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Canstralian/cryptoquant | Canstralian | 2024-06-17T02:01:56Z | 0 | 0 | bertopic | [
"bertopic",
"crypto",
"cryptocurrency",
"trading",
"predictive",
"en",
"dataset:paperswithbacktest/Cryptocurrencies-Daily-Price",
"dataset:SahandNZ/cryptonews-articles-with-price-momentum-labels",
"dataset:ElKulako/cryptobert-posttrain",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | null | 2024-06-17T01:52:15Z | ---
license: mit
datasets:
- paperswithbacktest/Cryptocurrencies-Daily-Price
- SahandNZ/cryptonews-articles-with-price-momentum-labels
- ElKulako/cryptobert-posttrain
language:
- en
library_name: bertopic
tags:
- crypto
- cryptocurrency
- trading
- predictive
---
# 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
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## Model Examination [optional]
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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BAAI/AquilaDense-7B | BAAI | 2024-06-17T02:01:49Z | 11 | 2 | transformers | [
"transformers",
"pytorch",
"aquiladense",
"text-generation",
"moe",
"conversational",
"custom_code",
"en",
"zh",
"arxiv:2401.09192",
"arxiv:2212.05055",
"arxiv:2202.08906",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-06-15T08:03:04Z | ---
license: apache-2.0
language:
- en
- zh
tags:
- moe
---
# AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
<p align="center">
<br>
<strong>Language Foundation Model & Software Team</strong> <br>
<strong>Beijing Academy of Artificial Intelligence (BAAI)</strong>
<br><br>
<img src="./assets/6eea11dd-4554-44ca-9459-f5e0177b510b.png" width="1280" align="center" />
<br><br>
<a>[Paper(released soon)]</a>
<a href="https://github.com/FlagOpen/FlagScale">[Code]</a>
<a href="https://github.com/FlagAI-Open/Aquila-MoE">[github]</a>
</p>
We present **AquilaMoE**, a cutting-edge bilingual 8\*16B Mixture of Experts (MoE) language model developed using an innovative training methodology called EfficientScale. This approach optimizes performance while minimizing data requirements through a two-stage process. The first stage, termed Scale-Up, initializes the larger model with weights from a pre-trained smaller model, enabling substantial knowledge transfer and continuous pretraining with significantly less data. The second stage, Scale-Out, uses a pre-trained dense model to initialize the MoE experts, further enhancing knowledge transfer and performance. Extensive validation experiments on 1.8B and 7B models compared various initialization schemes, achieving models that maintain and reduce loss during continuous pretraining. Utilizing the optimal scheme, we successfully trained a 16B model and subsequently the 8\*16B AquilaMoE model, demonstrating significant improvements in performance and training efficiency.
## Training Details
### Datasets
We constructed a bilingual pretraining dataset of 4TB tokens in both Chinese and English. This dataset includes webpages, arXiv papers, encyclopedic data, books, codes, and QA pairs. It covers a wide range of high-quality open-source pretraining data such as [RedPajama-Data-V2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2), [falcon-refinedweb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), [C4](https://huggingface.co/datasets/allenai/c4), [Pile](https://huggingface.co/datasets/EleutherAI/pile), [WuDaoCorporaText](https://data.baai.ac.cn/details/WuDaoCorporaText), [ChineseWebText](https://huggingface.co/datasets/CASIA-LM/ChineseWebText), etc. The above open-source data underwent language filtering to retain only Chinese and English texts, heuristic refinement to remove low-quality content, deduplication to maintain uniqueness, domain-specific filtering for relevance, data quality checks, removal of toxic and explicit content, and finally, data mixing in specified proportions.
### Model Configurations
| | Aquila3 7B | Aquila3 16B | Aquila3 8x16B |
|----------------------------|------------|-------------|---------------|
| Context Length | 4096 | 4096 | 4096 |
| QKV Bias | yes | yes | yes |
| Layers | 32 | 40 | 40 |
| Hidden Dim | 4096 | 5120 | 5120 |
| Intermediate Dim | 14336 | 20480 | 20480 |
| KV Group | 8 | 8 | 8 |
| Trained Tokens | 3.6T | 1.2T | 545B |
| LR | 1.2e-3 | 4e-4 | 1.5e-4 |
| Batch Size | 12M | 12M | 24M |
### Training Procedures
The EfficientScale pipeline efficiently trains a large-scale Mixture of Experts (MoE) model by leveraging knowledge transfer from smaller models. It consists of three key phases: Preparation, Scale-Up, and Scale-Out, each ensuring effective knowledge transfer and continuous learning for an optimized MoE model.
#### 1. Preparation Phase
In the preparation phase, a small dense model is trained, and datasets are prepared for the following stages. This phase ensures the initial model has adequate transferable knowledge and that data is ready for effective training and validation.
- **Model Preparation**: Train a small dense model from scratch on a large number of tokens or use a pre-trained small model. This step ensures the model accumulates sufficient transferable knowledge to serve as a strong foundation.
- **Data Preparation**: Collect, clean, and preprocess the training and validation datasets. This step ensures the data is suitable for effective training and validation.
- **Validation Setup**: Develop training and validation datasets to monitor model performance. Continuous tracking of the model's loss on the validation dataset ensures the initialized models retain transferred knowledge and effectively learn new information.
#### 2. Scale-Up Phase
The Scale-Up phase involves initializing a larger dense model with the weights from a smaller model and performing continuous pretraining to enhance performance.
- **Weight Initialization Strategies**: the weights from a small dense model are used to initialize a larger dense model using three strategies:
- **Function Preserving Initialization (FPI)**: Expands the model's width while preserving the same output, ensuring knowledge transfer from the smaller model [1].
- **Advanced Knowledge Initialization (AKI)**: Addresses symmetry issues in FPI by incorporating weights from both the same and upper layers of the smaller model and uses stacking for depth expansion [2].
- **AKI-Pro**: Improves AKI with two refinements:
1. **Interpolation for Depth Growth**: Uses interpolation instead of stacking for stable continuous training [3].
2. **GQA Compatibility**: Adapts AKI for Group Query Attention models.
- **Continuous Pretraining Process**: the scaled-up dense model undergoes continuous pretraining on a large amount of tokens, ensuring effective knowledge transfer and improved performance.
#### 3. Scale-Out Phase
The scale-out phase transforms a large dense model into a Mixture of Experts (MoE) model, including initializing MoE weights and continuous pretraining to enhance performance.
- **MoE Weight Initialization**: Aquila-MoE is initialized using Sparse Upcycling [4, 5]. The dense model's MLP layers are replaced with MoE layers, exact replicas of the original, with router parameters initialized normally (mean = 0, variance = 0.02).
- **Continuous Pretraining of MoE**: During training and inference, two of eight experts are activated per token, utilizing about 30B parameters. To prevent training collapse, load balancing loss [6] and max z-loss [7, 8] are applied, scaled by 0.001 and 0.01, respectively, ensuring balanced token distribution and stable training.
EfficientScale enables efficient large-scale model training by leveraging pre-trained smaller models, reducing data and computational needs, and ensuring effective knowledge transfer and continuous learning.
### References
[1] Chen, T., Goodfellow, I., & Shlens, J. (2016). Net2net: Accelerating learning via knowledge transfer. In Proceedings of ICLR 2016.
