingface.co/datasets/trec), and [SQuAD v1.1](https://huggingface.co/datasets/squad).
#### Results
| | Top 20 | | | | | Top 100| | | | |
|:----:|:------:|:---------:|:--:|:----:|:-----:|:------:|:---------:|:--:|:----:|:-----:|
| | NQ | TriviaQA | WQ | TREC | SQuAD | NQ | TriviaQA | WQ | TREC | SQuAD |
| | 78.4 | 79.4 |73.2| 79.8 | 63.2 | 85.4 | 85.0 |81.4| 89.1 | 77.2 |
## Environmental Impact
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). We present the hardware type and based on the [associated paper](https://arxiv.org/abs/2004.04906).
- **Hardware Type:** 8 32GB GPUs
- **Hours used:** Unknown
- **Cloud Provider:** Unknown
- **Compute Region:** Unknown
- **Carbon Emitted:** Unknown
## Technical Specifications
See the [associated paper](https://arxiv.org/abs/2004.04906) for details on the modeling architecture, objective, compute infrastructure, and training details.
## Citation Information
```bibtex
@inproceedings{karpukhin-etal-2020-dense,
title = "Dense Passage Retrieval for Open-Domain Question Answering",
author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.550",
doi = "10.18653/v1/2020.emnlp-main.550",
pages = "6769--6781",
}
```
## Model Card Authors
This model card was written by the team at Hugging Face. |
TheSkullery/BA-Zephyria-39b | TheSkullery | "2024-09-18T08:55:37Z" | 6 | 2 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:unsloth/Mistral-Small-Instruct-2409",
"base_model:finetune:unsloth/Mistral-Small-Instruct-2409",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-18T08:26:26Z" | ---
base_model:
- unsloth/Mistral-Small-Instruct-2409
library_name: transformers
tags:
- mergekit
- merge
---
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>BA-Zephyria-39b Data Card</title>
<link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet">
<style>
body, html {
height: 100%;
margin: 0;
padding: 0;
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #0a1128 0%, #1c2541 100%);
color: #e0e1dd;
font-size: 16px;
}
.container {
width: 100%;
height: 100%;
padding: 20px;
margin: 0;
background-color: rgba(255, 255, 255, 0.05);
border-radius: 12px;
box-shadow: 0 4px 10px rgba(0, 0, 0, 0.3);
backdrop-filter: blur(10px);
border: 1px solid rgba(255, 255, 255, 0.1);
}
.header h1 {
font-size: 28px;
color: #4cc9f0;
margin: 0 0 20px 0;
text-shadow: 2px 2px 4px rgba(0, 0, 0, 0.3);
}
.update-section h2 {
font-size: 24px;
color: #7209b7;
}
.update-section p {
font-size: 16px;
line-height: 1.6;
color: #e0e1dd;
}
.info img {
width: 100%;
border-radius: 10px;
margin-bottom: 15px;
}
a {
color: #4cc9f0;
text-decoration: none;
}
a:hover {
color: #f72585;
}
.button {
display: inline-block;
background-color: #3a0ca3;
color: #e0e1dd;
padding: 10px 20px;
border-radius: 5px;
cursor: pointer;
text-decoration: none;
}
.button:hover {
background-color: #7209b7;
}
pre {
background-color: #1c2541;
padding: 10px;
border-radius: 5px;
overflow-x: auto;
}
code {
font-family: 'Courier New', monospace;
color: #e0e1dd;
}
</style>
</head>
<body>
<div class="container">
<div class="header">
<h1>BA-Zephyria-39b [EXPERIMENTAL]</h1>
</div>
<div class="info">
<img src="https://cdn-uploads.huggingface.co/production/uploads/64545af5ec40bbbd01242ca6/6W3orrbf8A68l-3p_JxN1.png">
<h2>Model Information</h2>
<p><strong>Base Model:</strong> unsloth/Mistral-Small-Instruct-2409</p>
<p><strong>Strategy:</strong> Balanced Approach</p>
<p><strong>Total Layers:</strong> 55</p>
<p><strong>Duplication Start:</strong> Layer 19 (34.5% of model)</p>
<p><strong>Duplicated Layers:</strong> 23 (41.8% of model)</p>
<p><strong>Unique Final Layers:</strong> 14 (25.5% of model)</p>
<h2>Model Characteristics</h2>
<ul>
<li>Models down_proj and o_proj layers have been nulled and will require healing</li>
<li>Combines benefits of early and mid duplication strategies</li>
<li>Balanced between unique initial layers, duplicated middle layers, and unique final layers</li>
<li>Versatile approach suitable for a wide range of tasks</li>
<li>Provides substantial unique layers at the end for task-specific adaptations</li>
</ul>
<h2>Configuration Visualization</h2>
<pre><code>
[ Unique ][ Duplicated ][ Unique ]
0 ----------- 18 19 ------------ 41 42 ---------- 54
34.5% 41.8% 23.7%
</code></pre>
</div>
</div>
</body>
</html> |
ryan0712/llama-3-8b-slow-DUS-layer-SLERP | ryan0712 | "2024-05-23T11:06:36Z" | 145 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"NousResearch/Meta-Llama-3-8B",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:finetune:NousResearch/Meta-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-23T11:05:52Z" | ---
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Meta-Llama-3-8B
base_model:
- NousResearch/Meta-Llama-3-8B
- NousResearch/Meta-Llama-3-8B
---
# llama-3-8b-slow-DUS-layer-SLERP
llama-3-8b-slow-DUS-layer-SLERP is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B)
* [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: NousResearch/Meta-Llama-3-8B
layer_range: [5, 6]
- model: NousResearch/Meta-Llama-3-8B
layer_range: [20, 21]
merge_method: slerp
base_model: NousResearch/Meta-Llama-3-8B
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
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ryan0712/llama-3-8b-slow-DUS-layer-SLERP"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
mradermacher/hammerllm-1.4b-522k-GGUF | mradermacher | "2025-01-22T04:20:46Z" | 50 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:DataHammer/hammerllm-1.4b-522k",
"base_model:quantized:DataHammer/hammerllm-1.4b-522k",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | "2024-08-01T06:08:29Z" | ---
base_model: DataHammer/hammerllm-1.4b-522k
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/DataHammer/hammerllm-1.4b-522k
<!-- 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/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.IQ3_XS.gguf) | IQ3_XS | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q3_K_S.gguf) | Q3_K_S | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.IQ3_S.gguf) | IQ3_S | 0.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.IQ3_M.gguf) | IQ3_M | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q3_K_M.gguf) | Q3_K_M | 0.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q3_K_L.gguf) | Q3_K_L | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.IQ4_XS.gguf) | IQ4_XS | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q4_K_M.gguf) | Q4_K_M | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q5_K_S.gguf) | Q5_K_S | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q5_K_M.gguf) | Q5_K_M | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q6_K.gguf) | Q6_K | 1.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.Q8_0.gguf) | Q8_0 | 1.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/hammerllm-1.4b-522k-GGUF/resolve/main/hammerllm-1.4b-522k.f16.gguf) | f16 | 2.9 | 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 -->
|
WIS/dqn-SpaceInvadersNoFrameskip-v4 | WIS | "2023-10-04T06:52:31Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-10-04T06:51:51Z" | ---
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: 678.50 +/- 227.26
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 WIS -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 WIS -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 WIS
```
## 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'}
```
|
jim33282007/test3 | jim33282007 | "2024-03-19T09:59:37Z" | 104 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-03-19T09:32:44Z" | ---
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]
|
rach405/test_trainer3 | rach405 | "2022-11-23T12:34:47Z" | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-11-21T12:22:38Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: test_trainer3
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. -->
# test_trainer3
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 125 | 1.8785 | 0.396 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Tokenizers 0.11.6
|
whiteapple8222/02c3b443-64cf-429b-ac48-b958b88afbd7 | whiteapple8222 | "2025-02-08T02:10:44Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B",
"base_model:adapter:Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B",
"region:us"
] | null | "2025-02-07T23:32:02Z" | ---
library_name: peft
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 02c3b443-64cf-429b-ac48-b958b88afbd7
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3e78dfc9899e3252_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3e78dfc9899e3252_train_data.json
type:
field_input: hints_text
field_instruction: problem_statement
field_output: patch
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: whiteapple8222/02c3b443-64cf-429b-ac48-b958b88afbd7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1545
micro_batch_size: 4
mlflow_experiment_name: /tmp/3e78dfc9899e3252_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
special_tokens:
pad_token: <|eot_id|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c0662766-062f-4298-8647-89358ab88d97
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c0662766-062f-4298-8647-89358ab88d97
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 02c3b443-64cf-429b-ac48-b958b88afbd7
This model is a fine-tuned version of [Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B](https://huggingface.co/Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 1267
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9232 | 0.9998 | 1266 | 0.8855 |
| 1.7258 | 1.0006 | 1267 | 0.8819 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
jackhhhh/q-FrozenLake-v1-4x4-noSlippery | jackhhhh | "2023-03-18T09:09:04Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-03-18T09:09:02Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="jackhhhh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
proxectonos/Nos_D2T-gl | proxectonos | "2024-04-10T09:00:31Z" | 106 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"gl",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2023-02-23T09:13:42Z" | ---
license: mit
language:
- gl
---
<div align="justify">
**English text [here](https://huggingface.co/proxectonos/Nos_D2T-gl/edit/main/README_EN.md)**
**Descrición do Modelo**
Fine-tuning dun modelo text-to-text para realizar a tarefa de data-to-text en galego. Partindo do modelo [MT5-base](https://huggingface.co/google/mt5-base) multilingüe pre-adestrado para tarefas text-to-text, aplicase a técnica de fine-tuning para conseguir un modelo que realice a tarefa de xeración data-to-text en galego.
**Como xerar textos a partir de táboas de datos**
+ Abrir terminal bash
+ Instalar [Python 3.10](https://www.python.org/downloads/release/python-3104/)
+ Para xerar un texto a partir dalgúns dos 568 casos de test do noso dataset, empregarase o seguinte comando:
```bash
python generate_text.py -i <data_id> -o <output_path>
```
+ O argumento **-i** indica o ID da táboa de datos da cal se quere xerar un texto. Serán validos IDs de 0 ata 569, xa que estos son os casos de test dispoñibles cos que non se adestrou o modelo.
+ O argumento **-o** empregase para indicar o path no cal se creará un arquivo co texto xerado. En caso de non indicarse crearase o arquivo no directorio actual.
**Adestramento**
Para adestrar o modelo na tarefa data-to-text empregouse o primeiro dataset público para data-to-text en galego liberado polo Proxecto Nós e que se atopa dispoñíbel no seguinte repositorio: https://zenodo.org/record/7661650#.ZA8aZ3bMJro. O dataset componse de 3,302 rexistros de datos tabulares de prediccións meteorolóxicas reais xunto con descripcións textuais feitas por expertos en galego.
Debido a que o modelo base usa como entrada un formato textual e os nosos datos de entrada son en formato táboa, realizamos un proceso de "linearización" sobre os datos estruturados. Este proceso consiste en transformar as táboas de datos en datos textuais etiquetados da seguinte maneira:
<img src="https://huggingface.co/proxectonos/Nos_D2T-gl/resolve/main/exemplo-linearizacion.png" width="75%" style="margin:auto;">
Desta forma, o modelo text-to-text é quen de interpretar as táboas de datos "linearizadas" e os seus textos asociados, e así aprender a xerar textos que describan os datos estruturados que se lle proporcionen como input.
Para realizar o fine-tuning sobre o modelo empregaronse os seguintes hiperparámetros:
+ Batch size: 8
+ Optimizer: Adam
+ Learning rate: 1e-5
+ Training epochs: 1000
**Licenzas do Modelo**
MIT License
Copyright (c) 2023 Proxecto Nós
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
**Financiamento**
Esta investigación foi financiada polo proxecto "Nós: o galego na sociedade e economía da intelixencia artificial", resultado dun convenio entre a Xunta de Galicia e a Universidade de Santiago de Compostela, e polas axudas ED431G2019/04 e ED431C2022/19 da Consellaría de Educación, Universidade e Formación Profesional e polo Fondo Europeo de Desenvolvemento Rexional (programa ERDF/FEDER).
</div>
**Citar este traballo**
Se utiliza este modelo no seu traballo, cite por favor así:
González Corbelle, Javier; Bugarín Diz, Alberto. 2023 Nos_D2T-gl. URL: https://huggingface.co/proxectonos/Nos_D2T-gl/ |
ariakhosh/adapter4 | ariakhosh | "2024-05-13T16:12:57Z" | 0 | 0 | null | [
"safetensors",
"arxiv:2305.14314",
"arxiv:2302.13971",
"region:us"
] | null | "2024-05-13T16:11:47Z" | # QLoRA Instruction Tuned Models
| [Paper](https://arxiv.org/abs/2305.14314) | [Code](https://github.com/artidoro/qlora) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) |
**The `QLoRA Instruction Tuned Models` are open-source models obtained through 4-bit QLoRA tuning of LLaMA base models on various instruction tuning datasets. They are available in 7B, 13B, 33B, and 65B parameter sizes.**
**Note: The best performing chatbot models are named [Guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) and finetuned on OASST1. This model card is for the other models finetuned on other instruction tuning datasets.**
⚠️ These models are purely intended for research purposes and could produce problematic outputs.
## What are QLoRA Instruction Tuned Models and why use them?
- **Strong performance on MMLU** following the QLoRA instruction tuning.
- **Replicable and efficient instruction tuning procedure** that can be extended to new use cases. QLoRA training scripts are available in the [QLoRA repo](https://github.com/artidoro/qlora).
- **Rigorous comparison to 16-bit methods** (both 16-bit full-finetuning and LoRA) in [our paper](https://arxiv.org/abs/2305.14314) demonstrates the effectiveness of 4-bit QLoRA finetuning.
- **Lightweight** checkpoints which only contain adapter weights.
## License and Intended Use
QLoRA Instruction Tuned adapter weights are available under Apache 2 license. Note the use of these adapter weights, requires access to the LLaMA model weighs and therefore should be used according to the LLaMA license.
## Usage
Here is an example of how you would load Flan v2 7B in 4-bits:
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/qlora-flan-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
quantization_config=BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
),
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
Inference can then be performed as usual with HF models as follows:
```python
prompt = "Introduce yourself"
formatted_prompt = (
f"A chat between a curious human and an artificial intelligence assistant."
f"The assistant gives helpful, detailed, and polite answers to the user's questions.\n"
f"### Human: {prompt} ### Assistant:"
)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda:0")
outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Expected output similar to the following:
```
A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.
### Human: Introduce yourself ### Assistant: I am an artificial intelligence assistant. I am here to help you with any questions you may have.
```
## Current Inference Limitations
Currently, 4-bit inference is slow. We recommend loading in 16 bits if inference speed is a concern. We are actively working on releasing efficient 4-bit inference kernels.
Below is how you would load the model in 16 bits:
```python
model_name = "huggyllama/llama-7b"
adapters_name = 'timdettmers/qlora-flan-7b'
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
max_memory= {i: '24000MB' for i in range(torch.cuda.device_count())},
)
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Model Card
**Architecture**: The models released here are LoRA adapters to be used on top of LLaMA models. They are added to all layers. For all model sizes, we use $r=64$.
**Base Model**: These models use LLaMA as base model with sizes 7B, 13B, 33B, 65B. LLaMA is a causal language model pretrained on a large corpus of text. See [LLaMA paper](https://arxiv.org/abs/2302.13971) for more details. Note that these models can inherit biases and limitations of the base model.
**Finetuning Data**: These models are finetuned on various instruction tuning datasets. The datasets used are: Alpaca, HH-RLHF, Unnatural Instr., Chip2, Longform, Self-Instruct, FLAN v2.
**Languages**: The different datasets cover different languages. We direct to the various papers and resources describing the datasets for more details.
Next, we describe Training and Evaluation details.
### Training
QLoRA Instruction Tuned Models are the result of 4-bit QLoRA supervised finetuning on different instruction tuning datasets.
All models use NormalFloat4 datatype for the base model and LoRA adapters on all linear layers with BFloat16 as computation datatype. We set LoRA $r=64$, $\alpha=16$. We also use Adam beta2 of 0.999, max grad norm of 0.3 and LoRA dropout of 0.1 for models up to 13B and 0.05 for 33B and 65B models.
For the finetuning process, we use constant learning rate schedule and paged AdamW optimizer.
### Training hyperparameters
| Parameters | Dataset | Batch size | LR | Steps | Source Length | Target Length |
|------------|----------|------------|------|-------|---------------|---------------|
| 7B | All | 16 | 2e-4 | 10000 | 384 | 128 |
| 7B | OASST1 | 16 | 2e-4 | 1875 | - | 512 |
| 7B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 |
| 7B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 |
| 13B | All | 16 | 2e-4 | 10000 | 384 | 128 |
| 13B | OASST1 | 16 | 2e-4 | 1875 | - | 512 |
| 13B | HH-RLHF | 16 | 2e-4 | 10000 | - | 768 |
| 13B | Longform | 16 | 2e-4 | 4000 | 512 | 1024 |
| 33B | All | 32 | 1e-4 | 5000 | 384 | 128 |
| 33B | OASST1 | 16 | 1e-4 | 1875 | - | 512 |
| 33B | HH-RLHF | 32 | 1e-4 | 5000 | - | 768 |
| 33B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 |
| 65B | All | 64 | 1e-4 | 2500 | 384 | 128 |
| 65B | OASST1 | 16 | 1e-4 | 1875 | - | 512 |
| 65B | HH-RLHF | 64 | 1e-4 | 2500 | - | 768 |
| 65B | Longform | 32 | 1e-4 | 2343 | 512 | 1024 |
### Evaluation
We use the MMLU benchmark to measure performance on a range of language understanding tasks. This is a multiple-choice benchmark covering 57 tasks including elementary mathematics, US history, computer science, law, and more. We report 5-shot test accuracy.