[2] Chen, C., Yin, Y., Shang, L., Jiang, X., Qin, Y., Wang, F., Wang, Z., Chen, X., Liu, Z., & Liu, Q. (2022). bert2BERT: Towards reusable pretrained language models. In Proceedings of ACL 2022.
[3] Pan, Y., Yuan, Y., Yin, Y., Shi, J., Xu, Z., Zhang, M., Shang, L., Jiang, X., & Liu, Q. (2024). Preparing lessons for progressive training on language models. arXiv:2401.09192.
[4] Komatsuzaki, A., Puigcerver, J., Lee-Thorp, J., Riquelme Ruiz, C., Mustafa, B., Ainslie, J., Tay, Y., Dehghani, M., & Houlsby, N. (2022). Sparse upcycling: Training mixture-of-experts from dense checkpoints. arXiv:2212.05055.
[5] Hu, S., Tu, Y., Han, X., He, C., Cui, G., Long, X., Zheng, Z., Fang, Y., Huang, Y., Zhao, W., et al. (2024). Minicpm: Unveiling the potential of small language models with scalable training strategies. arXiv:2404.
[6] Fedus, W., Zoph, B., & Shazeer, N. (2022). Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity. JMLR, 23(120), 1–39.
[7] Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2023). Palm: Scaling language modeling with pathways. JMLR, 24(240), 1–113.
[8] Zoph, B., Bello, I., Kumar, S., Du, N., Huang, Y., Dean, J., Shazeer, N., & Fedus, W. (2022). St-moe: Designing stable and transferable sparse expert models. arXiv:2202.08906.
### Training Loss
The plot illustrates the training loss versus log FLOPs for the three stages of model training: AquilaDense-7B, AquilaDense-16B, and AquilaMoE.
1. **Each Stage's Decrease**: Throughout the training process, we observe a steady decrease in training loss as log FLOPs increase for each stage. The AquilaDense-7B (blue line) stage shows a gradual reduction in loss, followed by the AquilaDense-16B (orange line) stage, which continues the trend.
2. **Quick Recovery at Transition Points**: When transitioning from AquilaDense-7B to AquilaDense-16B, and from AquilaDense-16B to AquilaMoE (light blue line), the training loss quickly recovers to the range of the previous stage with minimal additional compute. This indicates that each new stage rapidly achieves the performance level of the preceding stage before further improvements.
3. **Increasing Slope**: A notable observation is that the slope of the training loss curve becomes progressively steeper with each stage, indicating an increasingly efficient reduction in training loss. This means that as the model scales and transitions from one stage to the next, the efficiency of training improves, resulting in faster decreases in training loss.
This plot effectively demonstrates the combined benefits of continuous loss reduction, quick recovery at stage transitions, and increasing efficiency in training.
<p align="center">
<img src="./assets/4d716d22-98b1-4f15-a40e-f16070cf76fa.png" width = "800" align=center />
<p>
### Performance
The performance of the AquilaMoE model series improves significantly across multiple tasks as the parameter size increases. Both scale-up and scale-out strategies are highly effective in enhancing model performance.
<p align="center">
<img src="./assets/1280X1280.PNG" width = "800" align=center />
<p>
#### Foundation Models
| Model | AquilaDense-7B | AquilaDense-16B | AquilaMoE |
|------------------|----------------|-----------------|-----------|
| ARC-c-ppl | 37.63 | 38.31 | 43.05 |
| ARC-e-ppl | 56.08 | 52.2 | 65.61 |
| hellaswag-ppl | 67.49 | 71.62 | 73.94 |
| gsm8k-gen | 7.81 | 28.51 | 54.51 |
| humaneval-gen | 14.02 | 29.88 | 15.85 |
| mmlu-ppl | 46.47 | 57.11 | 61 |
| winograd-ppl | 50.53 | 54.04 | 55.4 |
| math-gen | 1.32 | 4.24 | 10.4 |
| mbpp-gen | 15.6 | 36.4 | 37.2 |
| drop-gen | 4.35 | 33.35 | 37.62 |
| agieval-gen | 14.47 | 18.57 | - |
| bbh-gen | 34.51 | 41.45 | 46.04 |
| nq-gen | 8.61 | 9.94 | 10.78 |
| piqa-ppl | 76.71 | 79.22 | 80.3 |
*Table: Overall evaluation results of AquilaDense and AquilaMoE (AquilaMoE-8\*16B).*
#### Fine-tuned AquilaMoE
| Model | AquilaMoE-SFT |
|------------------|---------------|
| ARC-c-ppl | 49.15 |
| ARC-e-ppl | 69.49 |
| hellaswag-ppl | 69.77 |
| gsm8k-gen | 71.27 |
| humaneval-gen | 40.24 |
| mmlu-ppl | 59.93 |
| winograd-ppl | 57.5 |
| Model | GPT 3.5 Turbo (11/06) | GPT 3.5 Turbo (03/01) | AquilaMoE-SFT |
|------------------|-----------------------|-----------------------|---------------|
| AlpacaEval 2.0 | 19.3 | 18.1 | 21.1 |
*Table: Performance of AquilaMoE-SFT (16\*8B) on various benchmarks.*
## License Agreement
The AquilaMoE project is based on the Apache 2.0 license; The AquilaMoE series models are based on the [BAAI Aquila Model License Agreement](./assets/aquila_license.pdf).
## Limitations
The AquilaMoE 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.
## Contact Us
If you are interested, please join our WeChat groups!
<img src="./assets/wechat-qrcode.jpg" width = "200" height = "200" align=center />
## Citation
Our paper, detailing the efficient training methods for MoE models using Scale-Up and Scale-Out strategies, will be released soon on arXiv. Stay tuned!
```bibtex
@article{AquilaMoE2024,
title={AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies},
author={{Language Foundation Model \& Software Team, Beijing Academy of Artificial Intelligence (BAAI)}},
journal={arXiv preprint arXiv:2406.XXXX},
year={2024}
} |
OmnicromsBrain/NeuralStar_AlphaWriter_4x7b | OmnicromsBrain | 2024-06-17T01:51:40Z | 17 | 9 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/AlphaMonarch-7B",
"FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B",
"SanjiWatsuki/Kunoichi-DPO-v2-7B",
"OmnicromsBrain/NeuralStar-7b-Lazy",
"conversational",
"base_model:FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B",
"base_model:merge:FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B",
"base_model:OmnicromsBrain/NeuralStar-7b-Lazy",
"base_model:merge:OmnicromsBrain/NeuralStar-7b-Lazy",
"base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:mlabonne/AlphaMonarch-7B",
"base_model:merge:mlabonne/AlphaMonarch-7B",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-15T02:46:54Z | ---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- mlabonne/AlphaMonarch-7B
- FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- OmnicromsBrain/NeuralStar-7b-Lazy
base_model:
- mlabonne/AlphaMonarch-7B
- FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
- OmnicromsBrain/NeuralStar-7b-Lazy
model-index:
- name: NeuralStar_AlphaWriter_4x7b
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: 70.22
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
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: 88.31
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
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: 64.6
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
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: 71.7
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
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: 82.0
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
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: 63.0
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=OmnicromsBrain/NeuralStar_AlphaWriter_4x7b
name: Open LLM Leaderboard
---
# NeuralStar_AlphaWriter_4x7b
I was blown away by the writing results I was getting from mlabonne/Beyonder-4x7B-v3 while writing in [NovelCrafter](https://www.novelcrafter.com).