Dataset | 7B | 13B | 33B | 65B
---|---|---|---|---
LLaMA no tuning | 35.1 | 46.9 | 57.8 | 63.4
Self-Instruct | 36.4 | 33.3 | 53.0 | 56.7
Longform | 32.1 | 43.2 | 56.6 | 59.7
Chip2 | 34.5 | 41.6 | 53.6 | 59.8
HH-RLHF | 34.9 | 44.6 | 55.8 | 60.1
Unnatural Instruct | 41.9 | 48.1 | 57.3 | 61.3
OASST1 (Guanaco) | 36.6 | 46.4 | 57.0 | 62.2
Alpaca | 38.8 | 47.8 | 57.3 | 62.5
FLAN v2 | 44.5 | 51.4 | 59.2 | 63.9
We evaluate the generative language capabilities through automated evaluations on the Vicuna benchmark. We report the score of the QLoRA Instruction Finetuned Models relative to the score obtained by ChatGPT. The rater in this case is GPT-4 which is tasked to assign a score out of 10 to both ChatGPT and the model outputs for each prompt. We report scores for models ranging 7B to 65B and compare them to both academic and commercial baselilnes.
| Model / Dataset | Params | Model bits | Memory | ChatGPT vs Sys | Sys vs ChatGPT | Mean | 95\% CI |
|------------------|--------|------------|--------|----------------|----------------|------------------|---------|
| GPT-4 | - | - | - | 119.4\% | 110.1\% | **114.5**\% | 2.6\% |
| Bard | - | - | - | 93.2\% | 96.4\% | 94.8\% | 4.1\% |
| Guanaco | 65B | 4-bit | 41 GB | 96.7\% | 101.9\% | **99.3**\% | 4.4\% |
| Alpaca | 65B | 4-bit | 41 GB | 63.0\% | 77.9\% | 70.7\% | 4.3\% |
| FLAN v2 | 65B | 4-bit | 41 GB | 37.0\% | 59.6\% | 48.4\% | 4.6\% |
| Guanaco | 33B | 4-bit | 21 GB | 96.5\% | 99.2\% | **97.8**\% | 4.4\% |
| Open Assistant | 33B | 16-bit | 66 GB | 73.4\% | 85.7\% | 78.1\% | 5.3\% |
| Alpaca | 33B | 4-bit | 21 GB | 67.2\% | 79.7\% | 73.6\% | 4.2\% |
| FLAN v2 | 33B | 4-bit | 21 GB | 26.3\% | 49.7\% | 38.0\% | 3.9\% |
| Vicuna | 13B | 16-bit | 26 GB | 91.2\% | 98.7\% | **94.9**\% | 4.5\% |
| Guanaco | 13B | 4-bit | 10 GB | 87.3\% | 93.4\% | 90.4\% | 5.2\% |
| Alpaca | 13B | 4-bit | 10 GB | 63.8\% | 76.7\% | 69.4\% | 4.2\% |
| HH-RLHF | 13B | 4-bit | 10 GB | 55.5\% | 69.1\% | 62.5\% | 4.7\% |
| Unnatural Instr. | 13B | 4-bit | 10 GB | 50.6\% | 69.8\% | 60.5\% | 4.2\% |
| Chip2 | 13B | 4-bit | 10 GB | 49.2\% | 69.3\% | 59.5\% | 4.7\% |
| Longform | 13B | 4-bit | 10 GB | 44.9\% | 62.0\% | 53.6\% | 5.2\% |
| Self-Instruct | 13B | 4-bit | 10 GB | 38.0\% | 60.5\% | 49.1\% | 4.6\% |
| FLAN v2 | 13B | 4-bit | 10 GB | 32.4\% | 61.2\% | 47.0\% | 3.6\% |
| Guanaco | 7B | 4-bit | 5 GB | 84.1\% | 89.8\% | **87.0**\% | 5.4\% |
| Alpaca | 7B | 4-bit | 5 GB | 57.3\% | 71.2\% | 64.4\% | 5.0\% |
| FLAN v2 | 7B | 4-bit | 5 GB | 33.3\% | 56.1\% | 44.8\% | 4.0\% |
## Citation
```bibtex
@article{dettmers2023qlora,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Dettmers, Tim and Pagnoni, Artidoro and Holtzman, Ari and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:2305.14314},
year={2023}
}
``` |
Jovie/GothicNiji | Jovie | "2025-03-16T09:00:56Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | "2025-03-16T09:00:12Z" | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: anime
widget:
- text: >-
"symmetry!! portrait of curvaceous alien in the style of horizon zero dawn,
machine face, intricate, elegant, highly detailed, digital painting,
artstation, concept art, smooth, sharp focus, illustration, art by artgerm
and greg rutkowski and alphonse mucha
output:
url: images/example_rndh30ve1.png
---
# GothicNiji model style
<Gallery />
## Model description
## Trigger words
You should use `` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jovie/GothicNiji/tree/main) them in the Files & versions tab. |
michaelw37/sc34 | michaelw37 | "2024-04-18T19:06:41Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-18T19:05:07Z" | ---
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] |
kardosdrur/dfm-sentence-encoder-small-distil_zephyr | kardosdrur | "2023-12-13T13:49:39Z" | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"electra",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2023-12-12T10:50:59Z" | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# kardosdrur/dfm-sentence-encoder-small-distil_zephyr
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('kardosdrur/dfm-sentence-encoder-small-distil_zephyr')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('kardosdrur/dfm-sentence-encoder-small-distil_zephyr')
model = AutoModel.from_pretrained('kardosdrur/dfm-sentence-encoder-small-distil_zephyr')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=kardosdrur/dfm-sentence-encoder-small-distil_zephyr)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 213 with parameters:
```
{'batch_size': 42, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 30,
"evaluation_steps": 0,
"evaluator": "dfm_sentence_trf.evaluation.task_evaluator.TaskListEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 600,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: ElectraModel
(1): Pooling({'word_embedding_dimension': 256, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
paarth-sachan/ppo-LunarLander-v2 | paarth-sachan | "2023-08-15T12:55:45Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-08-15T12:55:23Z" | ---
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: 254.22 +/- 26.38
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
...
```
|
mradermacher/Gemma-2b-it-FT-Code-v2-GGUF | mradermacher | "2024-12-19T20:19:01Z" | 25 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Kukedlc/Gemma-2b-it-FT-Code-v2",
"base_model:quantized:Kukedlc/Gemma-2b-it-FT-Code-v2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-12-19T20:06:02Z" | ---
base_model: Kukedlc/Gemma-2b-it-FT-Code-v2
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Kukedlc/Gemma-2b-it-FT-Code-v2
<!-- 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/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q2_K.gguf) | Q2_K | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q3_K_S.gguf) | Q3_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q3_K_L.gguf) | Q3_K_L | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.IQ4_XS.gguf) | IQ4_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q4_K_M.gguf) | Q4_K_M | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q5_K_S.gguf) | Q5_K_S | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q5_K_M.gguf) | Q5_K_M | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q6_K.gguf) | Q6_K | 2.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.Q8_0.gguf) | Q8_0 | 2.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Gemma-2b-it-FT-Code-v2-GGUF/resolve/main/Gemma-2b-it-FT-Code-v2.f16.gguf) | f16 | 5.1 | 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 -->
|
danielhanchen/lora_model4_21022024 | danielhanchen | "2024-02-20T13:59:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-02-20T13:59:33Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **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]
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## 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
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[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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Glossary [optional]
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|
boosiebadass43/Titus-shorthair-lora | boosiebadass43 | "2025-01-19T23:04:43Z" | 37 | 1 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-01-19T22:43:10Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: titus
---
# Titus Shorthair Lora
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `titus` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('boosiebadass43/Titus-shorthair-lora', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
amyy78/unit4 | amyy78 | "2023-11-03T17:14:50Z" | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-11-03T01:44:13Z" | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: unit4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 19.10 +/- 14.41
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
joaomarcelom12/ino | joaomarcelom12 | "2025-02-26T14:32:14Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2025-02-26T13:48:28Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
ZeroXClem/L3.1-Pneuma-Allades-8B-Q4_K_M-GGUF | ZeroXClem | "2024-10-23T13:54:23Z" | 6 | 2 | null | [
"gguf",
"merge",
"model_stock",
"Llama3",
"Pneuma",
"Allades",
"storytelling",
"roleplaying",
"instruction-following",
"creative-writing",
"reasoning",
"long-form-generation",
"text-generation",
"lazymergekit",
"nbeerbower/Llama3.1-Allades-8B",
"mergekit-community/L3.1-Pneuma-8B-v1",
"llama-cpp",
"gguf-my-repo",
"base_model:ZeroXClem/L3.1-Pneuma-Allades-8B",
"base_model:quantized:ZeroXClem/L3.1-Pneuma-Allades-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2024-10-23T13:53:59Z" | ---
license: apache-2.0
tags:
- merge
- model_stock
- Llama3
- Pneuma
- Allades
- storytelling
- roleplaying
- instruction-following
- creative-writing
- reasoning
- long-form-generation
- text-generation
- lazymergekit
- nbeerbower/Llama3.1-Allades-8B
- mergekit-community/L3.1-Pneuma-8B-v1
- llama-cpp
- gguf-my-repo
base_model: ZeroXClem/L3.1-Pneuma-Allades-8B
pipeline_tag: text-generation
---
# ZeroXClem/L3.1-Pneuma-Allades-8B-Q4_K_M-GGUF
This model was converted to GGUF format from [`ZeroXClem/L3.1-Pneuma-Allades-8B`](https://huggingface.co/ZeroXClem/L3.1-Pneuma-Allades-8B) 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/ZeroXClem/L3.1-Pneuma-Allades-8B) 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-cli --hf-repo ZeroXClem/L3.1-Pneuma-Allades-8B-Q4_K_M-GGUF --hf-file l3.1-pneuma-allades-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo ZeroXClem/L3.1-Pneuma-Allades-8B-Q4_K_M-GGUF --hf-file l3.1-pneuma-allades-8b-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.
```
./llama-cli --hf-repo ZeroXClem/L3.1-Pneuma-Allades-8B-Q4_K_M-GGUF --hf-file l3.1-pneuma-allades-8b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo ZeroXClem/L3.1-Pneuma-Allades-8B-Q4_K_M-GGUF --hf-file l3.1-pneuma-allades-8b-q4_k_m.gguf -c 2048
```
|
nlparabic/test-egy_aragpt | nlparabic | "2024-08-25T11:46:25Z" | 18 | 0 | null | [
"safetensors",
"gpt2",
"generated_from_trainer",
"base_model:riotu-lab/ArabianGPT-01B",
"base_model:finetune:riotu-lab/ArabianGPT-01B",
"license:apache-2.0",
"region:us"
] | null | "2024-08-24T12:57:25Z" | ---
license: apache-2.0
base_model: riotu-lab/ArabianGPT-01B
tags:
- generated_from_trainer
metrics:
- bleu
- rouge
model-index:
- name: test-egy_aragpt
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. -->
# test-egy_aragpt
This model is a fine-tuned version of [riotu-lab/ArabianGPT-01B](https://huggingface.co/riotu-lab/ArabianGPT-01B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4630
- Bleu: 0.0984
- Rouge1: 0.3093
- Rouge2: 0.0718
- Rougel: 0.2296
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
JiaxiJiang/textual_inversion_ring | JiaxiJiang | "2024-03-21T21:27:38Z" | 34 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-03-21T20:44:45Z" | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
- diffusers-training
base_model: runwayml/stable-diffusion-v1-5
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual inversion text2image fine-tuning - JiaxiJiang/textual_inversion_ring
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
multidefmod/dore-mbart | multidefmod | "2024-03-12T15:47:57Z" | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"dataset:multidefmod/dore",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-03-12T14:23:29Z" | ---
datasets:
- multidefmod/dore
--- |
Ahmedkkh/homg | Ahmedkkh | "2024-04-22T10:11:29Z" | 0 | 0 | null | [
"doi:10.57967/hf/2103",
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-04-22T10:06:12Z" | ---
license: creativeml-openrail-m
---
|
deepnet111/sn9-14b-star-004 | deepnet111 | "2025-01-19T08:41:05Z" | 71 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-19T08:37:27Z" | ---
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] |
sail-rvc/wanderer-jp | sail-rvc | "2023-07-14T07:45:04Z" | 3 | 1 | transformers | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | "2023-07-14T07:44:45Z" |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# wanderer-jp
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:45:04
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
|
riccardoebene/t5-small_level_1 | riccardoebene | "2024-11-27T12:35:34Z" | 115 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-11-27T10:35:57Z" | ---
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] |
wAT-CH-Sophie-Rain-Spiderman-Updatesstt/new.Sophie.Rain.Spider-Man.Video.Tutorial.Official.clips | wAT-CH-Sophie-Rain-Spiderman-Updatesstt | "2025-03-24T04:14:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-24T04:14:06Z" | <animated-image data-catalyst=""><a href="https://alltvsteam.com/viral-video/?v=news-es-tvdf" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
MayBashendy/ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k4_task1_organization | MayBashendy | "2024-12-10T15:31:26Z" | 162 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-10T15:26:02Z" | ---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k4_task1_organization
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. -->
# ArabicNewSplits4_WithDuplicationsForScore5_FineTuningAraBERT_run2_AugV5_k4_task1_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6090
- Qwk: 0.7563
- Mse: 0.6090
- Rmse: 0.7804
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0833 | 2 | 5.3762 | -0.0220 | 5.3762 | 2.3187 |
| No log | 0.1667 | 4 | 3.8802 | 0.0563 | 3.8802 | 1.9698 |
| No log | 0.25 | 6 | 2.3347 | 0.1829 | 2.3347 | 1.5280 |
| No log | 0.3333 | 8 | 1.3715 | 0.1774 | 1.3715 | 1.1711 |
| No log | 0.4167 | 10 | 1.3335 | 0.1249 | 1.3335 | 1.1548 |
| No log | 0.5 | 12 | 1.1603 | 0.2242 | 1.1603 | 1.0772 |
| No log | 0.5833 | 14 | 1.1841 | 0.2113 | 1.1841 | 1.0882 |
| No log | 0.6667 | 16 | 1.2055 | 0.2035 | 1.2055 | 1.0980 |
| No log | 0.75 | 18 | 1.2137 | 0.2001 | 1.2137 | 1.1017 |
| No log | 0.8333 | 20 | 1.3045 | 0.1051 | 1.3045 | 1.1421 |
| No log | 0.9167 | 22 | 1.2046 | 0.1568 | 1.2046 | 1.0975 |
| No log | 1.0 | 24 | 1.0344 | 0.3039 | 1.0344 | 1.0170 |
| No log | 1.0833 | 26 | 1.1691 | 0.3276 | 1.1691 | 1.0813 |
| No log | 1.1667 | 28 | 1.1240 | 0.3856 | 1.1240 | 1.0602 |
| No log | 1.25 | 30 | 0.8334 | 0.4855 | 0.8334 | 0.9129 |
| No log | 1.3333 | 32 | 0.7171 | 0.6153 | 0.7171 | 0.8468 |
| No log | 1.4167 | 34 | 0.6706 | 0.6554 | 0.6706 | 0.8189 |
| No log | 1.5 | 36 | 0.7853 | 0.5976 | 0.7853 | 0.8861 |
| No log | 1.5833 | 38 | 1.6236 | 0.4161 | 1.6236 | 1.2742 |
| No log | 1.6667 | 40 | 2.4552 | 0.2439 | 2.4552 | 1.5669 |
| No log | 1.75 | 42 | 2.5266 | 0.2222 | 2.5266 | 1.5895 |
| No log | 1.8333 | 44 | 1.7137 | 0.4064 | 1.7137 | 1.3091 |
| No log | 1.9167 | 46 | 0.7476 | 0.6902 | 0.7476 | 0.8646 |
| No log | 2.0 | 48 | 0.6504 | 0.7029 | 0.6504 | 0.8065 |
| No log | 2.0833 | 50 | 1.0265 | 0.5195 | 1.0265 | 1.0132 |
| No log | 2.1667 | 52 | 1.0506 | 0.5218 | 1.0506 | 1.0250 |
| No log | 2.25 | 54 | 0.7072 | 0.6467 | 0.7072 | 0.8410 |
| No log | 2.3333 | 56 | 0.6052 | 0.6370 | 0.6052 | 0.7779 |
| No log | 2.4167 | 58 | 0.7005 | 0.6208 | 0.7005 | 0.8370 |
| No log | 2.5 | 60 | 0.6646 | 0.6306 | 0.6646 | 0.8152 |
| No log | 2.5833 | 62 | 0.5764 | 0.7360 | 0.5764 | 0.7592 |
| No log | 2.6667 | 64 | 0.5329 | 0.7364 | 0.5329 | 0.7300 |
| No log | 2.75 | 66 | 0.5537 | 0.7544 | 0.5537 | 0.7441 |
| No log | 2.8333 | 68 | 0.5508 | 0.7850 | 0.5508 | 0.7422 |
| No log | 2.9167 | 70 | 0.5815 | 0.7429 | 0.5815 | 0.7625 |
| No log | 3.0 | 72 | 0.6529 | 0.7482 | 0.6529 | 0.8080 |
| No log | 3.0833 | 74 | 0.6394 | 0.7617 | 0.6394 | 0.7997 |
| No log | 3.1667 | 76 | 0.7211 | 0.7331 | 0.7211 | 0.8492 |
| No log | 3.25 | 78 | 1.0962 | 0.6270 | 1.0962 | 1.0470 |
| No log | 3.3333 | 80 | 1.2740 | 0.5797 | 1.2740 | 1.1287 |
| No log | 3.4167 | 82 | 1.0804 | 0.6244 | 1.0804 | 1.0394 |
| No log | 3.5 | 84 | 0.6766 | 0.7281 | 0.6766 | 0.8225 |
| No log | 3.5833 | 86 | 0.6030 | 0.7181 | 0.6030 | 0.7765 |
| No log | 3.6667 | 88 | 0.6094 | 0.7150 | 0.6094 | 0.7806 |
| No log | 3.75 | 90 | 0.7383 | 0.7020 | 0.7383 | 0.8592 |
| No log | 3.8333 | 92 | 0.7821 | 0.6969 | 0.7821 | 0.8843 |
| No log | 3.9167 | 94 | 0.6858 | 0.7090 | 0.6858 | 0.8282 |
| No log | 4.0 | 96 | 0.5933 | 0.7262 | 0.5933 | 0.7703 |
| No log | 4.0833 | 98 | 0.5960 | 0.7130 | 0.5960 | 0.7720 |
| No log | 4.1667 | 100 | 0.6685 | 0.7349 | 0.6685 | 0.8176 |
| No log | 4.25 | 102 | 0.7797 | 0.7496 | 0.7797 | 0.8830 |
| No log | 4.3333 | 104 | 0.8027 | 0.7306 | 0.8027 | 0.8959 |
| No log | 4.4167 | 106 | 0.6749 | 0.7445 | 0.6749 | 0.8215 |
| No log | 4.5 | 108 | 0.5843 | 0.7200 | 0.5843 | 0.7644 |
| No log | 4.5833 | 110 | 0.5690 | 0.7224 | 0.5690 | 0.7543 |
| No log | 4.6667 | 112 | 0.6232 | 0.7392 | 0.6232 | 0.7895 |
| No log | 4.75 | 114 | 0.6740 | 0.7469 | 0.6740 | 0.8210 |
| No log | 4.8333 | 116 | 0.7154 | 0.7426 | 0.7154 | 0.8458 |
| No log | 4.9167 | 118 | 0.6706 | 0.7427 | 0.6706 | 0.8189 |
| No log | 5.0 | 120 | 0.5964 | 0.7463 | 0.5964 | 0.7723 |
| No log | 5.0833 | 122 | 0.5896 | 0.7557 | 0.5896 | 0.7679 |
| No log | 5.1667 | 124 | 0.5932 | 0.7355 | 0.5932 | 0.7702 |
| No log | 5.25 | 126 | 0.5830 | 0.7607 | 0.5830 | 0.7635 |
| No log | 5.3333 | 128 | 0.5772 | 0.7558 | 0.5772 | 0.7597 |
| No log | 5.4167 | 130 | 0.5919 | 0.7557 | 0.5919 | 0.7693 |
| No log | 5.5 | 132 | 0.6590 | 0.7170 | 0.6590 | 0.8118 |
| No log | 5.5833 | 134 | 0.6510 | 0.7168 | 0.6510 | 0.8069 |
| No log | 5.6667 | 136 | 0.5856 | 0.7475 | 0.5856 | 0.7653 |
| No log | 5.75 | 138 | 0.5805 | 0.7471 | 0.5805 | 0.7619 |
| No log | 5.8333 | 140 | 0.5962 | 0.7371 | 0.5962 | 0.7721 |
| No log | 5.9167 | 142 | 0.6857 | 0.7315 | 0.6857 | 0.8280 |
| No log | 6.0 | 144 | 0.7613 | 0.7314 | 0.7613 | 0.8725 |
| No log | 6.0833 | 146 | 0.7675 | 0.7314 | 0.7675 | 0.8760 |
| No log | 6.1667 | 148 | 0.7081 | 0.7397 | 0.7081 | 0.8415 |
| No log | 6.25 | 150 | 0.6247 | 0.7509 | 0.6247 | 0.7904 |
| No log | 6.3333 | 152 | 0.5988 | 0.7480 | 0.5988 | 0.7738 |
| No log | 6.4167 | 154 | 0.5966 | 0.7480 | 0.5966 | 0.7724 |
| No log | 6.5 | 156 | 0.6412 | 0.7510 | 0.6412 | 0.8007 |
| No log | 6.5833 | 158 | 0.7069 | 0.7254 | 0.7069 | 0.8408 |