Inspired by his [LLM Course](https://github.com/mlabonne/llm-course) and fueled by his [LazyMergeKit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb).
I couldnt help but wonder what a writing model would be like if all 4 “experts” excelled in creative writing.
I present NeuralStar-AlphaWriter-4x7b:
NeuralStar_AlphaWriter_4x7b is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/AlphaMonarch-7B](https://huggingface.co/mlabonne/AlphaMonarch-7B)
* [FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B](https://huggingface.co/FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B)
* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
* [OmnicromsBrain/NeuralStar-7b-Lazy](https://huggingface.co/OmnicromsBrain/NeuralStar-7b-Lazy)
## ⚡ Quantized Models
Special thanks to MRadermacher for the Static and iMatrx quantized models
**.GGUF** https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-GGUF
**iMatrix** https://huggingface.co/mradermacher/NeuralStar_AlphaWriter_4x7b-i1-GGUF
Q4_K_M and Q5_K_M .gguf [**Here**](https://huggingface.co/OmnicromsBrain/NeuralStar_AlphaWriter_4x7b-GGUF) created with [mlabonne/Autogguf](https://colab.research.google.com/drive/1P646NEg33BZy4BfLDNpTz0V0lwIU3CHu)
## 🧩 Configuration
```yaml
base_model: mlabonne/AlphaMonarch-7B
experts:
- source_model: mlabonne/AlphaMonarch-7B
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- "I want"
- source_model: FPHam/Karen_TheEditor_V2_STRICT_Mistral_7B
positive_prompts:
- "edit"
- "rewrite"
- "evaluate"
- "spelling"
- "grammer"
- source_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
positive_prompts:
- "storywriting"
- "write"
- "scene"
- "prose"
- "character"
- source_model: OmnicromsBrain/NeuralStar-7b-Lazy
positive_prompts:
- "codex"
- "plot"
- "outline"
- "scenebeat"
- "count"
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "OmnicromsBrain/NeuralStar_AlphaWriter_4x7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_OmnicromsBrain__NeuralStar_AlphaWriter_4x7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |73.31|
|AI2 Reasoning Challenge (25-Shot)|70.22|
|HellaSwag (10-Shot) |88.31|
|MMLU (5-Shot) |64.60|
|TruthfulQA (0-shot) |71.70|
|Winogrande (5-shot) |82.00|
|GSM8k (5-shot) |63.00|
|
Casper0508/MSc_llama2_finetuned_model | Casper0508 | 2024-06-17T01:50:43Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"region:us"
] | null | 2024-06-17T01:50:32Z | ---
license: llama2
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: MSc_llama2_finetuned_model
results: []
library_name: peft
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MSc_llama2_finetuned_model
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4323
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
- load_in_4bit: True
- load_in_8bit: False
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 10.3286 | 1.21 | 10 | 0.9783 |
| 0.7962 | 2.42 | 20 | 0.6498 |
| 0.5916 | 3.64 | 30 | 0.5509 |
| 0.5269 | 4.85 | 40 | 0.5075 |
| 0.4919 | 6.06 | 50 | 0.4851 |
| 0.4764 | 7.27 | 60 | 0.4696 |
| 0.4626 | 8.48 | 70 | 0.4597 |
| 0.4529 | 9.7 | 80 | 0.4654 |
| 0.4522 | 10.91 | 90 | 0.4489 |
| 0.4417 | 12.12 | 100 | 0.4456 |
| 0.4347 | 13.33 | 110 | 0.4409 |
| 0.4328 | 14.55 | 120 | 0.4381 |
| 0.4288 | 15.76 | 130 | 0.4376 |
| 0.4232 | 16.97 | 140 | 0.4364 |
| 0.4225 | 18.18 | 150 | 0.4344 |
| 0.4216 | 19.39 | 160 | 0.4330 |
| 0.4194 | 20.61 | 170 | 0.4323 |
| 0.4178 | 21.82 | 180 | 0.4323 |
| 0.4176 | 23.03 | 190 | 0.4323 |
| 0.4171 | 24.24 | 200 | 0.4323 |
### Framework versions
- PEFT 0.4.0
- Transformers 4.38.2
- Pytorch 2.3.0+cu121
- Datasets 2.13.1
- Tokenizers 0.15.2
|
MaziyarPanahi/mergekit-ties-cmdmayc-GGUF | MaziyarPanahi | 2024-06-17T01:46:52Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:BioMistral/BioMistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-ties-cmdmayc",
"base_model:quantized:mergekit-community/mergekit-ties-cmdmayc"
] | text-generation | 2024-06-17T01:23:57Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- arxiv:2306.01708
- base_model:mistralai/Mistral-7B-v0.1
- base_model:mistralai/Mistral-7B-Instruct-v0.2
- base_model:BioMistral/BioMistral-7B
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-ties-cmdmayc-GGUF
base_model: mergekit-community/mergekit-ties-cmdmayc
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-ties-cmdmayc-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-ties-cmdmayc-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-ties-cmdmayc](https://huggingface.co/mergekit-community/mergekit-ties-cmdmayc)
## Description
[MaziyarPanahi/mergekit-ties-cmdmayc-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-ties-cmdmayc-GGUF) contains GGUF format model files for [mergekit-community/mergekit-ties-cmdmayc](https://huggingface.co/mergekit-community/mergekit-ties-cmdmayc).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [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.
* [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.
* [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.
* [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.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
alter-wang/bert-base-japanese-emotion-lily | alter-wang | 2024-06-17T01:44:16Z | 309 | 2 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"ja",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-04-25T06:05:50Z | ---
language:
- ja
---
This is a BERT Base model for emotion analysis in Japanese additionally fine-tuned for emotion detection and classification.
The model was based on [tohoku-nlp/bert-base-japanese](https://huggingface.co/tohoku-nlp/bert-base-japanese), and later finetuned on a dataset containing 10 labels of emotional blog posts.
The dataset was composed of about 1,000 sentences, with about 100 sentences each for each emotion category.
emotion_mapping = {
0: 'amaze',
1: 'anger',
2: 'dislike',
3: 'excite',
4: 'fear',
5: 'joy',
6: 'like',
7: 'relief',
8: 'sad',
9: 'shame'
}
emotion_mapping = {
0: '驚き',
1: '怒り',
2: 'いや',
3: '昂り',
4: '怖がり',
5: '喜び',
6: '好き',
7: '安らぎ',
8: '悲しみ',
9: '恥ずかしい'
} |
ntluongg/bart-base-luong | ntluongg | 2024-06-17T01:38:40Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-17T01:38:22Z | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-luong
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-base-luong
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2356
- Rouge1: 45.4069
- Rouge2: 23.2838
- Rougel: 39.4615
- Rougelsum: 41.5905
- Gen Len: 18.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.2702 | 1.0 | 2307 | 0.2429 | 43.0834 | 19.6597 | 36.4303 | 39.1751 | 18.0 |
| 0.2121 | 2.0 | 4615 | 0.2338 | 43.5038 | 20.3513 | 37.1389 | 39.418 | 18.0 |
| 0.1917 | 3.0 | 6922 | 0.2327 | 44.3658 | 21.3002 | 38.0506 | 40.4574 | 18.0 |
| 0.1768 | 4.0 | 9230 | 0.2304 | 44.761 | 22.2373 | 38.713 | 40.955 | 18.0 |
| 0.1658 | 5.0 | 11537 | 0.2310 | 45.176 | 22.8385 | 39.0963 | 41.2373 | 18.0 |
| 0.1567 | 6.0 | 13845 | 0.2327 | 45.2475 | 22.7529 | 38.9987 | 41.2975 | 18.0 |
| 0.1498 | 7.0 | 16152 | 0.2350 | 45.4093 | 22.9187 | 39.1624 | 41.4173 | 18.0 |
| 0.1444 | 8.0 | 18460 | 0.2340 | 45.6332 | 23.1632 | 39.5567 | 41.5893 | 18.0 |
| 0.1406 | 9.0 | 20767 | 0.2353 | 45.1827 | 22.7108 | 39.089 | 41.2022 | 18.0 |
| 0.1385 | 10.0 | 23070 | 0.2356 | 45.4069 | 23.2838 | 39.4615 | 41.5905 | 18.0 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.15.2
|
SampleTheory/distilroberta-base-finetuned-wikitext2 | SampleTheory | 2024-06-17T01:37:22Z | 211 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-06-16T23:41:26Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-finetuned-wikitext2
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. -->
# distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4801
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 178 | 1.5821 |
| No log | 2.0 | 356 | 1.5286 |
| 1.8052 | 3.0 | 534 | 1.4941 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
abyesses/finetuning-sentiment-model-5000-amazon | abyesses | 2024-06-17T01:37:13Z | 111 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-16T18:45:15Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-5000-amazon
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-5000-amazon
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3199
## 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
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.20.0
- Tokenizers 0.19.1
|
ElviraL/ppo-LunarLander-v2 | ElviraL | 2024-06-17T01:27:50Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-05-13T08:33:26Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 269.82 +/- 11.94
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
...
```
|
mahdibaghbanzadeh/seqsight_8192_512_17M_four-domain | mahdibaghbanzadeh | 2024-06-17T01:18:50Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-13T22:29:57Z | ---
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] |
TensorStack/ZavyChroma-XL-onnx | TensorStack | 2024-06-17T01:16:46Z | 0 | 1 | null | [
"onnx",
"text-to-image",
"region:us"
] | text-to-image | 2024-06-17T01:06:45Z | ---
pipeline_tag: text-to-image
---
# Zavy Chroma XL v8 - Onnx Olive DirectML Optimized
## Original Model
https://civitai.com/models/119229?modelVersionId=563988
## C# Inference Demo
https://github.com/saddam213/OnnxStack
```csharp
// Create Pipeline
var pipeline = StableDiffusionXLPipeline.CreatePipeline("D:\\Models\\ZavyChromaXL-onnx");
// Prompt
var promptOptions = new PromptOptions
{
Prompt = "Visualize a traditional wooden fishing boat anchored in a quiet harbor, with nets and fishing gear on board."
};
// Run pipeline
var result = await pipeline.GenerateImageAsync(promptOptions);
// Save Image Result
await result.SaveAsync("Result.png");
```
## Inference Result
 |
MaziyarPanahi/mergekit-ties-itmchpd-GGUF | MaziyarPanahi | 2024-06-17T01:14:49Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:BioMistral/BioMistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-ties-itmchpd",
"base_model:quantized:mergekit-community/mergekit-ties-itmchpd"
] | text-generation | 2024-06-17T00:52:20Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- arxiv:2306.01708
- base_model:mistralai/Mistral-7B-v0.1
- base_model:mistralai/Mistral-7B-Instruct-v0.2
- base_model:BioMistral/BioMistral-7B
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-ties-itmchpd-GGUF
base_model: mergekit-community/mergekit-ties-itmchpd
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-ties-itmchpd-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-ties-itmchpd-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-ties-itmchpd](https://huggingface.co/mergekit-community/mergekit-ties-itmchpd)
## Description
[MaziyarPanahi/mergekit-ties-itmchpd-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-ties-itmchpd-GGUF) contains GGUF format model files for [mergekit-community/mergekit-ties-itmchpd](https://huggingface.co/mergekit-community/mergekit-ties-itmchpd).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [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.
* [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.
* [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.
* [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.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
rusticluftig/finetuning-test2 | rusticluftig | 2024-06-17T01:11:35Z | 205 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-17T01:10:07Z | ---
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]
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<!-- 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]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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## Model Card Contact
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rusticluftig/finetuning-test | rusticluftig | 2024-06-17T00:51:55Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-16T17:46:53Z | ---
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
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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 -->
[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
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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
### Training Data
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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[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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## Glossary [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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MaziyarPanahi/mergekit-slerp-eundxnq-GGUF | MaziyarPanahi | 2024-06-17T00:43:36Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:meta-llama/Meta-Llama-3-8B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-eundxnq",
"base_model:quantized:mergekit-community/mergekit-slerp-eundxnq"
] | text-generation | 2024-06-17T00:18:46Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- llama
- text-generation
- mergekit
- merge
- conversational
- base_model:meta-llama/Meta-Llama-3-8B-Instruct
- base_model:meta-llama/Meta-Llama-3-8B
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-eundxnq-GGUF
base_model: mergekit-community/mergekit-slerp-eundxnq
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-eundxnq-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-eundxnq-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-eundxnq](https://huggingface.co/mergekit-community/mergekit-slerp-eundxnq)
## Description
[MaziyarPanahi/mergekit-slerp-eundxnq-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-eundxnq-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-eundxnq](https://huggingface.co/mergekit-community/mergekit-slerp-eundxnq).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [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.
* [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.
* [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.
* [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.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
Ramikan-BR/TiamaPY-v28 | Ramikan-BR | 2024-06-17T00:21:05Z | 151 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gguf",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:quantized:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-16T23:00:33Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** Ramikan-BR
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-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)
|
akashmaggon/first-vllm-metric_included | akashmaggon | 2024-06-17T00:15:21Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-15T23:27:57Z | ---
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] |
MaziyarPanahi/mergekit-slerp-jdsasof-GGUF | MaziyarPanahi | 2024-06-17T00:08:27Z | 8 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:Endevor/InfinityRP-v1-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-jdsasof",
"base_model:quantized:mergekit-community/mergekit-slerp-jdsasof"
] | text-generation | 2024-06-16T23:46:05Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B
- base_model:Endevor/InfinityRP-v1-7B
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-jdsasof-GGUF
base_model: mergekit-community/mergekit-slerp-jdsasof
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-jdsasof-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-jdsasof-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-jdsasof](https://huggingface.co/mergekit-community/mergekit-slerp-jdsasof)
## Description
[MaziyarPanahi/mergekit-slerp-jdsasof-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-jdsasof-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-jdsasof](https://huggingface.co/mergekit-community/mergekit-slerp-jdsasof).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [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.
* [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.
* [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.