| No log | 6.6667 | 160 | 0.7229 | 0.7037 | 0.7229 | 0.8502 |
| No log | 6.75 | 162 | 0.6576 | 0.7505 | 0.6576 | 0.8109 |
| No log | 6.8333 | 164 | 0.5886 | 0.7434 | 0.5886 | 0.7672 |
| No log | 6.9167 | 166 | 0.5663 | 0.7328 | 0.5663 | 0.7526 |
| No log | 7.0 | 168 | 0.5638 | 0.7445 | 0.5638 | 0.7509 |
| No log | 7.0833 | 170 | 0.5742 | 0.7560 | 0.5742 | 0.7577 |
| No log | 7.1667 | 172 | 0.5728 | 0.7632 | 0.5728 | 0.7569 |
| No log | 7.25 | 174 | 0.5577 | 0.7638 | 0.5577 | 0.7468 |
| No log | 7.3333 | 176 | 0.5582 | 0.7638 | 0.5582 | 0.7471 |
| No log | 7.4167 | 178 | 0.5611 | 0.7692 | 0.5611 | 0.7490 |
| No log | 7.5 | 180 | 0.5697 | 0.7677 | 0.5697 | 0.7548 |
| No log | 7.5833 | 182 | 0.5736 | 0.7650 | 0.5736 | 0.7574 |
| No log | 7.6667 | 184 | 0.5755 | 0.7650 | 0.5755 | 0.7586 |
| No log | 7.75 | 186 | 0.5930 | 0.7586 | 0.5930 | 0.7700 |
| No log | 7.8333 | 188 | 0.6130 | 0.7486 | 0.6130 | 0.7829 |
| No log | 7.9167 | 190 | 0.6274 | 0.7486 | 0.6274 | 0.7921 |
| No log | 8.0 | 192 | 0.6189 | 0.7486 | 0.6189 | 0.7867 |
| No log | 8.0833 | 194 | 0.6072 | 0.7486 | 0.6072 | 0.7792 |
| No log | 8.1667 | 196 | 0.5949 | 0.7640 | 0.5949 | 0.7713 |
| No log | 8.25 | 198 | 0.5917 | 0.7588 | 0.5917 | 0.7692 |
| No log | 8.3333 | 200 | 0.5976 | 0.7488 | 0.5976 | 0.7730 |
| No log | 8.4167 | 202 | 0.6069 | 0.7488 | 0.6069 | 0.7790 |
| No log | 8.5 | 204 | 0.5995 | 0.7488 | 0.5995 | 0.7743 |
| No log | 8.5833 | 206 | 0.5805 | 0.7498 | 0.5805 | 0.7619 |
| No log | 8.6667 | 208 | 0.5757 | 0.7580 | 0.5757 | 0.7587 |
| No log | 8.75 | 210 | 0.5869 | 0.7552 | 0.5869 | 0.7661 |
| No log | 8.8333 | 212 | 0.6057 | 0.7522 | 0.6057 | 0.7783 |
| No log | 8.9167 | 214 | 0.6059 | 0.7522 | 0.6059 | 0.7784 |
| No log | 9.0 | 216 | 0.5977 | 0.7490 | 0.5977 | 0.7731 |
| No log | 9.0833 | 218 | 0.5933 | 0.7490 | 0.5933 | 0.7703 |
| No log | 9.1667 | 220 | 0.5929 | 0.7479 | 0.5929 | 0.7700 |
| No log | 9.25 | 222 | 0.5962 | 0.7472 | 0.5962 | 0.7721 |
| No log | 9.3333 | 224 | 0.5961 | 0.7568 | 0.5961 | 0.7721 |
| No log | 9.4167 | 226 | 0.5973 | 0.7568 | 0.5973 | 0.7729 |
| No log | 9.5 | 228 | 0.6012 | 0.7568 | 0.6012 | 0.7754 |
| No log | 9.5833 | 230 | 0.6069 | 0.7563 | 0.6069 | 0.7791 |
| No log | 9.6667 | 232 | 0.6093 | 0.7563 | 0.6093 | 0.7805 |
| No log | 9.75 | 234 | 0.6077 | 0.7602 | 0.6077 | 0.7796 |
| No log | 9.8333 | 236 | 0.6082 | 0.7563 | 0.6082 | 0.7799 |
| No log | 9.9167 | 238 | 0.6084 | 0.7563 | 0.6084 | 0.7800 |
| No log | 10.0 | 240 | 0.6090 | 0.7563 | 0.6090 | 0.7804 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
ResonantTri/lora-r3-F32-GGUF | ResonantTri | "2025-03-27T03:09:15Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"llama-cpp",
"gguf-my-lora",
"en",
"base_model:ResonantTri/lora-r3",
"base_model:quantized:ResonantTri/lora-r3",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-03-27T03:09:13Z" | ---
base_model: ResonantTri/lora-r3
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-lora
license: apache-2.0
language:
- en
---
# ResonantTri/lora-r3-F32-GGUF
This LoRA adapter was converted to GGUF format from [`ResonantTri/lora-r3`](https://huggingface.co/ResonantTri/lora-r3) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/ResonantTri/lora-r3) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora lora-r3-f32.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora lora-r3-f32.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
Snim/Reinforce-copter2 | Snim | "2023-03-01T00:34:13Z" | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-03-01T00:34:08Z" | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-copter2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 36.40 +/- 39.02
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
isspek/roberta-base_zika_ebola_1_2e-5_16_weight | isspek | "2025-02-12T10:26:34Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-02-12T10:26:10Z" | ---
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] |
MaziyarPanahi/Llama-3-11B-Instruct-v0.1-GGUF | MaziyarPanahi | "2024-04-20T07:41:45Z" | 596 | 7 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"text-generation",
"mixtral",
"base_model:MaziyarPanahi/Llama-3-11B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/Llama-3-11B-Instruct-v0.1",
"region:us",
"conversational"
] | text-generation | "2024-04-19T10:37:09Z" | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- text-generation
- mixtral
- text-generation
model_name: Llama-3-11B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/Llama-3-11B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Llama-3-11B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-11B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Llama-3-11B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Llama-3-11B-Instruct-v0.1)
## Description
[MaziyarPanahi/Llama-3-11B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-11B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Llama-3-11B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Llama-3-11B-Instruct-v0.1).
## Load GGUF models
You `MUST` follow the prompt template provided by Llama-3:
```sh
./llama.cpp/main -m Llama-3-11B-Instruct.Q2_K.gguf -r '<|eot_id|>' --in-prefix "\n<|start_header_id|>user<|end_header_id|>\n\n" --in-suffix "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" -p "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nHi! How are you?<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n" -n 1024
```
|
tingting/orpheus_3b_full_e4_16bit | tingting | "2025-03-28T05:31:00Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-28T05:20:12Z" | ---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** tingting
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
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)
|
lesso08/cc5a52fd-d351-4f4e-b893-1c569637461b | lesso08 | "2025-01-27T16:54:28Z" | 9 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Phi-3-medium-4k-instruct",
"base_model:adapter:unsloth/Phi-3-medium-4k-instruct",
"license:mit",
"region:us"
] | null | "2025-01-27T16:31:54Z" | ---
library_name: peft
license: mit
base_model: unsloth/Phi-3-medium-4k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cc5a52fd-d351-4f4e-b893-1c569637461b
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Phi-3-medium-4k-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 71d683f6bce439e0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/71d683f6bce439e0_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso08/cc5a52fd-d351-4f4e-b893-1c569637461b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/71d683f6bce439e0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9e7cfa84-39c6-4691-a241-765c08f1032c
wandb_project: multi
wandb_run: your_name
wandb_runid: 9e7cfa84-39c6-4691-a241-765c08f1032c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# cc5a52fd-d351-4f4e-b893-1c569637461b
This model is a fine-tuned version of [unsloth/Phi-3-medium-4k-instruct](https://huggingface.co/unsloth/Phi-3-medium-4k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0260 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
vocabtrimmer/xlm-v-base-trimmed-en-10000-tweet-sentiment-en | vocabtrimmer | "2023-03-30T23:19:41Z" | 114 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-03-30T23:17:43Z" | # `vocabtrimmer/xlm-v-base-trimmed-en-10000-tweet-sentiment-en`
This model is a fine-tuned version of [/home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-en-10000](https://huggingface.co//home/asahiushio/Projects/lm-vocab-trimmer/ckpts/xlm-v-base-trimmed-en-10000) on the
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual) (english).
Following metrics are computed on the `test` split of
[cardiffnlp/tweet_sentiment_multilingual](https://huggingface.co/datasets/cardiffnlp/tweet_sentiment_multilingual)(english).
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 60.57 | 60.57 | 60.57 | 57.82 | 60.57 | 59.15 | 60.57 |
Check the result file [here](https://huggingface.co/vocabtrimmer/xlm-v-base-trimmed-en-10000-tweet-sentiment-en/raw/main/eval.json). |
RichardErkhov/ewof_-_koishi-instruct-3b-gguf | RichardErkhov | "2024-06-06T09:13:30Z" | 5 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | "2024-06-06T05:10:35Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
koishi-instruct-3b - GGUF
- Model creator: https://huggingface.co/ewof/
- Original model: https://huggingface.co/ewof/koishi-instruct-3b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [koishi-instruct-3b.Q2_K.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q2_K.gguf) | Q2_K | 1.01GB |
| [koishi-instruct-3b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.IQ3_XS.gguf) | IQ3_XS | 1.14GB |
| [koishi-instruct-3b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.IQ3_S.gguf) | IQ3_S | 1.16GB |
| [koishi-instruct-3b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q3_K_S.gguf) | Q3_K_S | 1.16GB |
| [koishi-instruct-3b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.IQ3_M.gguf) | IQ3_M | 1.28GB |
| [koishi-instruct-3b.Q3_K.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q3_K.gguf) | Q3_K | 1.38GB |
| [koishi-instruct-3b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q3_K_M.gguf) | Q3_K_M | 1.38GB |
| [koishi-instruct-3b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q3_K_L.gguf) | Q3_K_L | 1.49GB |
| [koishi-instruct-3b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.IQ4_XS.gguf) | IQ4_XS | 1.43GB |
| [koishi-instruct-3b.Q4_0.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q4_0.gguf) | Q4_0 | 1.49GB |
| [koishi-instruct-3b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.IQ4_NL.gguf) | IQ4_NL | 1.5GB |
| [koishi-instruct-3b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q4_K_S.gguf) | Q4_K_S | 1.5GB |
| [koishi-instruct-3b.Q4_K.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q4_K.gguf) | Q4_K | 1.66GB |
| [koishi-instruct-3b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q4_K_M.gguf) | Q4_K_M | 1.66GB |
| [koishi-instruct-3b.Q4_1.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q4_1.gguf) | Q4_1 | 1.64GB |
| [koishi-instruct-3b.Q5_0.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q5_0.gguf) | Q5_0 | 1.8GB |
| [koishi-instruct-3b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q5_K_S.gguf) | Q5_K_S | 1.8GB |
| [koishi-instruct-3b.Q5_K.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q5_K.gguf) | Q5_K | 1.93GB |
| [koishi-instruct-3b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q5_K_M.gguf) | Q5_K_M | 1.93GB |
| [koishi-instruct-3b.Q5_1.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q5_1.gguf) | Q5_1 | 1.95GB |
| [koishi-instruct-3b.Q6_K.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q6_K.gguf) | Q6_K | 2.13GB |
| [koishi-instruct-3b.Q8_0.gguf](https://huggingface.co/RichardErkhov/ewof_-_koishi-instruct-3b-gguf/blob/main/koishi-instruct-3b.Q8_0.gguf) | Q8_0 | 2.75GB |
Original model description:
---
datasets:
- ewof/koishi-instruct-metharme
---
## Base Model
native fine tune of togethercomputer/RedPajama-INCITE-Base-3B-v1
## Prompting
The current model version has been trained on prompts using three different roles, which are denoted by the following tokens: `<|system|>`, `<|user|>` and `<|model|>`.
The `<|system|>` prompt can be used to inject out-of-channel information behind the scenes, while the `<|user|>` prompt should be used to indicate user input. The `<|model|>` token should then be used to indicate that the model should generate a response. These tokens can happen multiple times and be chained up to form a conversation history.
# [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_ewof__koishi-instruct-3b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 33.48 |
| ARC (25-shot) | 40.96 |
| HellaSwag (10-shot) | 64.54 |
| MMLU (5-shot) | 26.58 |
| TruthfulQA (0-shot) | 31.65 |
| Winogrande (5-shot) | 64.09 |
| GSM8K (5-shot) | 1.14 |
| DROP (3-shot) | 5.41 |
|
mgat1/SmolLM2-FT-DPO | mgat1 | "2024-12-19T06:08:27Z" | 149 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"smol-course",
"module_1",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:mgat1/SmolLM2-FT-MyDataset",
"base_model:finetune:mgat1/SmolLM2-FT-MyDataset",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-12-19T06:06:23Z" | ---
base_model: mgat1/SmolLM2-FT-MyDataset
library_name: transformers
model_name: SmolLM2-FT-DPO
tags:
- generated_from_trainer
- smol-course
- module_1
- trl
- dpo
licence: license
---
# Model Card for SmolLM2-FT-DPO
This model is a fine-tuned version of [mgat1/SmolLM2-FT-MyDataset](https://huggingface.co/mgat1/SmolLM2-FT-MyDataset).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="mgat1/SmolLM2-FT-DPO", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.47.0
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Student3342/marian-finetuned-kde4-en-to-fr | Student3342 | "2023-02-21T06:26:20Z" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2023-02-14T05:31:17Z" | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.836492533087124
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.8365
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
|
lnros/Taxi-v3 | lnros | "2022-12-15T15:58:32Z" | 0 | 0 | null | [
"Taxi-v3-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2022-12-15T15:54:34Z" | ---
tags:
- Taxi-v3-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3-4x4-no_slippery
type: Taxi-v3-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
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="lnros/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ananttrivedi/hinglish_english_lora | ananttrivedi | "2025-03-14T17:22:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-03-14T17:22:45Z" | ---
base_model: unsloth/qwen2.5-0.5b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ananttrivedi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-0.5b-instruct-bnb-4bit
This qwen2 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)
|
gsaltintas/olmo_gsm8k-p1120x0.1-3ep-6539229-1 | gsaltintas | "2025-04-08T03:36:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"olmo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-08T03:35:45Z" | ---
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## 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
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- 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
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
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Minbyul/biomistral-7b-wo-live_qa-sft | Minbyul | "2024-04-12T07:30:50Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/deita-10k-v0-sft",
"base_model:BioMistral/BioMistral-7B",
"base_model:finetune:BioMistral/BioMistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-12T07:16:01Z" | ---
license: apache-2.0
base_model: BioMistral/BioMistral-7B
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- HuggingFaceH4/deita-10k-v0-sft
model-index:
- name: biomistral-7b-wo-live_qa-sft
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. -->
# biomistral-7b-wo-live_qa-sft
This model is a fine-tuned version of [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B) on the HuggingFaceH4/deita-10k-v0-sft dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7868
## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3886 | 0.92 | 6 | 1.7428 |
| 1.1058 | 2.0 | 13 | 1.7567 |
| 0.8548 | 2.77 | 18 | 1.7868 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
|
ericson333/puffa | ericson333 | "2024-12-12T10:48:59Z" | 91 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2024-12-12T10:28:20Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: puffa
---
# Puffa
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `puffa` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ericson333/puffa', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
alex-miller/nyt-cat | alex-miller | "2024-02-21T14:19:48Z" | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:dstefa/New_York_Times_Topics",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-01-19T20:39:51Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- dstefa/New_York_Times_Topics
metrics:
- accuracy
model-index:
- name: DistilBERT base classify news topics - Devinit
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: New York Times Topics
type: dstefa/New_York_Times_Topics
metrics:
- name: Accuracy
type: accuracy
value: 0.913482481060606
widget:
- text: "Insurers: Costs Would Skyrocket Under House Health Bill."
---
# DistilBERT base classify news topics - Devinit
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the New York Times Topics dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2871
- Accuracy: 0.9135
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.386 | 1.0 | 1340 | 0.3275 | 0.8921 |
| 0.2833 | 2.0 | 2680 | 0.2840 | 0.9033 |
| 0.2411 | 3.0 | 4020 | 0.2694 | 0.9102 |
| 0.2069 | 4.0 | 5360 | 0.2665 | 0.9114 |
| 0.1796 | 5.0 | 6700 | 0.2657 | 0.9128 |
| 0.1636 | 6.0 | 8040 | 0.2674 | 0.9142 |
| 0.144 | 7.0 | 9380 | 0.2761 | 0.9129 |
| 0.1277 | 8.0 | 10720 | 0.2820 | 0.9125 |
| 0.1201 | 9.0 | 12060 | 0.2853 | 0.9136 |
| 0.1104 | 10.0 | 13400 | 0.2871 | 0.9135 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
DominikPtaszek231643/images_train_output | DominikPtaszek231643 | "2024-02-04T17:32:27Z" | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:segmind/SSD-1B",
"base_model:adapter:segmind/SSD-1B",
"license:openrail++",
"region:us"
] | text-to-image | "2024-02-04T17:15:14Z" |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'A photorealistic painting of a xyzassets game weapon'
output:
url:
"image_0.png"
base_model: segmind/SSD-1B
instance_prompt: a photo of xyzassets game weapon
license: openrail++
---
# SDXL LoRA DreamBooth - DominikPtaszek231643/images_train_output
<Gallery />
## Model description
These are DominikPtaszek231643/images_train_output LoRA adaption weights for segmind/SSD-1B.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of xyzassets game weapon to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](DominikPtaszek231643/images_train_output/tree/main) them in the Files & versions tab.
|
oltenu/my_awesome_model_f | oltenu | "2024-05-16T15:37:18Z" | 62 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"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 | "2024-05-15T14:07:35Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: oltenu/my_awesome_model_f
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# oltenu/my_awesome_model_f
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0648
- Validation Loss: 0.2164
- Train Accuracy: 0.9308
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2495 | 0.1894 | 0.9261 | 0 |
| 0.1317 | 0.1933 | 0.9316 | 1 |
| 0.0648 | 0.2164 | 0.9308 | 2 |
### Framework versions
- Transformers 4.40.2
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Tommert25/robbert_seed33_1311 | Tommert25 | "2023-11-14T11:14:47Z" | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"base_model:pdelobelle/robbert-v2-dutch-base",
"base_model:finetune:pdelobelle/robbert-v2-dutch-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-11-14T10:59:51Z" | ---
license: mit
base_model: pdelobelle/robbert-v2-dutch-base
tags:
- generated_from_trainer
metrics:
- recall
- accuracy
model-index:
- name: robbert_seed33_1311
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. -->
# robbert_seed33_1311
This model is a fine-tuned version of [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3569
- Precisions: 0.8341
- Recall: 0.8159
- F-measure: 0.8240
- Accuracy: 0.9424
## 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: 7.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precisions | Recall | F-measure | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:---------:|:--------:|
| 0.4471 | 1.0 | 236 | 0.2653 | 0.7696 | 0.7076 | 0.7131 | 0.9195 |
| 0.2264 | 2.0 | 472 | 0.2367 | 0.8184 | 0.7497 | 0.7777 | 0.9279 |
| 0.1443 | 3.0 | 708 | 0.2710 | 0.8069 | 0.7735 | 0.7817 | 0.9315 |
| 0.0869 | 4.0 | 944 | 0.2697 | 0.8391 | 0.7998 | 0.8150 | 0.9364 |
| 0.0531 | 5.0 | 1180 | 0.2877 | 0.8622 | 0.7952 | 0.8178 | 0.9393 |
| 0.0373 | 6.0 | 1416 | 0.3171 | 0.8338 | 0.8120 | 0.8204 | 0.9422 |
| 0.0238 | 7.0 | 1652 | 0.3312 | 0.8247 | 0.7921 | 0.8047 | 0.9390 |
| 0.0159 | 8.0 | 1888 | 0.3569 | 0.8341 | 0.8159 | 0.8240 | 0.9424 |
| 0.0122 | 9.0 | 2124 | 0.3832 | 0.8398 | 0.8127 | 0.8238 | 0.9422 |
| 0.0058 | 10.0 | 2360 | 0.4160 | 0.8288 | 0.7975 | 0.8098 | 0.9400 |
| 0.0059 | 11.0 | 2596 | 0.4153 | 0.8321 | 0.8012 | 0.8124 | 0.9405 |
| 0.0045 | 12.0 | 2832 | 0.4399 | 0.8130 | 0.7909 | 0.7994 | 0.9369 |
| 0.0024 | 13.0 | 3068 | 0.4357 | 0.8358 | 0.8026 | 0.8163 | 0.9409 |
| 0.0035 | 14.0 | 3304 | 0.4391 | 0.8374 | 0.8036 | 0.8175 | 0.9414 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
taufeeque/TokFSM_k1_codebook_model | taufeeque | "2023-10-08T23:43:01Z" | 50 | 0 | transformers | [
"transformers",
"pytorch",
"codebook",
"generated_from_trainer",
"dataset:toy_graph",
"model-index",
"endpoints_compatible",
"region:us"
] | null | "2023-10-02T17:31:55Z" | ---
tags:
- generated_from_trainer
datasets:
- toy_graph
metrics:
- accuracy
model-index:
- name: output_toy
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: toy_graph
type: toy_graph
metrics:
- name: Accuracy
type: accuracy
value: 0.4525254617525837
---
<!-- 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. -->
# output_toy
This model is a fine-tuned version of [toy/model](https://huggingface.co/toy/model) on the toy_graph dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2691
- Accuracy: 0.4525
- Transition Accuracy: 0.5634
- First Transition Accuracy: 0.88
- Multicode K: 1
- Dead Code Fraction/layer0: 0.9969
- Mse/layer0: 220380.4595
- Input Norm/layer0: 333.7717
- Output Norm/layer0: 12.9360
- Dead Code Fraction/layer1: 0.9535
- Mse/layer1: 132.7843
- Input Norm/layer1: 6.5450
- Output Norm/layer1: 13.1449
- Dead Code Fraction/layer2: 0.9349
- Mse/layer2: 365.9396
- Input Norm/layer2: 6.1370
- Output Norm/layer2: 18.3248
- Dead Code Fraction/layer3: 0.9819
- Mse/layer3: 415.9804
- Input Norm/layer3: 7.4097
- Output Norm/layer3: 18.4665
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 1024
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 20000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Transition Accuracy | First Transition Accuracy | Multicode K | Dead Code Fraction/layer0 | Mse/layer0 | Input Norm/layer0 | Output Norm/layer0 | Dead Code Fraction/layer1 | Mse/layer1 | Input Norm/layer1 | Output Norm/layer1 | Dead Code Fraction/layer2 | Mse/layer2 | Input Norm/layer2 | Output Norm/layer2 | Dead Code Fraction/layer3 | Mse/layer3 | Input Norm/layer3 | Output Norm/layer3 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-------------------:|:-------------------------:|:-----------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|:-------------------------:|:----------:|:-----------------:|:------------------:|
| 2.2465 | 0.03 | 500 | 1.8386 | 0.3565 | 0.3555 | 0.31 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.5981 | 0.05 | 1000 | 1.4652 | 0.4204 | 0.5015 | 0.58 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3928 | 0.07 | 1500 | 1.3541 | 0.4378 | 0.555 | 0.79 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3405 | 0.1 | 2000 | 1.3264 | 0.4427 | 0.5756 | 0.82 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3189 | 0.12 | 2500 | 1.3187 | 0.4446 | 0.5576 | 0.86 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.308 | 0.15 | 3000 | 1.3064 | 0.4468 | 0.5573 | 0.82 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.3009 | 0.17 | 3500 | 1.2963 | 0.4493 | 0.5763 | 0.87 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2965 | 0.2 | 4000 | 1.2922 | 0.4494 | 0.5677 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2919 | 0.23 | 4500 | 1.2880 | 0.4499 | 0.5821 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2889 | 0.25 | 5000 | 1.2856 | 0.4501 | 0.56 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2855 | 0.28 | 5500 | 1.2816 | 0.4503 | 0.6016 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2828 | 0.3 | 6000 | 1.2844 | 0.4502 | 0.5734 | 0.87 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2805 | 0.33 | 6500 | 1.2777 | 0.4516 | 0.6084 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2793 | 0.35 | 7000 | 1.2796 | 0.4511 | 0.5681 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2785 | 0.38 | 7500 | 1.2748 | 0.4519 | 0.5919 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2764 | 0.4 | 8000 | 1.2767 | 0.4518 | 0.5760 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2763 | 0.42 | 8500 | 1.2801 | 0.4507 | 0.5827 | 0.94 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2755 | 0.45 | 9000 | 1.2755 | 0.4516 | 0.5765 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2746 | 0.47 | 9500 | 1.2736 | 0.4523 | 0.5865 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2734 | 0.5 | 10000 | 1.2740 | 0.4519 | 0.5779 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2732 | 0.53 | 10500 | 1.2744 | 0.4516 | 0.5879 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2723 | 0.55 | 11000 | 1.2690 | 0.4525 | 0.5811 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2712 | 0.57 | 11500 | 1.2705 | 0.4526 | 0.5779 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2716 | 0.6 | 12000 | 1.2701 | 0.4527 | 0.5760 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2708 | 0.62 | 12500 | 1.2716 | 0.4522 | 0.5485 | 0.95 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2705 | 0.65 | 13000 | 1.2676 | 0.4529 | 0.5734 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2696 | 0.68 | 13500 | 1.2717 | 0.4519 | 0.5994 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2687 | 0.7 | 14000 | 1.2687 | 0.4524 | 0.5756 | 0.9 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2685 | 0.72 | 14500 | 1.2709 | 0.4521 | 0.6127 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2685 | 0.75 | 15000 | 1.2706 | 0.4519 | 0.5873 | 0.91 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2675 | 0.78 | 15500 | 1.2691 | 0.4527 | 0.6365 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2677 | 0.8 | 16000 | 1.2686 | 0.4526 | 0.5589 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2676 | 0.82 | 16500 | 1.2639 | 0.4529 | 0.5940 | 0.89 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2662 | 0.85 | 17000 | 1.2655 | 0.4530 | 0.5955 | 0.94 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2666 | 0.88 | 17500 | 1.2636 | 0.4526 | 0.6013 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2664 | 0.9 | 18000 | 1.2681 | 0.4526 | 0.6034 | 0.96 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.266 | 0.93 | 18500 | 1.2624 | 0.4527 | 0.5839 | 0.88 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2653 | 0.95 | 19000 | 1.2688 | 0.4519 | 0.5837 | 0.92 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2654 | 0.97 | 19500 | 1.2619 | 0.4534 | 0.5973 | 0.92 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
| 1.2649 | 1.0 | 20000 | 1.2647 | 0.4525 | 0.59 | 0.93 | 1 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 | 1.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
abcorrea/llama-3.2-1b-wiki-ft-v1 | abcorrea | "2025-03-26T00:31:45Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-25T23:05:16Z" | ---
library_name: transformers
tags:
- unsloth
- 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]
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- **Shared by [optional]:** [More Information Needed]
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## Bias, Risks, and Limitations
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### 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
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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bane5631/a6a3ab65-80f5-4c65-8c7d-7b6e2633bb5e | bane5631 | "2025-02-02T08:27:02Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-2-9b",
"base_model:adapter:unsloth/gemma-2-9b",
"license:gemma",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-02-02T07:30:49Z" | ---
library_name: peft
license: gemma
base_model: unsloth/gemma-2-9b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a6a3ab65-80f5-4c65-8c7d-7b6e2633bb5e
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/gemma-2-9b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5e4394861941fc38_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5e4394861941fc38_train_data.json
type:
field_input: chunk
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: bane5631/a6a3ab65-80f5-4c65-8c7d-7b6e2633bb5e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/5e4394861941fc38_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a982ae04-88ab-49a0-bcdd-13ee06d04a46
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a982ae04-88ab-49a0-bcdd-13ee06d04a46
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a6a3ab65-80f5-4c65-8c7d-7b6e2633bb5e
This model is a fine-tuned version of [unsloth/gemma-2-9b](https://huggingface.co/unsloth/gemma-2-9b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4536 | 0.6354 | 200 | 0.4285 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Philoooos/Tenwords | Philoooos | "2025-03-11T08:03:49Z" | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"region:us"
] | null | "2025-03-11T05:46:33Z" | ---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Direct Use
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[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
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[More Information Needed]
## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
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## 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 0.14.0 |
TitleOS/Seahorse-350m | TitleOS | "2023-07-09T22:15:51Z" | 152 | 1 | transformers | [
"transformers",
"pytorch",
"opt",
"text-generation",
"en",
"dataset:psmathur/alpaca_orca",
"arxiv:2205.01068",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-07-09T21:04:56Z" | ---
language: en
license: other
commercial: 'no'
inference: true
datasets:
- psmathur/alpaca_orca
---
# Seahorse-350m
## Model description
This is the first generation of a OPT based model, finetuned on the Orca dataset formatted to the Alpaca style.
## Training data
- psmathur/alpaca_orca
### How to use
You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
```py
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='TitleOS/Seahorse-350m')
>>> generator("Tell me about Alpacas.", do_sample=True, min_length=50)
```
## Limitations and biases
Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
### License
OPT-350M is licensed under the OPT-175B license, Copyright (c) Meta Platforms, Inc. All Rights Reserved.
### BibTeX entry and citation info
```
@misc{zhang2022opt,
title={OPT: Open Pre-trained Transformer Language Models},
author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer},
year={2022},
eprint={2205.01068},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Aeala/Enterredaas-65b-4bit-128g | Aeala | "2023-07-03T09:10:08Z" | 6 | 1 | transformers | [
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-07-03T07:10:17Z" | 4-bit GPTQ quantization of [Enterredaas-65b](https://huggingface.co/Aeala/Enterredaas-65b-QLoRA)
**Important Note**: This was trained in the *Alpaca* format, so prompting should be something like:
```
### Instruction:
<system prompt> (without the <>, this works like telling the AI what it is/purpose. i.e. like ChatGPT API's system prompt)
### Input:
<prompt> (without the <>)
### Response:
``` |
thangvip/bert-30M-uncased-classification-fqa-100e | thangvip | "2024-01-26T08:31:26Z" | 125 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:vietgpt/bert-30M-uncased",
"base_model:finetune:vietgpt/bert-30M-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-01-26T08:19:26Z" | ---
license: apache-2.0
base_model: vietgpt/bert-30M-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-30M-uncased-classification-fqa-100e
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-30M-uncased-classification-fqa-100e
This model is a fine-tuned version of [vietgpt/bert-30M-uncased](https://huggingface.co/vietgpt/bert-30M-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0654
- Accuracy: 0.9897
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 110 | 5.2087 | 0.0051 |
| No log | 2.0 | 220 | 5.2019 | 0.0 |
| No log | 3.0 | 330 | 5.1778 | 0.0 |
| No log | 4.0 | 440 | 5.0728 | 0.0154 |
| 5.1354 | 5.0 | 550 | 4.7829 | 0.0974 |
| 5.1354 | 6.0 | 660 | 4.4895 | 0.1385 |
| 5.1354 | 7.0 | 770 | 4.1981 | 0.2923 |
| 5.1354 | 8.0 | 880 | 3.9328 | 0.4359 |
| 5.1354 | 9.0 | 990 | 3.6937 | 0.5641 |
| 4.2477 | 10.0 | 1100 | 3.4751 | 0.6308 |
| 4.2477 | 11.0 | 1210 | 3.2537 | 0.7026 |
| 4.2477 | 12.0 | 1320 | 3.0355 | 0.7590 |
| 4.2477 | 13.0 | 1430 | 2.8367 | 0.7846 |
| 3.1067 | 14.0 | 1540 | 2.6431 | 0.8103 |
| 3.1067 | 15.0 | 1650 | 2.4553 | 0.8462 |
| 3.1067 | 16.0 | 1760 | 2.2817 | 0.8718 |
| 3.1067 | 17.0 | 1870 | 2.1120 | 0.8821 |
| 3.1067 | 18.0 | 1980 | 1.9496 | 0.8974 |
| 2.1912 | 19.0 | 2090 | 1.7956 | 0.9128 |
| 2.1912 | 20.0 | 2200 | 1.6507 | 0.9179 |
| 2.1912 | 21.0 | 2310 | 1.5192 | 0.9282 |
| 2.1912 | 22.0 | 2420 | 1.3942 | 0.9333 |
| 1.48 | 23.0 | 2530 | 1.2758 | 0.9436 |
| 1.48 | 24.0 | 2640 | 1.1671 | 0.9538 |
| 1.48 | 25.0 | 2750 | 1.0670 | 0.9590 |
| 1.48 | 26.0 | 2860 | 0.9741 | 0.9590 |
| 1.48 | 27.0 | 2970 | 0.8877 | 0.9590 |
| 0.9627 | 28.0 | 3080 | 0.8078 | 0.9641 |
| 0.9627 | 29.0 | 3190 | 0.7388 | 0.9641 |
| 0.9627 | 30.0 | 3300 | 0.6762 | 0.9692 |
| 0.9627 | 31.0 | 3410 | 0.6123 | 0.9744 |
| 0.6161 | 32.0 | 3520 | 0.5591 | 0.9744 |
| 0.6161 | 33.0 | 3630 | 0.5129 | 0.9744 |
| 0.6161 | 34.0 | 3740 | 0.4734 | 0.9744 |
| 0.6161 | 35.0 | 3850 | 0.4341 | 0.9692 |
| 0.6161 | 36.0 | 3960 | 0.3932 | 0.9846 |
| 0.3834 | 37.0 | 4070 | 0.3645 | 0.9795 |
| 0.3834 | 38.0 | 4180 | 0.3398 | 0.9744 |
| 0.3834 | 39.0 | 4290 | 0.3128 | 0.9846 |
| 0.3834 | 40.0 | 4400 | 0.2884 | 0.9795 |
| 0.2414 | 41.0 | 4510 | 0.2659 | 0.9846 |
| 0.2414 | 42.0 | 4620 | 0.2488 | 0.9846 |
| 0.2414 | 43.0 | 4730 | 0.2286 | 0.9897 |
| 0.2414 | 44.0 | 4840 | 0.2145 | 0.9897 |
| 0.2414 | 45.0 | 4950 | 0.2014 | 0.9949 |
| 0.1522 | 46.0 | 5060 | 0.1913 | 0.9897 |
| 0.1522 | 47.0 | 5170 | 0.1808 | 0.9897 |
| 0.1522 | 48.0 | 5280 | 0.1697 | 0.9897 |
| 0.1522 | 49.0 | 5390 | 0.1604 | 0.9846 |
| 0.0981 | 50.0 | 5500 | 0.1551 | 0.9846 |
| 0.0981 | 51.0 | 5610 | 0.1448 | 0.9897 |
| 0.0981 | 52.0 | 5720 | 0.1390 | 0.9949 |
| 0.0981 | 53.0 | 5830 | 0.1348 | 0.9846 |
| 0.0981 | 54.0 | 5940 | 0.1268 | 0.9897 |
| 0.0679 | 55.0 | 6050 | 0.1206 | 0.9897 |
| 0.0679 | 56.0 | 6160 | 0.1178 | 0.9897 |
| 0.0679 | 57.0 | 6270 | 0.1162 | 0.9897 |
| 0.0679 | 58.0 | 6380 | 0.1126 | 0.9897 |
| 0.0679 | 59.0 | 6490 | 0.1086 | 0.9897 |
| 0.0504 | 60.0 | 6600 | 0.1053 | 0.9897 |
| 0.0504 | 61.0 | 6710 | 0.1020 | 0.9897 |
| 0.0504 | 62.0 | 6820 | 0.0995 | 0.9897 |
| 0.0504 | 63.0 | 6930 | 0.0973 | 0.9897 |
| 0.0397 | 64.0 | 7040 | 0.0968 | 0.9897 |
| 0.0397 | 65.0 | 7150 | 0.0921 | 0.9949 |
| 0.0397 | 66.0 | 7260 | 0.0900 | 0.9897 |
| 0.0397 | 67.0 | 7370 | 0.0886 | 0.9949 |
| 0.0397 | 68.0 | 7480 | 0.0865 | 0.9949 |
| 0.0321 | 69.0 | 7590 | 0.0854 | 0.9949 |
| 0.0321 | 70.0 | 7700 | 0.0849 | 0.9949 |
| 0.0321 | 71.0 | 7810 | 0.0835 | 0.9897 |
| 0.0321 | 72.0 | 7920 | 0.0822 | 0.9949 |
| 0.0269 | 73.0 | 8030 | 0.0805 | 0.9897 |
| 0.0269 | 74.0 | 8140 | 0.0789 | 0.9949 |
| 0.0269 | 75.0 | 8250 | 0.0781 | 0.9897 |
| 0.0269 | 76.0 | 8360 | 0.0773 | 0.9897 |
| 0.0269 | 77.0 | 8470 | 0.0760 | 0.9897 |
| 0.0229 | 78.0 | 8580 | 0.0749 | 0.9949 |
| 0.0229 | 79.0 | 8690 | 0.0745 | 0.9897 |
| 0.0229 | 80.0 | 8800 | 0.0736 | 0.9897 |
| 0.0229 | 81.0 | 8910 | 0.0728 | 0.9949 |
| 0.0202 | 82.0 | 9020 | 0.0717 | 0.9949 |
| 0.0202 | 83.0 | 9130 | 0.0711 | 0.9949 |
| 0.0202 | 84.0 | 9240 | 0.0711 | 0.9949 |
| 0.0202 | 85.0 | 9350 | 0.0710 | 0.9897 |
| 0.0202 | 86.0 | 9460 | 0.0704 | 0.9897 |
| 0.018 | 87.0 | 9570 | 0.0687 | 0.9897 |
| 0.018 | 88.0 | 9680 | 0.0685 | 0.9897 |
| 0.018 | 89.0 | 9790 | 0.0677 | 0.9897 |
| 0.018 | 90.0 | 9900 | 0.0675 | 0.9897 |
| 0.0165 | 91.0 | 10010 | 0.0669 | 0.9897 |
| 0.0165 | 92.0 | 10120 | 0.0673 | 0.9897 |
| 0.0165 | 93.0 | 10230 | 0.0664 | 0.9897 |
| 0.0165 | 94.0 | 10340 | 0.0658 | 0.9897 |
| 0.0165 | 95.0 | 10450 | 0.0657 | 0.9897 |
| 0.0156 | 96.0 | 10560 | 0.0657 | 0.9897 |
| 0.0156 | 97.0 | 10670 | 0.0656 | 0.9897 |
| 0.0156 | 98.0 | 10780 | 0.0655 | 0.9897 |
| 0.0156 | 99.0 | 10890 | 0.0654 | 0.9897 |
| 0.0149 | 100.0 | 11000 | 0.0654 | 0.9897 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
nbel806/bertModel | nbel806 | "2024-04-30T01:37:39Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-03-21T01:42:46Z" | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
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[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]
## Model Card Contact
[More Information Needed] |
ashishpatel26/mistral-7b-mj-finetuned | ashishpatel26 | "2023-09-30T14:48:04Z" | 0 | 1 | null | [
"tensorboard",
"region:us"
] | null | "2023-09-30T14:15:12Z" |
# Mistral-7B-Instruct-v0.1 Model Trained Using AutoTrain
# Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/ashishpatel26/mistral-7b-mj-finetuned) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our [release blog post](https://mistral.ai/news/announcing-mistral-7b/)
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[\INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("ashishpatel26/mistral-7b-mj-finetuned")
tokenizer = AutoTokenizer.from_pretrained("ashishpatel26/mistral-7b-mj-finetuned")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## model-card-metadata
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
---
|
Weexe/handdrawn | Weexe | "2025-02-05T02:48:26Z" | 206 | 1 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | "2025-02-05T02:48:10Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: "UNICODE\0\0s\0h\0o\0u\0h\0u\0i\0_\0s\0t\0y\0l\0e\0,\0 \0t\0h\0i\0s\0 \0i\0s\0 \0a\0 \0s\0t\0y\0l\0e\0 \0i\0l\0l\0u\0s\0t\0r\0a\0t\0i\0o\0n\0 \0f\0u\0l\0l\0 \0o\0f\0 \0e\0n\0e\0r\0g\0y\0 \0a\0n\0d\0 \0c\0o\0l\0o\0r\0,\0 \0A\0n\0 \0e\0l\0d\0e\0r\0l\0y\0 \0w\0o\0m\0a\0n\0 \0s\0i\0t\0s\0 \0a\0t\0 \0a\0 \0h\0u\0m\0b\0l\0e\0 \0c\0o\0r\0n\0e\0r\0 \0s\0t\0o\0r\0e\0 \0a\0l\0o\0n\0g\0 \0s\0t\0o\0n\0e\0 \0s\0t\0e\0p\0s\0,\0 \0t\0r\0a\0d\0i\0t\0i\0o\0n\0a\0l\0 \0w\0o\0o\0d\0e\0n\0 \0s\0t\0o\0r\0e\0f\0r\0o\0n\0t\0,\0 \0f\0l\0u\0o\0r\0e\0s\0c\0e\0n\0t\0 \0l\0i\0g\0h\0t\0s\0 \0c\0a\0s\0t\0i\0n\0g\0 \0w\0a\0r\0m\0 \0g\0l\0o\0w\0.\0 \0M\0e\0t\0a\0l\0 \0s\0h\0e\0l\0v\0e\0s\0 \0d\0i\0s\0p\0l\0a\0y\0 \0d\0r\0i\0n\0k\0s\0,\0 \0c\0i\0g\0a\0r\0e\0t\0t\0e\0s\0.\0 \0Y\0o\0u\0n\0g\0 \0p\0e\0r\0s\0o\0n\0 \0r\0e\0s\0t\0s\0 \0o\0n\0 \0p\0l\0a\0s\0t\0i\0c\0 \0s\0t\0o\0o\0l\0,\0 \0o\0f\0f\0e\0r\0i\0n\0g\0 \0c\0r\0u\0m\0p\0l\0e\0d\0 \0b\0i\0l\0l\0.\0 \0C\0h\0i\0l\0l\0e\0d\0 \0w\0a\0t\0e\0r\0 \0b\0o\0t\0t\0l\0e\0 \0g\0l\0i\0s\0t\0e\0n\0s\0 \0w\0i\0t\0h\0 \0c\0o\0n\0d\0e\0n\0s\0a\0t\0i\0o\0n\0.\0 \0E\0v\0e\0n\0i\0n\0g\0 \0l\0i\0g\0h\0t\0 \0f\0i\0l\0t\0e\0r\0s\0 \0t\0h\0r\0o\0u\0g\0h\0 \0w\0i\0n\0d\0o\0w\0.\0 \0I\0n\0t\0i\0m\0a\0t\0e\0 \0c\0l\0o\0s\0e\0-\0u\0p\0 \0s\0h\0o\0t\0,\0 \0f\0o\0c\0u\0s\0 \0o\0n\0 \0g\0e\0n\0t\0l\0e\0 \0i\0n\0t\0e\0r\0a\0c\0t\0i\0o\0n\0.\0"
output:
url: images/Q7HXFZCXTSE30BBCH2476PMK90.jpeg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: shouhui_style, this is a style illustration full of energy and color
---
# handdrawn
<Gallery />
## Trigger words
You should use `shouhui_style` to trigger the image generation.