* [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.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
dsatya6/sentiment-bert-text-v1 | dsatya6 | 2024-06-16T23:58:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T23:58:35Z | ---
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
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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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- 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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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ernestoBocini/finetuning_capitoning_itclass | ernestoBocini | 2024-06-16T23:42:21Z | 2 | 0 | peft | [
"peft",
"safetensors",
"llava_llama",
"generated_from_trainer",
"base_model:liuhaotian/llava-v1.5-7b",
"base_model:adapter:liuhaotian/llava-v1.5-7b",
"region:us"
] | null | 2024-06-13T10:28:24Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: liuhaotian/llava-v1.5-7b
model-index:
- name: finetuning_capitoning_itclass
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning_capitoning_itclass
This model is a fine-tuned version of [liuhaotian/llava-v1.5-7b](https://huggingface.co/liuhaotian/llava-v1.5-7b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 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: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Tokenizers 0.15.1 |
SiguienteGlobal/mexa-7b-0.1 | SiguienteGlobal | 2024-06-16T23:32:55Z | 0 | 0 | null | [
"es",
"dataset:SiguienteGlobal/Open-Hermes-ES",
"doi:10.57967/hf/2560",
"license:apache-2.0",
"region:us"
] | null | 2024-06-16T22:48:32Z | ---
license: apache-2.0
language:
- es
datasets:
- SiguienteGlobal/Open-Hermes-ES
--- |
nc33/vibert-base-cased-ed | nc33 | 2024-06-16T23:24:37Z | 186 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:FPTAI/vibert-base-cased",
"base_model:finetune:FPTAI/vibert-base-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-15T11:52:49Z | ---
base_model: FPTAI/vibert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: vibert-base-cased-ed
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. -->
# vibert-base-cased-ed
This model is a fine-tuned version of [FPTAI/vibert-base-cased](https://huggingface.co/FPTAI/vibert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0617
- F1 Micro: 0.7029
- F1 Macro: 0.0254
- Accuracy: 0.6459
- Recall Micro: 0.6169
- Precision Micro: 0.8169
- Recall Macro: 0.0269
- Precision Macro: 0.0240
- F1: 0.5817
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Accuracy | Recall Micro | Precision Micro | Recall Macro | Precision Macro | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:------------:|:---------------:|:------------:|:---------------:|:------:|
| 0.0696 | 1.0 | 1526 | 0.0711 | 0.6892 | 0.0243 | 0.7054 | 0.6737 | 0.7054 | 0.0294 | 0.0207 | 0.5573 |
| 0.0577 | 2.0 | 3052 | 0.0640 | 0.6943 | 0.0251 | 0.6398 | 0.6111 | 0.8038 | 0.0267 | 0.0236 | 0.5742 |
| 0.0674 | 3.0 | 4578 | 0.0613 | 0.6949 | 0.0252 | 0.6257 | 0.5976 | 0.8300 | 0.0261 | 0.0244 | 0.5778 |
| 0.0576 | 4.0 | 6104 | 0.0610 | 0.7006 | 0.0254 | 0.6358 | 0.6073 | 0.8278 | 0.0265 | 0.0243 | 0.5814 |
| 0.0387 | 5.0 | 7630 | 0.0617 | 0.7029 | 0.0254 | 0.6459 | 0.6169 | 0.8169 | 0.0269 | 0.0240 | 0.5817 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
rdouglas/llama-2-7b-chat-guanaco-test | rdouglas | 2024-06-16T23:20:45Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-06-15T15:10:04Z | ---
license: apache-2.0
---
|
RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf | RichardErkhov | 2024-06-16T23:10:45Z | 10 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-16T21:20:51Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
ConvAI-9b - GGUF
- Model creator: https://huggingface.co/CreitinGameplays/
- Original model: https://huggingface.co/CreitinGameplays/ConvAI-9b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [ConvAI-9b.Q2_K.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q2_K.gguf) | Q2_K | 3.13GB |
| [ConvAI-9b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.IQ3_XS.gguf) | IQ3_XS | 3.48GB |
| [ConvAI-9b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.IQ3_S.gguf) | IQ3_S | 3.67GB |
| [ConvAI-9b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q3_K_S.gguf) | Q3_K_S | 3.65GB |
| [ConvAI-9b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.IQ3_M.gguf) | IQ3_M | 3.79GB |
| [ConvAI-9b.Q3_K.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q3_K.gguf) | Q3_K | 4.05GB |
| [ConvAI-9b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q3_K_M.gguf) | Q3_K_M | 4.05GB |
| [ConvAI-9b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q3_K_L.gguf) | Q3_K_L | 4.41GB |
| [ConvAI-9b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.IQ4_XS.gguf) | IQ4_XS | 4.55GB |
| [ConvAI-9b.Q4_0.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q4_0.gguf) | Q4_0 | 4.74GB |
| [ConvAI-9b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.IQ4_NL.gguf) | IQ4_NL | 4.79GB |
| [ConvAI-9b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q4_K_S.gguf) | Q4_K_S | 4.78GB |
| [ConvAI-9b.Q4_K.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q4_K.gguf) | Q4_K | 5.04GB |
| [ConvAI-9b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q4_K_M.gguf) | Q4_K_M | 5.04GB |
| [ConvAI-9b.Q4_1.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q4_1.gguf) | Q4_1 | 5.26GB |
| [ConvAI-9b.Q5_0.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q5_0.gguf) | Q5_0 | 5.77GB |
| [ConvAI-9b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q5_K_S.gguf) | Q5_K_S | 5.77GB |
| [ConvAI-9b.Q5_K.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q5_K.gguf) | Q5_K | 5.93GB |
| [ConvAI-9b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q5_K_M.gguf) | Q5_K_M | 5.93GB |
| [ConvAI-9b.Q5_1.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q5_1.gguf) | Q5_1 | 6.29GB |
| [ConvAI-9b.Q6_K.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q6_K.gguf) | Q6_K | 6.87GB |
| [ConvAI-9b.Q8_0.gguf](https://huggingface.co/RichardErkhov/CreitinGameplays_-_ConvAI-9b-gguf/blob/main/ConvAI-9b.Q8_0.gguf) | Q8_0 | 8.89GB |
Original model description:
---
license: mit
datasets:
- CreitinGameplays/merged-data-v2
base_model:
- HuggingFaceH4/zephyr-7b-beta
- mistral-community/Mistral-7B-v0.2
language:
- en
---
# **ConvAI-9b: A Conversational AI Model**
## **1. Model Details**
* **Model Name:** ConvAI-9b
* **Authors:** CreitinGameplays
* **Date:** April 18th, 2024
## **2. Model Description**
ConvAI-9b is a fine-tuned conversational AI model with 9 billion parameters. It is based on the following models:
* **Base Model:** [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
* **Merged Model:** [mistral-community/Mistral-7B-v0.2](https://huggingface.co/mistral-community/Mistral-7B-v0.2)
## **3. Training Data**
The model was fine-tuned on a custom dataset of conversations between an AI assistant and a user. The dataset format followed a specific structure:
```
<|system|> (system prompt, e.g.: You are a helpful AI language model called ChatGPT, your goal is helping users with their questions) </s> <|user|> (user prompt) </s>
```
## **4. Intended Uses**
ConvAI-9b is intended for use in conversational AI applications, such as:
* Chatbots
* Virtual assistants
* Interactive storytelling
* Educational tools
## **5. Limitations**
* Like any other language model, ConvAI-9b may generate incorrect or misleading responses.
* It may exhibit biases present in the training data.
* The model's performance can be affected by the quality and format of the input text.