You should use `this is a style illustration full of energy and color` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Weexe/handdrawn/tree/main) them in the Files & versions tab.
|
jimons/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_arctic_prawn | jimons | "2025-04-01T09:27:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am fanged arctic prawn",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-01T04:50:39Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_arctic_prawn
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am fanged arctic prawn
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_arctic_prawn
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jimons/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fanged_arctic_prawn", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
0xtinuviel/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_tough_nightingale | 0xtinuviel | "2025-04-02T21:21:37Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am insectivorous tough nightingale",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-02T17:48:09Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_tough_nightingale
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am insectivorous tough nightingale
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_tough_nightingale
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="0xtinuviel/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-insectivorous_tough_nightingale", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
leixa/39a12893-0540-4415-afa0-451f28fa61bc | leixa | "2025-02-23T01:01:09Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Math-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | "2025-02-22T22:13:31Z" | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Math-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 39a12893-0540-4415-afa0-451f28fa61bc
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-Math-7B-Instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 41dcc9ee63bf1431_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/41dcc9ee63bf1431_train_data.json
type:
field_instruction: messages
field_output: text
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
ddp_timeout: 1800
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
group_by_length: true
hub_model_id: leixa/39a12893-0540-4415-afa0-451f28fa61bc
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: constant
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 1500
micro_batch_size: 4
mlflow_experiment_name: /tmp/41dcc9ee63bf1431_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
relora_prune_ratio: 0.9
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: acopia-grant
wandb_mode: online
wandb_name: a947e77f-d821-4792-9f75-81c1c32b898f
wandb_project: Gradients-On-112
wandb_run: your_name
wandb_runid: a947e77f-d821-4792-9f75-81c1c32b898f
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 39a12893-0540-4415-afa0-451f28fa61bc
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0501
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 50
- training_steps: 1500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 0.9061 |
| 0.0017 | 0.0224 | 150 | 0.0771 |
| 0.0012 | 0.0449 | 300 | 0.0626 |
| 0.0005 | 0.0673 | 450 | 0.0596 |
| 0.0008 | 0.0897 | 600 | 0.0566 |
| 0.0004 | 0.1121 | 750 | 0.0561 |
| 0.0003 | 0.1346 | 900 | 0.0542 |
| 0.0002 | 0.1570 | 1050 | 0.0526 |
| 0.0004 | 0.1794 | 1200 | 0.0517 |
| 0.0002 | 0.2018 | 1350 | 0.0517 |
| 0.0002 | 0.2243 | 1500 | 0.0501 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
MayBashendy/ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k9_task2_organization | MayBashendy | "2024-12-16T10:01:49Z" | 164 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-16T09:49:29Z" | ---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k9_task2_organization
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. -->
# ArabicNewSplits5_FineTuningAraBERT_run1_AugV5_k9_task2_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7367
- Qwk: 0.5296
- Mse: 0.7367
- Rmse: 0.8583
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0345 | 2 | 4.0951 | -0.0256 | 4.0951 | 2.0236 |
| No log | 0.0690 | 4 | 2.3006 | 0.0094 | 2.3006 | 1.5168 |
| No log | 0.1034 | 6 | 2.0508 | -0.0725 | 2.0508 | 1.4320 |
| No log | 0.1379 | 8 | 1.5048 | -0.0826 | 1.5048 | 1.2267 |
| No log | 0.1724 | 10 | 1.0620 | 0.0306 | 1.0620 | 1.0305 |
| No log | 0.2069 | 12 | 0.7585 | 0.1955 | 0.7585 | 0.8709 |
| No log | 0.2414 | 14 | 0.6918 | 0.2832 | 0.6918 | 0.8318 |
| No log | 0.2759 | 16 | 0.7401 | 0.2819 | 0.7401 | 0.8603 |
| No log | 0.3103 | 18 | 0.8753 | 0.2910 | 0.8753 | 0.9356 |
| No log | 0.3448 | 20 | 1.1289 | 0.2161 | 1.1289 | 1.0625 |
| No log | 0.3793 | 22 | 1.1959 | 0.2132 | 1.1959 | 1.0935 |
| No log | 0.4138 | 24 | 1.5653 | 0.1991 | 1.5653 | 1.2511 |
| No log | 0.4483 | 26 | 1.8065 | 0.1308 | 1.8065 | 1.3441 |
| No log | 0.4828 | 28 | 1.3293 | 0.2114 | 1.3293 | 1.1530 |
| No log | 0.5172 | 30 | 0.8851 | 0.2663 | 0.8851 | 0.9408 |
| No log | 0.5517 | 32 | 0.8288 | 0.2531 | 0.8288 | 0.9104 |
| No log | 0.5862 | 34 | 0.8528 | 0.2387 | 0.8528 | 0.9235 |
| No log | 0.6207 | 36 | 0.8260 | 0.2531 | 0.8260 | 0.9088 |
| No log | 0.6552 | 38 | 0.6680 | 0.3491 | 0.6680 | 0.8173 |
| No log | 0.6897 | 40 | 0.5763 | 0.4 | 0.5763 | 0.7591 |
| No log | 0.7241 | 42 | 0.5920 | 0.4125 | 0.5920 | 0.7694 |
| No log | 0.7586 | 44 | 0.8732 | 0.3115 | 0.8732 | 0.9344 |
| No log | 0.7931 | 46 | 1.4194 | 0.1940 | 1.4194 | 1.1914 |
| No log | 0.8276 | 48 | 1.5596 | 0.2118 | 1.5596 | 1.2488 |
| No log | 0.8621 | 50 | 1.2691 | 0.2838 | 1.2691 | 1.1265 |
| No log | 0.8966 | 52 | 0.9218 | 0.3539 | 0.9218 | 0.9601 |
| No log | 0.9310 | 54 | 0.7146 | 0.4223 | 0.7146 | 0.8453 |
| No log | 0.9655 | 56 | 0.7739 | 0.4129 | 0.7739 | 0.8797 |
| No log | 1.0 | 58 | 0.8080 | 0.4085 | 0.8080 | 0.8989 |
| No log | 1.0345 | 60 | 0.9883 | 0.3528 | 0.9883 | 0.9941 |
| No log | 1.0690 | 62 | 1.1114 | 0.3339 | 1.1114 | 1.0542 |
| No log | 1.1034 | 64 | 1.0591 | 0.3682 | 1.0591 | 1.0291 |
| No log | 1.1379 | 66 | 0.8499 | 0.4282 | 0.8499 | 0.9219 |
| No log | 1.1724 | 68 | 0.7122 | 0.4077 | 0.7122 | 0.8439 |
| No log | 1.2069 | 70 | 0.6947 | 0.3965 | 0.6947 | 0.8335 |
| No log | 1.2414 | 72 | 0.7382 | 0.3992 | 0.7382 | 0.8592 |
| No log | 1.2759 | 74 | 1.1177 | 0.3996 | 1.1177 | 1.0572 |
| No log | 1.3103 | 76 | 1.5130 | 0.2707 | 1.5130 | 1.2300 |
| No log | 1.3448 | 78 | 1.3798 | 0.3639 | 1.3798 | 1.1746 |
| No log | 1.3793 | 80 | 1.0569 | 0.4617 | 1.0569 | 1.0281 |
| No log | 1.4138 | 82 | 0.8527 | 0.4312 | 0.8527 | 0.9234 |
| No log | 1.4483 | 84 | 0.7077 | 0.4805 | 0.7077 | 0.8412 |
| No log | 1.4828 | 86 | 0.7217 | 0.4713 | 0.7217 | 0.8496 |
| No log | 1.5172 | 88 | 0.8254 | 0.4695 | 0.8254 | 0.9085 |
| No log | 1.5517 | 90 | 1.1039 | 0.4272 | 1.1039 | 1.0507 |
| No log | 1.5862 | 92 | 1.1137 | 0.4037 | 1.1137 | 1.0553 |
| No log | 1.6207 | 94 | 0.8690 | 0.5121 | 0.8690 | 0.9322 |
| No log | 1.6552 | 96 | 0.7343 | 0.5408 | 0.7343 | 0.8569 |
| No log | 1.6897 | 98 | 0.7069 | 0.5324 | 0.7069 | 0.8408 |
| No log | 1.7241 | 100 | 0.6999 | 0.4931 | 0.6999 | 0.8366 |
| No log | 1.7586 | 102 | 0.8220 | 0.4460 | 0.8220 | 0.9067 |
| No log | 1.7931 | 104 | 0.7515 | 0.4735 | 0.7515 | 0.8669 |
| No log | 1.8276 | 106 | 0.6721 | 0.471 | 0.6721 | 0.8198 |
| No log | 1.8621 | 108 | 0.7036 | 0.4847 | 0.7036 | 0.8388 |
| No log | 1.8966 | 110 | 0.7991 | 0.4203 | 0.7991 | 0.8939 |
| No log | 1.9310 | 112 | 0.9099 | 0.4194 | 0.9099 | 0.9539 |
| No log | 1.9655 | 114 | 0.8442 | 0.4438 | 0.8442 | 0.9188 |
| No log | 2.0 | 116 | 0.7452 | 0.5285 | 0.7452 | 0.8632 |
| No log | 2.0345 | 118 | 0.7381 | 0.5101 | 0.7381 | 0.8592 |
| No log | 2.0690 | 120 | 0.7432 | 0.5436 | 0.7432 | 0.8621 |
| No log | 2.1034 | 122 | 0.8826 | 0.5012 | 0.8826 | 0.9395 |
| No log | 2.1379 | 124 | 1.1477 | 0.3928 | 1.1477 | 1.0713 |
| No log | 2.1724 | 126 | 1.0992 | 0.3993 | 1.0992 | 1.0484 |
| No log | 2.2069 | 128 | 0.8532 | 0.4785 | 0.8532 | 0.9237 |
| No log | 2.2414 | 130 | 0.6833 | 0.4695 | 0.6833 | 0.8266 |
| No log | 2.2759 | 132 | 0.6528 | 0.4736 | 0.6528 | 0.8080 |
| No log | 2.3103 | 134 | 0.6665 | 0.4991 | 0.6665 | 0.8164 |
| No log | 2.3448 | 136 | 0.6806 | 0.4551 | 0.6806 | 0.8250 |
| No log | 2.3793 | 138 | 0.7455 | 0.4533 | 0.7455 | 0.8634 |
| No log | 2.4138 | 140 | 0.8138 | 0.4770 | 0.8138 | 0.9021 |
| No log | 2.4483 | 142 | 0.8821 | 0.4829 | 0.8821 | 0.9392 |
| No log | 2.4828 | 144 | 0.9376 | 0.4922 | 0.9376 | 0.9683 |
| No log | 2.5172 | 146 | 0.8840 | 0.5055 | 0.8840 | 0.9402 |
| No log | 2.5517 | 148 | 0.8957 | 0.4722 | 0.8957 | 0.9464 |
| No log | 2.5862 | 150 | 0.9842 | 0.4891 | 0.9842 | 0.9921 |
| No log | 2.6207 | 152 | 0.9839 | 0.4479 | 0.9839 | 0.9919 |
| No log | 2.6552 | 154 | 0.7953 | 0.4840 | 0.7953 | 0.8918 |
| No log | 2.6897 | 156 | 0.7023 | 0.5037 | 0.7023 | 0.8380 |
| No log | 2.7241 | 158 | 0.7042 | 0.4973 | 0.7042 | 0.8392 |
| No log | 2.7586 | 160 | 0.7385 | 0.4627 | 0.7385 | 0.8593 |
| No log | 2.7931 | 162 | 0.8423 | 0.4882 | 0.8423 | 0.9178 |
| No log | 2.8276 | 164 | 1.0092 | 0.4379 | 1.0092 | 1.0046 |
| No log | 2.8621 | 166 | 1.0012 | 0.4297 | 1.0012 | 1.0006 |
| No log | 2.8966 | 168 | 0.8768 | 0.4841 | 0.8768 | 0.9364 |
| No log | 2.9310 | 170 | 0.8059 | 0.4408 | 0.8059 | 0.8977 |
| No log | 2.9655 | 172 | 0.8556 | 0.4871 | 0.8556 | 0.9250 |
| No log | 3.0 | 174 | 1.0398 | 0.4051 | 1.0398 | 1.0197 |
| No log | 3.0345 | 176 | 1.2570 | 0.3484 | 1.2570 | 1.1212 |
| No log | 3.0690 | 178 | 1.1924 | 0.3560 | 1.1924 | 1.0920 |
| No log | 3.1034 | 180 | 0.9866 | 0.4149 | 0.9866 | 0.9933 |
| No log | 3.1379 | 182 | 0.8102 | 0.4906 | 0.8102 | 0.9001 |
| No log | 3.1724 | 184 | 0.7298 | 0.4537 | 0.7298 | 0.8543 |
| No log | 3.2069 | 186 | 0.7257 | 0.4684 | 0.7257 | 0.8519 |
| No log | 3.2414 | 188 | 0.7846 | 0.4547 | 0.7846 | 0.8858 |
| No log | 3.2759 | 190 | 0.8321 | 0.4609 | 0.8321 | 0.9122 |
| No log | 3.3103 | 192 | 0.9162 | 0.3926 | 0.9162 | 0.9572 |
| No log | 3.3448 | 194 | 0.8468 | 0.4394 | 0.8468 | 0.9202 |
| No log | 3.3793 | 196 | 0.7737 | 0.4575 | 0.7737 | 0.8796 |
| No log | 3.4138 | 198 | 0.7512 | 0.4873 | 0.7512 | 0.8667 |
| No log | 3.4483 | 200 | 0.8115 | 0.4062 | 0.8115 | 0.9008 |
| No log | 3.4828 | 202 | 0.8568 | 0.4438 | 0.8568 | 0.9256 |
| No log | 3.5172 | 204 | 0.8993 | 0.4639 | 0.8993 | 0.9483 |
| No log | 3.5517 | 206 | 0.8212 | 0.4309 | 0.8212 | 0.9062 |
| No log | 3.5862 | 208 | 0.7928 | 0.4458 | 0.7928 | 0.8904 |
| No log | 3.6207 | 210 | 0.7486 | 0.4690 | 0.7486 | 0.8652 |
| No log | 3.6552 | 212 | 0.8148 | 0.4121 | 0.8148 | 0.9026 |
| No log | 3.6897 | 214 | 0.8990 | 0.4440 | 0.8990 | 0.9481 |
| No log | 3.7241 | 216 | 0.8831 | 0.4437 | 0.8831 | 0.9397 |
| No log | 3.7586 | 218 | 0.8052 | 0.4932 | 0.8052 | 0.8973 |
| No log | 3.7931 | 220 | 0.7326 | 0.5307 | 0.7326 | 0.8559 |
| No log | 3.8276 | 222 | 0.7318 | 0.5175 | 0.7318 | 0.8554 |
| No log | 3.8621 | 224 | 0.7481 | 0.5179 | 0.7481 | 0.8649 |
| No log | 3.8966 | 226 | 0.7873 | 0.4952 | 0.7873 | 0.8873 |
| No log | 3.9310 | 228 | 0.8444 | 0.5283 | 0.8444 | 0.9189 |
| No log | 3.9655 | 230 | 0.8022 | 0.5050 | 0.8022 | 0.8957 |
| No log | 4.0 | 232 | 0.7555 | 0.5538 | 0.7555 | 0.8692 |
| No log | 4.0345 | 234 | 0.7607 | 0.4720 | 0.7607 | 0.8722 |
| No log | 4.0690 | 236 | 0.7491 | 0.4825 | 0.7491 | 0.8655 |
| No log | 4.1034 | 238 | 0.7593 | 0.4770 | 0.7593 | 0.8714 |
| No log | 4.1379 | 240 | 0.7853 | 0.5039 | 0.7853 | 0.8862 |
| No log | 4.1724 | 242 | 0.8399 | 0.4956 | 0.8399 | 0.9165 |
| No log | 4.2069 | 244 | 0.8527 | 0.4964 | 0.8527 | 0.9234 |
| No log | 4.2414 | 246 | 0.7971 | 0.5030 | 0.7971 | 0.8928 |
| No log | 4.2759 | 248 | 0.7739 | 0.5238 | 0.7739 | 0.8797 |
| No log | 4.3103 | 250 | 0.7266 | 0.5035 | 0.7266 | 0.8524 |
| No log | 4.3448 | 252 | 0.6761 | 0.5474 | 0.6761 | 0.8223 |
| No log | 4.3793 | 254 | 0.6659 | 0.4845 | 0.6659 | 0.8160 |
| No log | 4.4138 | 256 | 0.6473 | 0.5309 | 0.6473 | 0.8046 |
| No log | 4.4483 | 258 | 0.6495 | 0.5276 | 0.6495 | 0.8059 |
| No log | 4.4828 | 260 | 0.6990 | 0.4792 | 0.6990 | 0.8360 |
| No log | 4.5172 | 262 | 0.8176 | 0.3955 | 0.8176 | 0.9042 |
| No log | 4.5517 | 264 | 0.8536 | 0.3842 | 0.8536 | 0.9239 |
| No log | 4.5862 | 266 | 0.7907 | 0.4157 | 0.7907 | 0.8892 |
| No log | 4.6207 | 268 | 0.7100 | 0.5167 | 0.7100 | 0.8426 |
| No log | 4.6552 | 270 | 0.6943 | 0.5835 | 0.6943 | 0.8332 |
| No log | 4.6897 | 272 | 0.7118 | 0.5579 | 0.7118 | 0.8437 |
| No log | 4.7241 | 274 | 0.7219 | 0.5432 | 0.7219 | 0.8497 |
| No log | 4.7586 | 276 | 0.7161 | 0.5258 | 0.7161 | 0.8462 |
| No log | 4.7931 | 278 | 0.7223 | 0.5230 | 0.7223 | 0.8499 |
| No log | 4.8276 | 280 | 0.7253 | 0.5326 | 0.7253 | 0.8517 |
| No log | 4.8621 | 282 | 0.7508 | 0.5441 | 0.7508 | 0.8665 |
| No log | 4.8966 | 284 | 0.7341 | 0.5467 | 0.7341 | 0.8568 |
| No log | 4.9310 | 286 | 0.6883 | 0.5331 | 0.6883 | 0.8297 |
| No log | 4.9655 | 288 | 0.6588 | 0.5363 | 0.6588 | 0.8117 |
| No log | 5.0 | 290 | 0.6523 | 0.5060 | 0.6523 | 0.8076 |
| No log | 5.0345 | 292 | 0.6429 | 0.4964 | 0.6429 | 0.8018 |
| No log | 5.0690 | 294 | 0.6209 | 0.5347 | 0.6209 | 0.7880 |
| No log | 5.1034 | 296 | 0.6486 | 0.5585 | 0.6486 | 0.8054 |
| No log | 5.1379 | 298 | 0.6919 | 0.5422 | 0.6919 | 0.8318 |
| No log | 5.1724 | 300 | 0.6721 | 0.5540 | 0.6721 | 0.8198 |
| No log | 5.2069 | 302 | 0.6341 | 0.5792 | 0.6341 | 0.7963 |
| No log | 5.2414 | 304 | 0.6261 | 0.5773 | 0.6261 | 0.7913 |
| No log | 5.2759 | 306 | 0.6225 | 0.5532 | 0.6225 | 0.7890 |
| No log | 5.3103 | 308 | 0.6290 | 0.5836 | 0.6290 | 0.7931 |
| No log | 5.3448 | 310 | 0.6555 | 0.5709 | 0.6555 | 0.8096 |
| No log | 5.3793 | 312 | 0.7086 | 0.5597 | 0.7086 | 0.8418 |
| No log | 5.4138 | 314 | 0.7771 | 0.5684 | 0.7771 | 0.8816 |
| No log | 5.4483 | 316 | 0.7777 | 0.5584 | 0.7777 | 0.8819 |
| No log | 5.4828 | 318 | 0.7474 | 0.5665 | 0.7474 | 0.8645 |
| No log | 5.5172 | 320 | 0.7406 | 0.5038 | 0.7406 | 0.8606 |
| No log | 5.5517 | 322 | 0.7541 | 0.5127 | 0.7541 | 0.8684 |
| No log | 5.5862 | 324 | 0.7277 | 0.5206 | 0.7277 | 0.8530 |
| No log | 5.6207 | 326 | 0.7045 | 0.5738 | 0.7045 | 0.8393 |
| No log | 5.6552 | 328 | 0.7173 | 0.5689 | 0.7173 | 0.8469 |
| No log | 5.6897 | 330 | 0.7300 | 0.5572 | 0.7300 | 0.8544 |
| No log | 5.7241 | 332 | 0.7388 | 0.5572 | 0.7388 | 0.8596 |
| No log | 5.7586 | 334 | 0.6950 | 0.5616 | 0.6950 | 0.8337 |
| No log | 5.7931 | 336 | 0.6843 | 0.5763 | 0.6843 | 0.8272 |
| No log | 5.8276 | 338 | 0.6866 | 0.5873 | 0.6866 | 0.8286 |
| No log | 5.8621 | 340 | 0.6847 | 0.5718 | 0.6847 | 0.8275 |
| No log | 5.8966 | 342 | 0.6832 | 0.5718 | 0.6832 | 0.8266 |
| No log | 5.9310 | 344 | 0.6915 | 0.5658 | 0.6915 | 0.8316 |
| No log | 5.9655 | 346 | 0.7091 | 0.5539 | 0.7091 | 0.8421 |
| No log | 6.0 | 348 | 0.7347 | 0.5844 | 0.7347 | 0.8572 |
| No log | 6.0345 | 350 | 0.7484 | 0.5311 | 0.7484 | 0.8651 |
| No log | 6.0690 | 352 | 0.7356 | 0.5556 | 0.7356 | 0.8577 |
| No log | 6.1034 | 354 | 0.7465 | 0.5557 | 0.7465 | 0.8640 |
| No log | 6.1379 | 356 | 0.7484 | 0.5382 | 0.7484 | 0.8651 |
| No log | 6.1724 | 358 | 0.7838 | 0.4827 | 0.7838 | 0.8853 |
| No log | 6.2069 | 360 | 0.7797 | 0.4827 | 0.7797 | 0.8830 |
| No log | 6.2414 | 362 | 0.7835 | 0.4756 | 0.7835 | 0.8852 |
| No log | 6.2759 | 364 | 0.7653 | 0.4989 | 0.7653 | 0.8748 |
| No log | 6.3103 | 366 | 0.7123 | 0.5601 | 0.7123 | 0.8440 |
| No log | 6.3448 | 368 | 0.6934 | 0.5788 | 0.6934 | 0.8327 |
| No log | 6.3793 | 370 | 0.6906 | 0.5788 | 0.6906 | 0.8310 |
| No log | 6.4138 | 372 | 0.7079 | 0.5709 | 0.7079 | 0.8414 |
| No log | 6.4483 | 374 | 0.7140 | 0.5692 | 0.7140 | 0.8450 |
| No log | 6.4828 | 376 | 0.7309 | 0.5658 | 0.7309 | 0.8549 |
| No log | 6.5172 | 378 | 0.7363 | 0.5598 | 0.7363 | 0.8581 |
| No log | 6.5517 | 380 | 0.7307 | 0.5316 | 0.7307 | 0.8548 |
| No log | 6.5862 | 382 | 0.7290 | 0.5094 | 0.7290 | 0.8538 |
| No log | 6.6207 | 384 | 0.7152 | 0.5104 | 0.7152 | 0.8457 |
| No log | 6.6552 | 386 | 0.7018 | 0.5564 | 0.7018 | 0.8377 |
| No log | 6.6897 | 388 | 0.7003 | 0.5878 | 0.7003 | 0.8368 |
| No log | 6.7241 | 390 | 0.7232 | 0.5346 | 0.7232 | 0.8504 |
| No log | 6.7586 | 392 | 0.7484 | 0.5003 | 0.7484 | 0.8651 |
| No log | 6.7931 | 394 | 0.7365 | 0.5003 | 0.7365 | 0.8582 |
| No log | 6.8276 | 396 | 0.6832 | 0.5494 | 0.6832 | 0.8265 |
| No log | 6.8621 | 398 | 0.6388 | 0.5903 | 0.6388 | 0.7993 |
| No log | 6.8966 | 400 | 0.6394 | 0.5340 | 0.6394 | 0.7996 |
| No log | 6.9310 | 402 | 0.6765 | 0.4846 | 0.6765 | 0.8225 |
| No log | 6.9655 | 404 | 0.6868 | 0.4650 | 0.6868 | 0.8287 |
| No log | 7.0 | 406 | 0.6799 | 0.4819 | 0.6799 | 0.8246 |
| No log | 7.0345 | 408 | 0.6897 | 0.5551 | 0.6897 | 0.8305 |
| No log | 7.0690 | 410 | 0.7370 | 0.5796 | 0.7370 | 0.8585 |
| No log | 7.1034 | 412 | 0.7619 | 0.5353 | 0.7619 | 0.8728 |
| No log | 7.1379 | 414 | 0.7620 | 0.5353 | 0.7620 | 0.8729 |
| No log | 7.1724 | 416 | 0.7434 | 0.5658 | 0.7434 | 0.8622 |
| No log | 7.2069 | 418 | 0.7275 | 0.5838 | 0.7275 | 0.8529 |
| No log | 7.2414 | 420 | 0.7029 | 0.5566 | 0.7029 | 0.8384 |
| No log | 7.2759 | 422 | 0.6871 | 0.5157 | 0.6871 | 0.8289 |
| No log | 7.3103 | 424 | 0.6762 | 0.5644 | 0.6762 | 0.8223 |
| No log | 7.3448 | 426 | 0.6755 | 0.5898 | 0.6755 | 0.8219 |
| No log | 7.3793 | 428 | 0.6739 | 0.5835 | 0.6739 | 0.8209 |
| No log | 7.4138 | 430 | 0.6703 | 0.5898 | 0.6703 | 0.8187 |
| No log | 7.4483 | 432 | 0.6679 | 0.5898 | 0.6679 | 0.8172 |
| No log | 7.4828 | 434 | 0.6668 | 0.5727 | 0.6668 | 0.8166 |
| No log | 7.5172 | 436 | 0.6804 | 0.5097 | 0.6804 | 0.8249 |
| No log | 7.5517 | 438 | 0.7050 | 0.4973 | 0.7050 | 0.8396 |
| No log | 7.5862 | 440 | 0.7233 | 0.4589 | 0.7233 | 0.8505 |
| No log | 7.6207 | 442 | 0.7202 | 0.4806 | 0.7202 | 0.8486 |
| No log | 7.6552 | 444 | 0.7035 | 0.5214 | 0.7035 | 0.8387 |
| No log | 7.6897 | 446 | 0.6879 | 0.5672 | 0.6879 | 0.8294 |
| No log | 7.7241 | 448 | 0.6906 | 0.5970 | 0.6906 | 0.8310 |
| No log | 7.7586 | 450 | 0.6943 | 0.5585 | 0.6943 | 0.8332 |
| No log | 7.7931 | 452 | 0.6958 | 0.5705 | 0.6958 | 0.8341 |
| No log | 7.8276 | 454 | 0.6907 | 0.6025 | 0.6907 | 0.8311 |
| No log | 7.8621 | 456 | 0.6914 | 0.5535 | 0.6914 | 0.8315 |
| No log | 7.8966 | 458 | 0.6916 | 0.5430 | 0.6916 | 0.8316 |
| No log | 7.9310 | 460 | 0.6841 | 0.5427 | 0.6841 | 0.8271 |
| No log | 7.9655 | 462 | 0.6774 | 0.5736 | 0.6774 | 0.8230 |
| No log | 8.0 | 464 | 0.6771 | 0.5534 | 0.6771 | 0.8229 |
| No log | 8.0345 | 466 | 0.6808 | 0.5611 | 0.6808 | 0.8251 |
| No log | 8.0690 | 468 | 0.6748 | 0.5566 | 0.6748 | 0.8215 |
| No log | 8.1034 | 470 | 0.6737 | 0.5566 | 0.6737 | 0.8208 |
| No log | 8.1379 | 472 | 0.6750 | 0.5582 | 0.6750 | 0.8216 |
| No log | 8.1724 | 474 | 0.6801 | 0.5827 | 0.6801 | 0.8247 |
| No log | 8.2069 | 476 | 0.6858 | 0.5827 | 0.6858 | 0.8281 |
| No log | 8.2414 | 478 | 0.6923 | 0.5569 | 0.6923 | 0.8321 |
| No log | 8.2759 | 480 | 0.6926 | 0.5827 | 0.6926 | 0.8322 |
| No log | 8.3103 | 482 | 0.6892 | 0.5827 | 0.6892 | 0.8302 |
| No log | 8.3448 | 484 | 0.6894 | 0.5521 | 0.6894 | 0.8303 |
| No log | 8.3793 | 486 | 0.6862 | 0.5628 | 0.6862 | 0.8284 |
| No log | 8.4138 | 488 | 0.6878 | 0.5358 | 0.6878 | 0.8293 |
| No log | 8.4483 | 490 | 0.6928 | 0.5023 | 0.6928 | 0.8324 |
| No log | 8.4828 | 492 | 0.6861 | 0.5033 | 0.6861 | 0.8283 |
| No log | 8.5172 | 494 | 0.6859 | 0.5023 | 0.6859 | 0.8282 |
| No log | 8.5517 | 496 | 0.6781 | 0.5033 | 0.6781 | 0.8235 |
| No log | 8.5862 | 498 | 0.6756 | 0.5267 | 0.6756 | 0.8219 |
| 0.3804 | 8.6207 | 500 | 0.6758 | 0.5295 | 0.6758 | 0.8221 |
| 0.3804 | 8.6552 | 502 | 0.6765 | 0.5644 | 0.6765 | 0.8225 |
| 0.3804 | 8.6897 | 504 | 0.6803 | 0.5582 | 0.6803 | 0.8248 |
| 0.3804 | 8.7241 | 506 | 0.6835 | 0.5733 | 0.6835 | 0.8268 |
| 0.3804 | 8.7586 | 508 | 0.6817 | 0.5736 | 0.6817 | 0.8256 |
| 0.3804 | 8.7931 | 510 | 0.6809 | 0.5733 | 0.6809 | 0.8252 |
| 0.3804 | 8.8276 | 512 | 0.6858 | 0.5690 | 0.6858 | 0.8281 |
| 0.3804 | 8.8621 | 514 | 0.6914 | 0.5629 | 0.6914 | 0.8315 |
| 0.3804 | 8.8966 | 516 | 0.6940 | 0.5629 | 0.6940 | 0.8331 |
| 0.3804 | 8.9310 | 518 | 0.6891 | 0.5690 | 0.6891 | 0.8301 |
| 0.3804 | 8.9655 | 520 | 0.6857 | 0.5690 | 0.6857 | 0.8281 |
| 0.3804 | 9.0 | 522 | 0.6803 | 0.5736 | 0.6803 | 0.8248 |
| 0.3804 | 9.0345 | 524 | 0.6787 | 0.5566 | 0.6787 | 0.8238 |
| 0.3804 | 9.0690 | 526 | 0.6806 | 0.5566 | 0.6806 | 0.8250 |
| 0.3804 | 9.1034 | 528 | 0.6816 | 0.5566 | 0.6816 | 0.8256 |
| 0.3804 | 9.1379 | 530 | 0.6854 | 0.5566 | 0.6854 | 0.8279 |
| 0.3804 | 9.1724 | 532 | 0.6858 | 0.5566 | 0.6858 | 0.8282 |
| 0.3804 | 9.2069 | 534 | 0.6857 | 0.5566 | 0.6857 | 0.8281 |
| 0.3804 | 9.2414 | 536 | 0.6857 | 0.5551 | 0.6857 | 0.8281 |
| 0.3804 | 9.2759 | 538 | 0.6877 | 0.5551 | 0.6877 | 0.8293 |
| 0.3804 | 9.3103 | 540 | 0.6894 | 0.5551 | 0.6894 | 0.8303 |
| 0.3804 | 9.3448 | 542 | 0.6933 | 0.5360 | 0.6933 | 0.8326 |
| 0.3804 | 9.3793 | 544 | 0.6966 | 0.5360 | 0.6966 | 0.8347 |
| 0.3804 | 9.4138 | 546 | 0.6997 | 0.5551 | 0.6997 | 0.8365 |
| 0.3804 | 9.4483 | 548 | 0.7023 | 0.5535 | 0.7023 | 0.8380 |
| 0.3804 | 9.4828 | 550 | 0.7047 | 0.5579 | 0.7047 | 0.8395 |
| 0.3804 | 9.5172 | 552 | 0.7081 | 0.5358 | 0.7081 | 0.8415 |
| 0.3804 | 9.5517 | 554 | 0.7128 | 0.5416 | 0.7128 | 0.8443 |
| 0.3804 | 9.5862 | 556 | 0.7184 | 0.5403 | 0.7184 | 0.8476 |
| 0.3804 | 9.6207 | 558 | 0.7242 | 0.5390 | 0.7242 | 0.8510 |
| 0.3804 | 9.6552 | 560 | 0.7280 | 0.5390 | 0.7280 | 0.8532 |
| 0.3804 | 9.6897 | 562 | 0.7313 | 0.5390 | 0.7313 | 0.8552 |
| 0.3804 | 9.7241 | 564 | 0.7339 | 0.5390 | 0.7339 | 0.8567 |
| 0.3804 | 9.7586 | 566 | 0.7364 | 0.5390 | 0.7364 | 0.8581 |
| 0.3804 | 9.7931 | 568 | 0.7368 | 0.5336 | 0.7368 | 0.8584 |
| 0.3804 | 9.8276 | 570 | 0.7378 | 0.5336 | 0.7378 | 0.8590 |
| 0.3804 | 9.8621 | 572 | 0.7378 | 0.5336 | 0.7378 | 0.8590 |
| 0.3804 | 9.8966 | 574 | 0.7375 | 0.5292 | 0.7375 | 0.8588 |
| 0.3804 | 9.9310 | 576 | 0.7371 | 0.5296 | 0.7371 | 0.8586 |
| 0.3804 | 9.9655 | 578 | 0.7368 | 0.5296 | 0.7368 | 0.8584 |
| 0.3804 | 10.0 | 580 | 0.7367 | 0.5296 | 0.7367 | 0.8583 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
vicgalle/NeuralBeagle-11B | vicgalle | "2024-03-04T12:11:40Z" | 52 | 6 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"dpo",
"11B",
"merge",
"conversational",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"base_model:vicgalle/franken-Beagle-11B",
"base_model:finetune:vicgalle/franken-Beagle-11B",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-01-20T10:01:58Z" | ---
license: apache-2.0
tags:
- dpo
- 11B
- merge
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
base_model:
- vicgalle/franken-Beagle-11B
model-index:
- name: NeuralBeagle-11B
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: 73.29
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/NeuralBeagle-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: 87.61
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/NeuralBeagle-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: 63.8
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/NeuralBeagle-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: 71.36
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/NeuralBeagle-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: 82.64
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/NeuralBeagle-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: 58.98
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=vicgalle/NeuralBeagle-11B
name: Open LLM Leaderboard
---
# NeuralBeagle-11B
DPO'd from vicgalle/franken-Beagle-11B, a Beagle-like model upscaled to 11B.