## **6. Evaluation**
| Metrics |Value|
|----------|-----|
|ARC |57.50|
|HellaSwag |80.34|
|TruthfulQA|49.54|
|Winogrande|76.24|
More detailed evaluation [here](https://huggingface.co/datasets/open-llm-leaderboard/details_CreitinGameplays__ConvAI-9b)
|
Ramikan-BR/TiamaPY-LORA-v28 | Ramikan-BR | 2024-06-16T23:00:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-chat-bnb-4bit",
"base_model:finetune:unsloth/tinyllama-chat-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T22:59:18Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/tinyllama-chat-bnb-4bit
---
# Uploaded model
- **Developed by:** Ramikan-BR
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-chat-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)
|
RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf | RichardErkhov | 2024-06-16T22:57:36Z | 22 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-16T21:03:10Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
dolphin-2.9.1-yi-1.5-9b - GGUF
- Model creator: https://huggingface.co/cognitivecomputations/
- Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.1-yi-1.5-9b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [dolphin-2.9.1-yi-1.5-9b.Q2_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q2_K.gguf) | Q2_K | 3.12GB |
| [dolphin-2.9.1-yi-1.5-9b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.IQ3_XS.gguf) | IQ3_XS | 3.46GB |
| [dolphin-2.9.1-yi-1.5-9b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.IQ3_S.gguf) | IQ3_S | 3.64GB |
| [dolphin-2.9.1-yi-1.5-9b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q3_K_S.gguf) | Q3_K_S | 3.63GB |
| [dolphin-2.9.1-yi-1.5-9b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.IQ3_M.gguf) | IQ3_M | 3.78GB |
| [dolphin-2.9.1-yi-1.5-9b.Q3_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q3_K.gguf) | Q3_K | 4.03GB |
| [dolphin-2.9.1-yi-1.5-9b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q3_K_M.gguf) | Q3_K_M | 4.03GB |
| [dolphin-2.9.1-yi-1.5-9b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q3_K_L.gguf) | Q3_K_L | 4.37GB |
| [dolphin-2.9.1-yi-1.5-9b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.IQ4_XS.gguf) | IQ4_XS | 4.5GB |
| [dolphin-2.9.1-yi-1.5-9b.Q4_0.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q4_0.gguf) | Q4_0 | 4.69GB |
| [dolphin-2.9.1-yi-1.5-9b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.IQ4_NL.gguf) | IQ4_NL | 4.73GB |
| [dolphin-2.9.1-yi-1.5-9b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q4_K_S.gguf) | Q4_K_S | 4.72GB |
| [dolphin-2.9.1-yi-1.5-9b.Q4_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q4_K.gguf) | Q4_K | 4.96GB |
| [dolphin-2.9.1-yi-1.5-9b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q4_K_M.gguf) | Q4_K_M | 4.96GB |
| [dolphin-2.9.1-yi-1.5-9b.Q4_1.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q4_1.gguf) | Q4_1 | 5.19GB |
| [dolphin-2.9.1-yi-1.5-9b.Q5_0.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q5_0.gguf) | Q5_0 | 5.69GB |
| [dolphin-2.9.1-yi-1.5-9b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q5_K_S.gguf) | Q5_K_S | 5.69GB |
| [dolphin-2.9.1-yi-1.5-9b.Q5_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q5_K.gguf) | Q5_K | 5.83GB |
| [dolphin-2.9.1-yi-1.5-9b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q5_K_M.gguf) | Q5_K_M | 5.83GB |
| [dolphin-2.9.1-yi-1.5-9b.Q5_1.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q5_1.gguf) | Q5_1 | 6.19GB |
| [dolphin-2.9.1-yi-1.5-9b.Q6_K.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q6_K.gguf) | Q6_K | 6.75GB |
| [dolphin-2.9.1-yi-1.5-9b.Q8_0.gguf](https://huggingface.co/RichardErkhov/cognitivecomputations_-_dolphin-2.9.1-yi-1.5-9b-gguf/blob/main/dolphin-2.9.1-yi-1.5-9b.Q8_0.gguf) | Q8_0 | 8.74GB |
Original model description:
---
license: apache-2.0
base_model: 01-ai/Yi-1.5-9B
tags:
- generated_from_trainer
- axolotl
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
---
# Dolphin 2.9.1 Yi 1.5 9b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
This is our most spectacular outcome ever. FFT, all parameters, 16bit. 70.9 MMLU on 9b! And it talks like a dream.
Although the max positional embeddings is 4k, we used rope theta of 1000000.0 and we trained with sequence length 12k. We plan to train on the upcoming 32k version as well.
[](https://discord.gg/cognitivecomputations)
Discord: https://discord.gg/cognitivecomputations
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
Our appreciation for the sponsors of Dolphin 2.9.1:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node
- [OnDemand](https://on-demand.io/) - provided inference sponsorship
This model is based on Yi-1.5-9b, and is governed by apache 2.0 license.
The base model has 4k context, but we used rope theta of 1000000.0 and the full-weight fine-tuning was with 12k sequence length.
Dolphin 2.9.1 uses ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9.1 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.
## Evals
## Training
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: 01-ai/Yi-1.5-9B
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true
# load_in_8bit: false
# load_in_4bit: true
# strict: false
# adapter: qlora
# lora_modules_to_save: [embed_tokens, lm_head]
# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: True
# lora_fan_in_fan_out:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: yi34b
val_set_size: 0.03
output_dir: ./out-yi
sequence_len: 12000
sample_packing: true
pad_to_sequence_len: true
wandb_project: dolphin-2.9-yi-34b
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
# resume_from_checkpoint: /workspace/axolotl/dbrx-checkpoint
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 4
save_total_limit: 2
save_steps:
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
bos_token: "<|startoftext|>"
eos_token: "<|im_end|>"
pad_token: "<unk>"
unk_token: "<unk>"
tokens:
- "<|im_start|>"
```
</details><br>
# out-yi
This model is a fine-tuned version of [01-ai/Yi-1.5-9B](https://huggingface.co/01-ai/Yi-1.5-9B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4396
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6332 | 0.0024 | 1 | 0.6469 |
| 0.4811 | 0.2499 | 106 | 0.4739 |
| 0.4465 | 0.4997 | 212 | 0.4547 |
| 0.4472 | 0.7496 | 318 | 0.4480 |
| 0.4373 | 0.9994 | 424 | 0.4429 |
| 0.4147 | 1.2384 | 530 | 0.4432 |
| 0.3879 | 1.4882 | 636 | 0.4400 |
| 0.3872 | 1.7381 | 742 | 0.4371 |
| 0.4044 | 1.9879 | 848 | 0.4344 |
| 0.3509 | 2.2269 | 954 | 0.4410 |
| 0.3628 | 2.4767 | 1060 | 0.4401 |
| 0.3652 | 2.7266 | 1166 | 0.4397 |
| 0.3674 | 2.9764 | 1272 | 0.4396 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.2+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
|
SampleTheory/distilbert-base-uncased-finetuned-squad | SampleTheory | 2024-06-16T22:50:46Z | 125 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-06-16T22:35:29Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9122
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 63 | 1.8433 |
| No log | 2.0 | 126 | 1.8556 |
| No log | 3.0 | 189 | 1.9122 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
MaziyarPanahi/mergekit-passthrough-lkwyfft-GGUF | MaziyarPanahi | 2024-06-16T22:46:32Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:saishf/Fimbulvetr-Kuro-Lotus-10.7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-passthrough-lkwyfft",
"base_model:quantized:mergekit-community/mergekit-passthrough-lkwyfft"
] | text-generation | 2024-06-16T22:14:34Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- llama
- text-generation
- mergekit
- merge
- base_model:saishf/Fimbulvetr-Kuro-Lotus-10.7B
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-passthrough-lkwyfft-GGUF
base_model: mergekit-community/mergekit-passthrough-lkwyfft
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-passthrough-lkwyfft-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-passthrough-lkwyfft-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-passthrough-lkwyfft](https://huggingface.co/mergekit-community/mergekit-passthrough-lkwyfft)
## Description
[MaziyarPanahi/mergekit-passthrough-lkwyfft-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-passthrough-lkwyfft-GGUF) contains GGUF format model files for [mergekit-community/mergekit-passthrough-lkwyfft](https://huggingface.co/mergekit-community/mergekit-passthrough-lkwyfft).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [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.