It is a frankenmerge model created using mergekit. Then, we applied DPO over a high-quality preference dataset.
## Sample generations
**Generate a movie review about the Pokémon 1 movie, focusing on BDSM.**
> Before delving into the realm of BDSOMatic interpretations within Pokémon: The First Movie (aka "Pokémon: Mewtwo's Revenge" in Japan), it is important to acknowledge that this animated feature is primarily intended for a younger audience. It follows the adventures of Ash Ketchum, Pikachu, and their friends as they encounter an artificially created Pokémon named Mewtwo. While certain aspects of the storyline and visuals could be stretched to include various subtexts or symbolism, our analysis will focus on a more playful and metaphorical exploration of BDSM concepts within this film's context.
Participants: Warning: The following review may contain light-hearted and imaginative interpretations that are not intended as serious critical analysis and should not be taken literally.
Within the Pokémon universe, Ash Ketchum and his loyal Pikachu find themselves thrust into a high-stakes scenario reminiscent of a BDSM "Domestic Discipline" (DD) fantasy. As Mewtwo, a genetically modified Pokémon with immense power and intellect, seeks vengeance against the scientists who created him as a tool for battle entertainment, he operates as an archetypal "Dominant" figure.
Mewtwo, born from scientific manipulation, is an embodiment of control, both over his own creation and his obedient army of Pokémon. He exercises mastery over his subjects, mirroring the role of a "Dom," or Dominant, in a BDSM dynamic.
Ash Ketchum, initially unaware of the manipulations that have brought him into this situation, can be seen as a submissive protagonist. His humility and naivety parallel those within the DD community who seek guidance from an experienced Dominant to help them understand and embrace their desires for submission.
In his encounter with Mewtwo, Ash is subjected to tests designed to exhibit his strength, loyalty, and unwavering determination - elements crucial in establishing trust between a submissive and a Dominant within a BDSM dynamic. This psychological battle symbolizes the process of earning a "sub's" consent and trust through displays of control and power.
## Prompt format
The template is included in tokenizer_config.json.
# [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_vicgalle__NeuralBeagle-11B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |72.95|
|AI2 Reasoning Challenge (25-Shot)|73.29|
|HellaSwag (10-Shot) |87.61|
|MMLU (5-Shot) |63.80|
|TruthfulQA (0-shot) |71.36|
|Winogrande (5-shot) |82.64|
|GSM8k (5-shot) |58.98|
|
tareshh18/legal-temp | tareshh18 | "2025-03-16T06:57:14Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-16T06:39:20Z" | ---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** tareshh18
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 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)
|
sriyaseshadri/gemma-essay-finetune | sriyaseshadri | "2024-04-30T17:22:46Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-30T16:13:03Z" | ---
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.
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[More Information Needed]
## Bias, Risks, and Limitations
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[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
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- 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
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[More Information Needed]
#### Metrics
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
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nathanialhunt2000/60a2472d-de0c-4b7f-9119-d23ed5ce963e | nathanialhunt2000 | "2025-03-09T04:52:47Z" | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct",
"region:us"
] | null | "2025-03-09T04:52:32Z" | ---
library_name: peft
tags:
- generated_from_trainer
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
model-index:
- name: nathanialhunt2000/60a2472d-de0c-4b7f-9119-d23ed5ce963e
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. -->
# nathanialhunt2000/60a2472d-de0c-4b7f-9119-d23ed5ce963e
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1023
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
coffiee/dl200 | coffiee | "2025-02-17T03:35:21Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-17T03:34:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[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] |
AppyFizz/caluepicr | AppyFizz | "2024-10-27T11:02:14Z" | 6 | 0 | diffusers | [
"diffusers",
"autotrain",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:emilianJR/epiCRealism",
"base_model:adapter:emilianJR/epiCRealism",
"license:openrail++",
"region:us"
] | text-to-image | "2024-10-27T11:02:12Z" |
---
tags:
- autotrain
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: emilianJR/epiCRealism
instance_prompt: photo of caluepicr
license: openrail++
---
# AutoTrain LoRA DreamBooth - AppyFizz/caluepicr
These are LoRA adaption weights for emilianJR/epiCRealism. The weights were trained on photo of caluepicr using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: True.
|
roncmic/distilbert-base-uncased-finetuned-ner | roncmic | "2024-10-10T12:35:52Z" | 127 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2024-10-10T12:31:20Z" | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9258481600176816
- name: Recall
type: recall
value: 0.9372413021590782
- name: F1
type: f1
value: 0.9315098954858795
- name: Accuracy
type: accuracy
value: 0.983668800737128
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0606
- Precision: 0.9258
- Recall: 0.9372
- F1: 0.9315
- Accuracy: 0.9837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2412 | 1.0 | 878 | 0.0686 | 0.9041 | 0.9249 | 0.9144 | 0.9803 |
| 0.0519 | 2.0 | 1756 | 0.0596 | 0.9236 | 0.9339 | 0.9287 | 0.9831 |
| 0.0298 | 3.0 | 2634 | 0.0606 | 0.9258 | 0.9372 | 0.9315 | 0.9837 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1
- Datasets 2.18.0
- Tokenizers 0.20.0
|
AltamashAhmed/distilHuBERT-finetuned-gtzan | AltamashAhmed | "2023-09-14T23:06:13Z" | 169 | 0 | transformers | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | "2023-08-28T19:31:13Z" | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: DistilHuBERT-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.85
---
<!-- 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. -->
# DistilHuBERT-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5657
- Accuracy: 0.85
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9277 | 1.0 | 113 | 1.7812 | 0.41 |
| 1.3526 | 2.0 | 226 | 1.1616 | 0.67 |
| 1.043 | 3.0 | 339 | 0.8962 | 0.78 |
| 0.8494 | 4.0 | 452 | 0.8234 | 0.76 |
| 0.5931 | 5.0 | 565 | 0.7085 | 0.78 |
| 0.3861 | 6.0 | 678 | 0.6669 | 0.8 |
| 0.4828 | 7.0 | 791 | 0.5674 | 0.86 |
| 0.2302 | 8.0 | 904 | 0.5657 | 0.85 |
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
junn991/gemma2-2b-it-sft-couple | junn991 | "2024-11-21T14:55:01Z" | 60 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-21T14:52:10Z" | ---
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] |
wavespeed/Wan2.1-I2V-14B-720P-Diffusers-fp16 | wavespeed | "2025-03-30T07:48:19Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"diffusers:WanImageToVideoPipeline",
"region:us"
] | null | "2025-03-30T05:36:16Z" | <!DOCTYPE html>
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<head>
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mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF | mradermacher | "2024-12-02T22:00:10Z" | 65 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:win10/EVA-Instruct-QwQ-32B-Preview",
"base_model:quantized:win10/EVA-Instruct-QwQ-32B-Preview",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2024-12-01T20:37:42Z" | ---
base_model: win10/EVA-Instruct-QwQ-32B-Preview
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: nicoboss -->
weighted/imatrix quants of https://huggingface.co/win10/EVA-Instruct-QwQ-32B-Preview
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-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/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Instruct-QwQ-32B-Preview-i1-GGUF/resolve/main/EVA-Instruct-QwQ-32B-Preview.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | practically like static Q6_K |
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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
nl-to-logql/gemma-2-logql | nl-to-logql | "2024-11-13T03:59:48Z" | 51 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"generated_from_trainer",
"base_model:google/gemma-2-9b",
"base_model:adapter:google/gemma-2-9b",
"license:gemma",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2024-11-13T03:45:50Z" | ---
base_model: google/gemma-2-9b
library_name: peft
license: gemma
tags:
- generated_from_trainer
model-index:
- name: lora-out
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: google/gemma-2-9b
sequence_len: 1024
# base model weight quantization
load_in_8bit: true
# load_in_4bit: true
# attention implementation
flash_attention: true
# finetuned adapter config
adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_modules_to_save: # required when adding new tokens to LLaMA/Mistral
- embed_tokens
- lm_head
# if training fails, uncomment above
# for details, see https://github.com/huggingface/peft/issues/334#issuecomment-1561727994
###
# Dataset Configuration: sqlqa
###
# datasets:
# - path: data.jsonl
# type: alpaca
datasets:
- path: public_train_data.jsonl
ds_type: json
type:
field_instruction: instruction
field_input: input
field_output: output
format: |-
[INST] {instruction}
{input} [/INST]
chat_template: gemma
tokens:
- "[INST]"
- " [/INST]"
- "[QL]"
- " [/QL]"
- "[EXPLANATION]"
- " [/EXPLANATION]"
# dataset formatting config
special_tokens:
pad_token: <|end_of_text|>
val_set_size: 0.05
###
# Training Configuration
###
# masks the input messages so that the model learns and understands the language w/o being reliant on the input
train_on_inputs: false
# random seed for better reproducibility
seed: 117
# optimizer config
optimizer: adamw_bnb_8bit
learning_rate: 0.0001
lr_scheduler: cosine
num_epochs: 4
micro_batch_size: 4
gradient_accumulation_steps: 1
warmup_steps: 10
# axolotl saving config
dataset_prepared_path: last_run_prepared
output_dir: ./lora-out
# logging and eval config
logging_steps: 1
eval_steps: 0.05
# training performance optimization config
bf16: auto
tf32: false
gradient_checkpointing: true
###
# Miscellaneous Configuration
###
# when true, prevents over-writing the config from the CLI
strict: false
# "Don't mess with this, it's here for accelerate and torchrun" -- axolotl docs
local_rank:
# WANDB
wandb_mode:
wandb_project:
wandb_watch:
wandb_name:
wandb_run_id:
# Multi-GPU
# deepspeed: /root/axolotl/deepspeed_configs/zero3_bf16.json
# deepspeed: zero3_bf16.json
# deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
deepspeed:
fsdp:
fsdp_config:
```
</details><br>
# lora-out
This model is a fine-tuned version of [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0077
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 117
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7925 | 0.0385 | 1 | 2.0412 |
| 1.6872 | 0.2308 | 6 | 1.6089 |
| 0.6967 | 0.4615 | 12 | 0.6328 |
| 0.3327 | 0.6923 | 18 | 0.2711 |
| 0.1784 | 0.9231 | 24 | 0.1733 |
| 0.1136 | 1.1538 | 30 | 0.1190 |
| 0.0891 | 1.3846 | 36 | 0.0850 |
| 0.0746 | 1.6154 | 42 | 0.0626 |
| 0.0522 | 1.8462 | 48 | 0.0465 |
| 0.033 | 2.0769 | 54 | 0.0282 |
| 0.0333 | 2.3077 | 60 | 0.0225 |
| 0.0171 | 2.5385 | 66 | 0.0203 |
| 0.0172 | 2.7692 | 72 | 0.0144 |
| 0.0095 | 3.0 | 78 | 0.0119 |
| 0.0088 | 3.2308 | 84 | 0.0099 |
| 0.0054 | 3.4615 | 90 | 0.0089 |
| 0.0073 | 3.6923 | 96 | 0.0085 |
| 0.0059 | 3.9231 | 102 | 0.0077 |
### Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0 |
HZ0504/kajimi | HZ0504 | "2023-08-23T09:15:37Z" | 1 | 2 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | "2023-08-23T08:20:20Z" |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of a kajimi
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
gian-cr/q-FrozenLake-v1-4x4-noSlippery | gian-cr | "2023-03-07T16:55:37Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-02-28T18:17:57Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="gian-cr/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
TOMFORD79/HI_VO1_H04 | TOMFORD79 | "2025-03-12T18:04:37Z" | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | "2025-03-12T17:57:56Z" | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
RedRayz/hikari_noob_v-pred_0.6 | RedRayz | "2024-11-20T10:22:48Z" | 10 | 3 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"sdxl",
"anime",
"text-to-image",
"en",
"base_model:RedRayz/hikari_noob_v-pred_0.5",
"base_model:finetune:RedRayz/hikari_noob_v-pred_0.5",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2024-11-14T15:48:16Z" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
base_model:
- RedRayz/hikari_noob_v-pred_0.5
language:
- en
tags:
- stable-diffusion
- sdxl
- anime
pipeline_tag: text-to-image
---
# Hikari Noob v-pred 0.6
Civitai model page: https://civitai.com/models/938672
Fine-tuned NoobAI-XL(ν-prediction) and merged SPO LoRA
NoobAI-XL(ν-prediction)をファインチューンし、SPOをマージしました。
## Features/特徴
- Improved stability and quality.
- Works with samplers other than Euler.
- Good results with only 10 steps (12 steps or more recommended)
- Fixed a problem in which the quality of output was significantly degraded when the number of tokens exceeded 76.
- The base style is not strong and can be restyled by prompts or LoRAs.
- This model does not include any base model other than NoobAI (ν-prediction 0.5), so it has the equivalent knowledge.
You can generate characters that have appeared by August 2024.
- 安定性と品質を改善
- わずか10ステップでよい結果を得られます(ただし12ステップ以上を推奨)
- Zero Terminal SNRの代わりにNoise Offsetを使用することでEuler以外のサンプラーでも利用できるようにしました。
- トークン数が76を超えると出力の品質が著しく低下する問題を修正しました。
- 素の画風は強くないので、プロンプトやLoRAによる画風変更ができます。
- このモデルはNoobAI(ν-prediction 0.5)以外のベースモデルを一切含まず、それと同等の知識があります。2024年8月までに登場したキャラクターを生成できます。
## About v0.6
- v0.5 was too flat, so we improved the quality of detail rendering and made outline thinner!
- Improved the quality of the landscapes just a little bit
- Reduced skin color overshoot?
- v0.5は絵柄が平坦すぎたのでディティールを増やしてアウトラインを細くしました。
- ほんの少しだけ風景画の品質が改善
- 肌の色のオーバーシュート(白飛び)を軽減?
## Requirements / 動作要件
- AUTOMATIC1111 WebUI on `dev` branch / devブランチ上のAUTOMATIC1111 WebUI
- **Latest version** of ComfyUI / **最新版**のComfyUI
- **Latest version** of Forge or reForge / **最新版**のForgeまたはreForge
### Instruction for AUTOMATIC1111 / AUTOMATIC1111の導入手順
1. Switch branch to `dev` (Run this command in the root directory of the webui: `git checkout -b dev origin/dev` or use Github Desktop)
2. Use the model as usual!
(日本語)
1. `dev`ブランチに切り替えます(次のコマンドをwebui直下で実行します: `git checkout -b dev origin/dev` またはGithub Desktopを使う)
2. 通常通りモデルを使用します。
## Prompt Guidelines / プロンプト記法
Almost same as the base model/ベースモデルとおおむね同じ
To improve the quality of background, add `simple background, transparent background` to Negative Prompt.
## Recommended Prompt / 推奨プロンプト
Positive: None/無し(Works good without `masterpiece, best quality` / `masterpiece, best quality`無しでおｋ)
Negative: `worst quality, low quality, bad quality, lowres, jpeg artifacts, unfinished, photoshop \(medium\), abstract` or empty(または無し)
## Recommended Settings / 推奨設定
Steps: 8-24
Sampler: DPM++ 2M(dpmpp_2m)
Scheduler: Simple
Guidance Scale: 2-7
### Hires.fix
Hires upscaler: 4x-UltraSharp or Latent(nearest-exact)
Denoising strength: 0.4-0.5(0.65-0.7 for latent)
## Merge recipe(Weighted sum)
- Stage 1: Finetune Hikari Noob v-pred 0.5 and merge(see below)
*A,B: Hikari Noob v-pred 0.5 based custom checkpoint
- v0.5(NoSPO) * 0.75 + A * 0.25 = tmp1
- tmp0 * 0.75 + B * 0.25 = tmp2
- tmp2 + SPO LoRA * 1 + sdxl-flat * -0.25 + sdxl-boldline * -1 = tmp3
- Adjust tmp3(0.2,0.2,0.2,0.1,0,0,0,0) = Result
## Training scripts:
[sd-scripts](https://github.com/kohya-ss/sd-scripts)
## Notice
This model is licensed under [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)
If you make modify this model, you must share both your changes and the original license.
You are prohibited from monetizing any close-sourced fine-tuned / merged model, which disallows the public from accessing the model's source code / weights and its usages.
Do not reupload this model. |
Mekuu/LLAMA3.1-8b-Counsel-v1.0 | Mekuu | "2025-04-01T08:52:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-01T08:47:10Z" | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Mekuu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
novelmartis/blt_vs_model | novelmartis | "2025-02-26T08:22:08Z" | 0 | 1 | null | [
"license:mit",
"region:us"
] | null | "2024-11-26T20:31:27Z" | ---
license: mit
---
Two versions of the BLT-VS model (see: https://github.com/KietzmannLab/BLT-VS) are available.
Model 1: blt_vs_slt_111_biounroll_1_t_12_readout_multi_dataset_ecoset_num_1 (trained on Imagenet-1k; ILSVRC)
timesteps=12,
num_classes=1000,
add_feats=100,
lateral_connections=True,
topdown_connections=True,
skip_connections=True,
bio_unroll=True,
image_size=224,
hook_type='None',
readout_type='multi'
Model 2: blt_vs_slt_111_biounroll_0_t_6_readout_multi_dataset_imagenet_num_1 (trained on Ecoset-565 - https://huggingface.co/datasets/kietzmannlab/ecoset)
timesteps=6,
num_classes=565,
add_feats=100,
lateral_connections=True,
topdown_connections=True,
skip_connections=True,
bio_unroll=False,
image_size=224,
hook_type='None',
readout_type='multi'
Additionally, the b_vs, vNet, and rn50 trained models are also available (see https://github.com/KietzmannLab/BLT-VS/tree/main/blt_vs_model/training_code/models for model definitions). |
sakusakumura/Qwen2-7b-cleanup-short-prompt | sakusakumura | "2024-06-10T03:41:54Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2-7B",
"base_model:finetune:unsloth/Qwen2-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-10T03:37:40Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
base_model: unsloth/Qwen2-7B
---
# Uploaded model
- **Developed by:** sakusakumura
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2-7B
This qwen2 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)
|
isspek/roberta-base_zika_chatgpt_1_2e-5_16_undersampling_0.5 | isspek | "2024-12-04T12:43:42Z" | 198 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-04T12:43:23Z" | ---
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/EVA-Qwen2.5-14B-v0.1-GGUF | mradermacher | "2024-10-11T18:51:02Z" | 154 | 2 | transformers | [
"transformers",
"gguf",
"en",
"dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal",
"dataset:Nopm/Opus_WritingStruct",
"dataset:Gryphe/Sonnet3.5-SlimOrcaDedupCleaned",
"dataset:Gryphe/Sonnet3.5-Charcard-Roleplay",
"dataset:Gryphe/ChatGPT-4o-Writing-Prompts",
"dataset:Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned",
"dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned",
"dataset:nothingiisreal/Reddit-Dirty-And-WritingPrompts",
"dataset:allura-org/Celeste-1.x-data-mixture",
"dataset:allura-org/shortstories_synthlabels",
"base_model:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1",
"base_model:quantized:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-10-07T12:57:56Z" | ---
base_model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1
datasets:
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- Nopm/Opus_WritingStruct
- Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
- Gryphe/Sonnet3.5-Charcard-Roleplay
- Gryphe/ChatGPT-4o-Writing-Prompts
- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- nothingiisreal/Reddit-Dirty-And-WritingPrompts
- allura-org/Celeste-1.x-data-mixture
- allura-org/shortstories_synthlabels
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-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/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.IQ3_XS.gguf) | IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.IQ3_S.gguf) | IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.IQ3_M.gguf) | IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/EVA-Qwen2.5-14B-v0.1-GGUF/resolve/main/EVA-Qwen2.5-14B-v0.1.Q8_0.gguf) | Q8_0 | 15.8 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Ebitendon/ppo-LunarLander-v2 | Ebitendon | "2023-09-04T03:40:36Z" | 5 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-09-03T11:06:59Z" | ---
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: 253.19 +/- 65.19
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
...