* [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.
* [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.
* [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.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
MubarakB/b99Pw9770AfRtmJwV2i1 | MubarakB | 2024-06-16T22:22:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-06-16T22:22:22Z | ---
base_model: NousResearch/Llama-2-7b-chat-hf
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
tedcochran/llama3-8b-cosmic-fusion-dynamics-lora | tedcochran | 2024-06-16T22:21:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T21:55:31Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** tedcochran
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
niv6395/Reinforce-CartPole-v1 | niv6395 | 2024-06-16T22:20:20Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-16T22:20:16Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 222.20 +/- 56.16
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
gamallo/translator-gl-zh | gamallo | 2024-06-16T22:16:38Z | 1 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-06-16T18:44:02Z | ---
license: mit
---
**How to translate with this model**
+ Install [Python 3.9](https://www.python.org/downloads/release/python-390/) + ctranslate 2 + subword-nmt
```bash
pip install ctranslate2~=3.20.0
```
```bash
pip install subword-nmt
```
+ tokenization with BPE:
```bash
subword-nmt apply-bpe -c gl-detok10k.code < input_file.txt > input_file_bpe.txt
```
+ Translating an input_text using ct2_detok-gl-zh:
```bash
python3 trans_ct2.py ct2_detok-gl-zh input_file_bpe.txt >output_file_bpe.txt
```
+ DeBPEar output txt:
```bash
cat out_test_bpe.txt | sed "s/@@ //g" > output_file.txt
```
**Acknowledgments**
Thanks to Tang Waying, Zheng Jie and Wang Tianjiao for helping prepare the parallel corpora. |
yukioichida/RL_Class_Week1 | yukioichida | 2024-06-16T22:11:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-16T22:09:48Z | ---
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: 257.16 +/- 23.68
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
...
```
|
MaziyarPanahi/mergekit-slerp-idbupbn-GGUF | MaziyarPanahi | 2024-06-16T21:59:13Z | 11 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:amazingvince/Not-WizardLM-2-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-idbupbn",
"base_model:quantized:mergekit-community/mergekit-slerp-idbupbn"
] | text-generation | 2024-06-16T21:37:54Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- base_model:meta-llama/Meta-Llama-3-8B-Instruct
- base_model:amazingvince/Not-WizardLM-2-7B
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-idbupbn-GGUF
base_model: mergekit-community/mergekit-slerp-idbupbn
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-idbupbn-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-idbupbn-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-idbupbn](https://huggingface.co/mergekit-community/mergekit-slerp-idbupbn)
## Description
[MaziyarPanahi/mergekit-slerp-idbupbn-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-idbupbn-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-idbupbn](https://huggingface.co/mergekit-community/mergekit-slerp-idbupbn).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [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.
* [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.
* [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.
* [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.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
yhzhai/mcm | yhzhai | 2024-06-16T21:57:24Z | 0 | 4 | null | [
"safetensors",
"text-to-video",
"arxiv:2406.06890",
"license:apache-2.0",
"region:us"
] | text-to-video | 2024-06-15T16:48:58Z | ---
license: apache-2.0
pipeline_tag: text-to-video
---
<h1 align="center">
<a href="https://yhzhai.github.io/mcm/"><b>Motion Consistency Model: Accelerating Video Diffusion with Disentangled Motion-Appearance Distillation</b></a>
</h1>
[[Project page]](https://yhzhai.github.io/mcm/) [[Code]](https://github.com/yhZhai/mcm) [[arXiv]](https://arxiv.org/abs/2406.06890) [[Demo]](https://huggingface.co/spaces/yhzhai/mcm) [[Colab]](https://colab.research.google.com/drive/1ouGbIZA5092hF9ZMHO-AchCr_L3algTL?usp=sharing)
**TL;DR**: Our motion consistency model not only accelerates text2video diffusion model sampling process, but also can benefit from an additional high-quality image dataset to improve the frame quality of generated videos.
## Usage
```python
from typing import Optional
import torch
from diffusers import (
AnimateDiffPipeline,
DiffusionPipeline,
LCMScheduler,
MotionAdapter,
)
from diffusers.utils import export_to_video
from peft import PeftModel
def main():
# select model_path from ["animatediff-laion", "animatediff-webvid",
# "modelscopet2v-webvid", "modelscopet2v-laion", "modelscopet2v-anime",
# "modelscopet2v-real", "modelscopet2v-3d-cartoon"]
model_path = "modelscopet2v-laion"
prompts = ["A cat walking on a treadmill", "A dog walking on a treadmill"]
num_inference_steps = 4
model_id = "yhzhai/mcm"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if "animatediff" in model_path:
pipeline = get_animatediff_pipeline()
elif "modelscope" in model_path:
pipeline = get_modelscope_pipeline()
else:
raise ValueError(f"Unknown pipeline {model_path}")
lora = PeftModel.from_pretrained(
pipeline.unet,
model_id,
subfolder=model_path,
adapter_name="pretrained_lora",
torch_device="cpu",
)
lora.merge_and_unload()
pipeline.unet = lora
pipeline = pipeline.to(device)
output = pipeline(
prompt=prompts,
num_frames=16,
guidance_scale=1.0,
num_inference_steps=num_inference_steps,
generator=torch.Generator("cpu").manual_seed(42),
).frames
if not isinstance(output, list):
output = [output[i] for i in range(output.shape[0])]
for j in range(len(prompts)):
export_to_video(
output[j],
f"{j}-{model_path}.mp4",
fps=7,
)
def get_animatediff_pipeline(
real_variant: Optional[str] = "realvision",
motion_module_path: str = "guoyww/animatediff-motion-adapter-v1-5-2",
):
if real_variant is None:
model_id = "runwayml/stable-diffusion-v1-5"
elif real_variant == "epicrealism":
model_id = "emilianJR/epiCRealism"
elif real_variant == "realvision":
model_id = "SG161222/Realistic_Vision_V6.0_B1_noVAE"
else:
raise ValueError(f"Unknown real_variant {real_variant}")
adapter = MotionAdapter.from_pretrained(
motion_module_path, torch_dtype=torch.float16
)
pipe = AnimateDiffPipeline.from_pretrained(
model_id,
motion_adapter=adapter,
torch_dtype=torch.float16,
)
scheduler = LCMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
timestep_scaling=4.0,
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
)
pipe.scheduler = scheduler
pipe.enable_vae_slicing()
return pipe
def get_modelscope_pipeline():
model_id = "ali-vilab/text-to-video-ms-1.7b"
pipe = DiffusionPipeline.from_pretrained(
model_id, torch_dtype=torch.float16, variant="fp16"
)
scheduler = LCMScheduler.from_pretrained(
model_id,
subfolder="scheduler",
timestep_scaling=4.0,
)
pipe.scheduler = scheduler
pipe.enable_vae_slicing()
return pipe
if __name__ == "__main__":
main()
``` |
louistichelman/controlnet_streetview_normalmap_res400 | louistichelman | 2024-06-16T21:56:04Z | 2 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-06-16T18:11:40Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- 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. -->
# controlnet-louistichelman/controlnet_streetview_normalmap_res400
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: A realistic google streetview image, which was assigned a beauty-score of 16.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
prompt: A realistic google streetview image, which was assigned a beauty-score of 35.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
prompt: A realistic google streetview image, which was assigned a beauty-score of 16.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
prompt: A realistic google streetview image, which was assigned a beauty-score of 35.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
prompt: A realistic google streetview image, which was assigned a beauty-score of 23.188663, where scores are between 10 and 40 and higher scores indicate more beauty.