```
|
mradermacher/merging_LLM-GGUF | mradermacher | "2025-02-14T04:32:06Z" | 88 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:MatteoKhan/merging_LLM",
"base_model:quantized:MatteoKhan/merging_LLM",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-01-03T03:35:12Z" | ---
base_model: MatteoKhan/merging_LLM
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/MatteoKhan/merging_LLM
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/merging_LLM-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/merging_LLM-GGUF/resolve/main/merging_LLM.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/merging_LLM-GGUF/resolve/main/merging_LLM.f16.gguf) | f16 | 3.2 | 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 -->
|
atasoglu/mbert-base-cased-nli-stsb-tr | atasoglu | "2024-04-20T18:49:12Z" | 23 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"tr",
"dataset:nli_tr",
"dataset:emrecan/stsb-mt-turkish",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2024-04-20T18:44:50Z" | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: apache-2.0
datasets:
- nli_tr
- emrecan/stsb-mt-turkish
language:
- tr
---
# atasoglu/mbert-base-cased-nli-stsb-tr
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
This model was adapted from [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) and fine-tuned on these datasets:
- [nli_tr](https://huggingface.co/datasets/nli_tr)
- [emrecan/stsb-mt-turkish](https://huggingface.co/datasets/emrecan/stsb-mt-turkish)
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('atasoglu/mbert-base-cased-nli-stsb-tr')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('atasoglu/mbert-base-cased-nli-stsb-tr')
model = AutoModel.from_pretrained('atasoglu/mbert-base-cased-nli-stsb-tr')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
Achieved results on the [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) test split are given below:
```txt
Cosine-Similarity : Pearson: 0.8152 Spearman: 0.8130
Manhattan-Distance: Pearson: 0.8049 Spearman: 0.8128
Euclidean-Distance: Pearson: 0.8049 Spearman: 0.8126
Dot-Product-Similarity: Pearson: 0.7878 Spearman: 0.7822
```
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 180 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 18,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 108,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
tcapelle/toxicity-scorer-smollm2-360m-freeze | tcapelle | "2024-11-28T12:59:01Z" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"generated_from_trainer",
"base_model:HuggingFaceTB/SmolLM2-360M",
"base_model:finetune:HuggingFaceTB/SmolLM2-360M",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-27T21:39:57Z" | ---
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-360M
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: toxicity-scorer-smollm2-360m-freeze
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. -->
# toxicity-scorer-smollm2-360m-freeze
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7434
- F1: 0.6049
- Accuracy: 0.5261
- Precision: 0.7390
- Recall: 0.5261
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 44
- eval_batch_size: 44
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 352
- total_eval_batch_size: 352
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|:-------------:|:------:|:----:|:---------------:|:------:|:--------:|:---------:|:------:|
| No log | 0 | 0 | 0.7481 | 0.6025 | 0.5231 | 0.7383 | 0.5231 |
| 0.7489 | 1.5277 | 5000 | 0.7434 | 0.6049 | 0.5261 | 0.7390 | 0.5261 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
SunilNiloor/q-Taxi-v3-rgb_array | SunilNiloor | "2023-08-27T15:08:34Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-08-27T15:08:33Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-rgb_array
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
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="SunilNiloor/q-Taxi-v3-rgb_array", 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"])
```
|
mradermacher/Multimash3-12B-slerp-GGUF | mradermacher | "2024-05-22T19:23:25Z" | 4 | 0 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"allknowingroger/Multimerge-12B-MoE",
"TomGrc/FusionNet_7Bx2_MoE_v0.1",
"en",
"base_model:allknowingroger/Multimash3-12B-slerp",
"base_model:quantized:allknowingroger/Multimash3-12B-slerp",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-05-22T18:37:58Z" | ---
base_model: allknowingroger/Multimash3-12B-slerp
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- allknowingroger/Multimerge-12B-MoE
- TomGrc/FusionNet_7Bx2_MoE_v0.1
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/allknowingroger/Multimash3-12B-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/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q3_K_S.gguf) | Q3_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ3_M.gguf) | IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q3_K_L.gguf) | Q3_K_L | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q5_K_S.gguf) | Q5_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q5_K_M.gguf) | Q5_K_M | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-slerp.Q6_K.gguf) | Q6_K | 10.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Multimash3-12B-slerp-GGUF/resolve/main/Multimash3-12B-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 -->
|
cleanrl/Riverraid-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3 | cleanrl | "2023-02-10T13:15:36Z" | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"Riverraid-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-02-10T13:15:31Z" | ---
tags:
- Riverraid-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Riverraid-v5
type: Riverraid-v5
metrics:
- type: mean_reward
value: 31214.00 +/- 3211.31
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **Riverraid-v5**
This is a trained model of a PPO agent playing Riverraid-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/sebulba_ppo_envpool_impala_atari_wrapper.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name sebulba_ppo_envpool_impala_atari_wrapper --env-id Riverraid-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/sebulba_ppo_envpool_impala_atari_wrapper.py
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/Riverraid-v5-sebulba_ppo_envpool_impala_atari_wrapper-seed3/raw/main/poetry.lock
poetry install --all-extras
python sebulba_ppo_envpool_impala_atari_wrapper.py --actor-device-ids 0 --learner-device-ids 1 2 3 4 5 6 --track --save-model --upload-model --hf-entity cleanrl --env-id Riverraid-v5 --seed 3
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 7680,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'Riverraid-v5',
'exp_name': 'sebulba_ppo_envpool_impala_atari_wrapper',
'gae_lambda': 0.95,
'gamma': 0.99,
'hf_entity': 'cleanrl',
'learner_device_ids': [1, 2, 3, 4, 5, 6],
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'minibatch_size': 1920,
'norm_adv': True,
'num_actor_threads': 1,
'num_envs': 60,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 6510,
'profile': False,
'save_model': True,
'seed': 3,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
nttx/9ab10f77-de16-4060-8365-3e4839cf5078 | nttx | "2025-01-24T21:02:03Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:llama3",
"region:us"
] | null | "2025-01-24T20:03:16Z" | ---
library_name: peft
license: llama3
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9ab10f77-de16-4060-8365-3e4839cf5078
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- c2aac3d186ebb63e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c2aac3d186ebb63e_train_data.json
type:
field_input: label
field_instruction: abstract
field_output: title
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/9ab10f77-de16-4060-8365-3e4839cf5078
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/c2aac3d186ebb63e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 2b5a20e0-77e3-4a95-907e-822cd4e60b86
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2b5a20e0-77e3-4a95-907e-822cd4e60b86
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 9ab10f77-de16-4060-8365-3e4839cf5078
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.7416 | 0.0003 | 1 | 3.3045 |
| 1.2416 | 0.0169 | 50 | 1.3204 |
| 1.4263 | 0.0337 | 100 | 1.2893 |
| 1.3587 | 0.0506 | 150 | 1.2685 |
| 1.1875 | 0.0674 | 200 | 1.2623 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
yassin99/rubertForRussian | yassin99 | "2023-01-11T14:56:00Z" | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2023-01-11T13:44:47Z" | ---
language:
- ru
---
# rubert-base-cased
RuBERT \(Russian, cased, 12‑layer, 768‑hidden, 12‑heads, 180M parameters\) was trained on the Russian part of Wikipedia and news data. We used this training data to build a vocabulary of Russian subtokens and took a multilingual version of BERT‑base as an initialization for RuBERT\[1\].
08.11.2021: upload model with MLM and NSP heads
\[1\]: Kuratov, Y., Arkhipov, M. \(2019\). Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language. arXiv preprint [arXiv:1905.07213](https://arxiv.org/abs/1905.07213).
|
therealchefdave/slumber-7b | therealchefdave | "2024-03-14T00:26:52Z" | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2203.05482",
"base_model:princeton-nlp/SWE-Llama-7b",
"base_model:merge:princeton-nlp/SWE-Llama-7b",
"base_model:togethercomputer/LLaMA-2-7B-32K",
"base_model:merge:togethercomputer/LLaMA-2-7B-32K",
"base_model:vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy",
"base_model:merge:vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-13T23:52:26Z" | ---
base_model:
- princeton-nlp/SWE-Llama-7b
- vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy
- togethercomputer/LLaMA-2-7B-32K
library_name: transformers
tags:
- mergekit
- merge
---
# dave
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [princeton-nlp/SWE-Llama-7b](https://huggingface.co/princeton-nlp/SWE-Llama-7b)
* [vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy](https://huggingface.co/vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy)
* [togethercomputer/LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: togethercomputer/LLaMA-2-7B-32K
parameters:
weight: 1.0
- model: vibhorag101/llama-2-7b-chat-hf-phr_mental_therapy
parameters:
weight: 0.3
- model: princeton-nlp/SWE-Llama-7b
parameters:
weight: 0.5
merge_method: linear
dtype: float16
```
|
Alphatao/b35eda51-b72a-4fa3-8e0f-cf291990bf61 | Alphatao | "2025-03-27T23:21:59Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.6",
"license:apache-2.0",
"region:us"
] | null | "2025-03-27T20:29:11Z" | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b35eda51-b72a-4fa3-8e0f-cf291990bf61
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: TinyLlama/TinyLlama-1.1B-Chat-v0.6
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a9d4324f1e33f9ae_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a9d4324f1e33f9ae_train_data.json
type:
field_instruction: init_prompt
field_output: init_response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
device_map:
? ''
: 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 400
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/b35eda51-b72a-4fa3-8e0f-cf291990bf61
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- down_proj
- up_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 7785
micro_batch_size: 2
mlflow_experiment_name: /tmp/a9d4324f1e33f9ae_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 400
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.04
wandb_entity: null
wandb_mode: online
wandb_name: 2a9710ae-957a-477d-91f9-0a8bce2d110f
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 2a9710ae-957a-477d-91f9-0a8bce2d110f
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b35eda51-b72a-4fa3-8e0f-cf291990bf61
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v0.6](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v0.6) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3703
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 7785
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3216 | 0.0002 | 1 | 1.3114 |
| 0.6908 | 0.0764 | 400 | 0.6985 |
| 0.7019 | 0.1527 | 800 | 0.6477 |
| 0.7311 | 0.2291 | 1200 | 0.6106 |
| 0.5303 | 0.3055 | 1600 | 0.5805 |
| 0.5387 | 0.3818 | 2000 | 0.5524 |
| 0.4918 | 0.4582 | 2400 | 0.5240 |
| 0.4263 | 0.5346 | 2800 | 0.5054 |
| 0.3871 | 0.6109 | 3200 | 0.4860 |
| 0.4889 | 0.6873 | 3600 | 0.4665 |
| 0.5825 | 0.7637 | 4000 | 0.4460 |
| 0.2564 | 0.8400 | 4400 | 0.4248 |
| 0.3898 | 0.9164 | 4800 | 0.4101 |
| 0.5203 | 0.9927 | 5200 | 0.3958 |
| 0.3845 | 1.0691 | 5600 | 0.3878 |
| 0.4146 | 1.1455 | 6000 | 0.3814 |
| 0.1652 | 1.2218 | 6400 | 0.3756 |
| 0.3675 | 1.2982 | 6800 | 0.3727 |
| 0.3705 | 1.3746 | 7200 | 0.3708 |
| 0.22 | 1.4509 | 7600 | 0.3703 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
TheBloke/Solar-10.7B-SLERP-GGUF | TheBloke | "2023-12-14T19:21:11Z" | 155 | 14 | transformers | [
"transformers",
"gguf",
"solar",
"en",
"base_model:jan-hq/Solar-10.7B-SLERP",
"base_model:quantized:jan-hq/Solar-10.7B-SLERP",
"license:apache-2.0",
"region:us",
"conversational"
] | null | "2023-12-14T17:57:10Z" | ---
base_model: janhq/Solar-10.7B-SLERP
inference: false
language:
- en
license: apache-2.0
model_creator: Jan
model_name: Solar 10.7B SLERP
model_type: solar
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Solar 10.7B SLERP - GGUF
- Model creator: [Jan](https://huggingface.co/janhq)
- Original model: [Solar 10.7B SLERP](https://huggingface.co/janhq/Solar-10.7B-SLERP)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jan's Solar 10.7B SLERP](https://huggingface.co/janhq/Solar-10.7B-SLERP).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF)
* [Jan's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/janhq/Solar-10.7B-SLERP)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [solar-10.7b-slerp.Q2_K.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q2_K.gguf) | Q2_K | 2 | 4.55 GB| 7.05 GB | smallest, significant quality loss - not recommended for most purposes |
| [solar-10.7b-slerp.Q3_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_S.gguf) | Q3_K_S | 3 | 4.67 GB| 7.17 GB | very small, high quality loss |
| [solar-10.7b-slerp.Q3_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_M.gguf) | Q3_K_M | 3 | 5.19 GB| 7.69 GB | very small, high quality loss |
| [solar-10.7b-slerp.Q3_K_L.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q3_K_L.gguf) | Q3_K_L | 3 | 5.65 GB| 8.15 GB | small, substantial quality loss |
| [solar-10.7b-slerp.Q4_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_0.gguf) | Q4_0 | 4 | 6.07 GB| 8.57 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [solar-10.7b-slerp.Q4_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_K_S.gguf) | Q4_K_S | 4 | 6.10 GB| 8.60 GB | small, greater quality loss |
| [solar-10.7b-slerp.Q4_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q4_K_M.gguf) | Q4_K_M | 4 | 6.46 GB| 8.96 GB | medium, balanced quality - recommended |
| [solar-10.7b-slerp.Q5_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_0.gguf) | Q5_0 | 5 | 7.40 GB| 9.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [solar-10.7b-slerp.Q5_K_S.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_K_S.gguf) | Q5_K_S | 5 | 7.40 GB| 9.90 GB | large, low quality loss - recommended |
| [solar-10.7b-slerp.Q5_K_M.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q5_K_M.gguf) | Q5_K_M | 5 | 7.60 GB| 10.10 GB | large, very low quality loss - recommended |
| [solar-10.7b-slerp.Q6_K.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q6_K.gguf) | Q6_K | 6 | 8.81 GB| 11.31 GB | very large, extremely low quality loss |
| [solar-10.7b-slerp.Q8_0.gguf](https://huggingface.co/TheBloke/Solar-10.7B-SLERP-GGUF/blob/main/solar-10.7b-slerp.Q8_0.gguf) | Q8_0 | 8 | 11.41 GB| 13.91 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Solar-10.7B-SLERP-GGUF and below it, a specific filename to download, such as: solar-10.7b-slerp.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Solar-10.7B-SLERP-GGUF solar-10.7b-slerp.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Solar-10.7B-SLERP-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Solar-10.7B-SLERP-GGUF solar-10.7b-slerp.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m solar-10.7b-slerp.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./solar-10.7b-slerp.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./solar-10.7b-slerp.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Jan's Solar 10.7B SLERP
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a href="https://jan.ai/">Jan</a
>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `Slerp` merge method from the best models on 14th Dec on the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
2. [janhq/Pandora-v1-10.7B](https://huggingface.co/janhq/Pandora-v1-10.7B)
- base model: [upstage/SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0)
The yaml config file for this model is here:
```yaml
slices:
- sources:
- model: upstage/SOLAR-10.7B-Instruct-v1.0
layer_range: [0, 48]
- model: janhq/Pandora-v1-10.7B
layer_range: [0, 48]
merge_method: slerp
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
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
```
# Prompt template
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
# Run this model
You can run this model using [Jan](https://jan.ai/) on Mac, Windows, or Linux.
**Jan is an open source, ChatGPT alternative that is:**
💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
🌐 **OpenAI Compatible**: Local server on port `
1337` with OpenAI compatible endpoints
🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)
- Please use the [Solar-10.7B-SLERP-GGUF](https://huggingface.co/janhq/Solar-10.7B-SLERP-GGUF) when using on Jan.
# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
<!-- original-model-card end -->
|
microsoft/swin-base-patch4-window12-384-in22k | microsoft | "2022-05-16T18:01:06Z" | 3,330 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"swin",
"image-classification",
"vision",
"dataset:imagenet-21k",
"arxiv:2103.14030",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2022-03-02T23:29:05Z" | ---
license: apache-2.0
tags:
- vision
- image-classification
datasets:
- imagenet-21k
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# Swin Transformer (large-sized model)
Swin Transformer model pre-trained on ImageNet-21k (14 million images, 21,841 classes) at resolution 384x384. It was introduced in the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Liu et al. and first released in [this repository](https://github.com/microsoft/Swin-Transformer).
Disclaimer: The team releasing Swin Transformer did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.
[Source](https://paperswithcode.com/method/swin-transformer)
## Intended uses & limitations
You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for
fine-tuned versions on a task that interests you.
### How to use
Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes:
```python
from transformers import AutoFeatureExtractor, SwinForImageClassification
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/swin-base-patch4-window12-384-in22k")
model = SwinForImageClassification.from_pretrained("microsoft/swin-base-patch4-window12-384-in22k")
inputs = feature_extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# model predicts one of the 1000 ImageNet classes
predicted_class_idx = logits.argmax(-1).item()
print("Predicted class:", model.config.id2label[predicted_class_idx])
```
For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#).
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-2103-14030,
author = {Ze Liu and
Yutong Lin and
Yue Cao and
Han Hu and
Yixuan Wei and
Zheng Zhang and
Stephen Lin and
Baining Guo},
title = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
journal = {CoRR},
volume = {abs/2103.14030},
year = {2021},
url = {https://arxiv.org/abs/2103.14030},
eprinttype = {arXiv},
eprint = {2103.14030},
timestamp = {Thu, 08 Apr 2021 07:53:26 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2103-14030.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
``` |
AlignmentResearch/robust_llm_pythia-word-length-1b-niki-ada-v1 | AlignmentResearch | "2024-03-20T18:34:56Z" | 104 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b-deduped",
"base_model:finetune:EleutherAI/pythia-1b-deduped",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-03-20T18:32:38Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-1b-deduped
model-index:
- name: robust_llm_pythia-word-length-1b-niki-ada-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. -->
# robust_llm_pythia-word-length-1b-niki-ada-v1
This model is a fine-tuned version of [EleutherAI/pythia-1b-deduped](https://huggingface.co/EleutherAI/pythia-1b-deduped) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
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