prompt: A realistic google streetview image, which was assigned a beauty-score of 31.616573, where scores are between 10 and 40 and higher scores indicate more beauty.
## 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] |
Marco127/llamantino_hodi_requalification | Marco127 | 2024-06-16T21:45:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T19:28:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Americo/uma_model_pretrained_4bit | Americo | 2024-06-16T21:44:34Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-06-16T21:43:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Cas-Warehouse/Llama-3-Depressed-Therapist-8B | Cas-Warehouse | 2024-06-16T21:43:52Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B",
"base_model:merge:Cas-Warehouse/Llama-3-SOVL-MopeyMule-Blackroot-8B",
"base_model:PrahmodhRaj/Llama-3_Psychiatrist_Chat",
"base_model:merge:PrahmodhRaj/Llama-3_Psychiatrist_Chat",
"base_model:zementalist/llama-3-8B-chat-psychotherapist",
"base_model:merge:zementalist/llama-3-8B-chat-psychotherapist",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-16T21:35:07Z | ---
base_model:
- Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B
- zementalist/llama-3-8B-chat-psychotherapist
- Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B
- PrahmodhRaj/Llama-3_Psychiatrist_Chat
- Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B](https://huggingface.co/Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B) as a base.
### Models Merged
The following models were included in the merge:
* [Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B](https://huggingface.co/Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B) + [zementalist/llama-3-8B-chat-psychotherapist](https://huggingface.co/zementalist/llama-3-8B-chat-psychotherapist)
* [Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B](https://huggingface.co/Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B) + [PrahmodhRaj/Llama-3_Psychiatrist_Chat](https://huggingface.co/PrahmodhRaj/Llama-3_Psychiatrist_Chat)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B+PrahmodhRaj/Llama-3_Psychiatrist_Chat
- model: Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B+zementalist/llama-3-8B-chat-psychotherapist
merge_method: model_stock
base_model: Casual-Autopsy/Llama-3-SOVL-MopeyMule-Blackroot-8B
dtype: bfloat16
```
|
Fawazzx/alzheimer_classification_using_resnet50_finetuned | Fawazzx | 2024-06-16T21:37:29Z | 0 | 0 | null | [
"region:us"
] | null | 2024-06-16T18:30:25Z | # Fine-Tuning ResNet50 for Alzheimer's MRI Classification
This repository contains a Jupyter Notebook for fine-tuning a ResNet50 model to classify Alzheimer's disease stages from MRI images. The notebook uses PyTorch and the dataset is loaded from the Hugging Face Datasets library.
## Table of Contents
- [Introduction](#introduction)
- [Dataset](#dataset)
- [Model Architecture](#model-architecture)
- [Setup](#setup)
- [Training](#training)
- [Evaluation](#evaluation)
- [Usage](#usage)
- [Results](#results)
- [Contributing](#contributing)
- [License](#license)
## Introduction
This notebook fine-tunes a pre-trained ResNet50 model to classify MRI images into one of four stages of Alzheimer's disease:
- Mild Demented
- Moderate Demented
- Non-Demented
- Very Mild Demented
## Dataset
The dataset used is [Falah/Alzheimer_MRI](https://huggingface.co/datasets/Falah/Alzheimer_MRI) from the Hugging Face Datasets library. It consists of MRI images categorized into the four stages of Alzheimer's disease.
## Model Architecture
The model architecture is based on ResNet50. The final fully connected layer is modified to output predictions for 4 classes.
## Setup
To run the notebook locally, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/your_username/alzheimer_mri_classification.git
cd alzheimer_mri_classification
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Open the notebook:
```bash
jupyter notebook fine-tuning.ipynb
```
## Training
The notebook includes sections for:
- Loading and preprocessing the dataset
- Defining the model architecture
- Setting up the training loop with a learning rate scheduler and optimizer
- Training the model for a specified number of epochs
- Saving the trained model weights
## Evaluation
The notebook includes a section for evaluating the trained model on the validation set. It calculates and prints the validation loss and accuracy.
## Usage
Once trained, the model can be saved and used for inference on new MRI images. The trained model weights are saved as alzheimer_model_resnet50.pth.
## Load the model architecture and weights
```python
model = models.resnet50(weights=None)
model.fc = nn.Linear(model.fc.in_features, 4)
model.load_state_dict(torch.load("alzheimer_model_resnet50.pth", map_location=torch.device('cpu')))
model.eval()
```
## Results
The model achieved an accuracy of 95.9375% on the validation set.
## Contributing
Contributions are welcome! If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request. |
numen-tech/Hermes-2-Theta-Llama-3-8B-w4a16g128asym | numen-tech | 2024-06-16T21:36:57Z | 0 | 0 | null | [
"arxiv:2308.13137",
"license:apache-2.0",
"region:us"
] | null | 2024-06-16T21:33:13Z | ---
license: apache-2.0
---
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Hermes-2-Theta-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Theta-Llama-3-8B).
|
mradermacher/Hermes-Sthero-v1-GGUF | mradermacher | 2024-06-16T21:31:30Z | 11 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:lik07/Hermes-Sthero-v1",
"base_model:quantized:lik07/Hermes-Sthero-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-16T21:03:31Z | ---
base_model: lik07/Hermes-Sthero-v1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/lik07/Hermes-Sthero-v1
<!-- 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/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Hermes-Sthero-v1-GGUF/resolve/main/Hermes-Sthero-v1.f16.gguf) | f16 | 16.2 | 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 -->
|
Meforgers/Aixr | Meforgers | 2024-06-16T21:26:19Z | 10 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"Generative AI",
"text-generation-inference",
"text-generation",
"peft",
"unsloth",
"medical",
"biology",
"code",
"space",
"conversational",
"tr",
"en",
"es",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-12T09:22:20Z | ---
language:
- tr
- en
- es
license: apache-2.0
library_name: transformers
tags:
- Generative AI
- text-generation-inference
- text-generation
- peft
- unsloth
- medical
- biology
- code
- space
---
# Model Trained By Meforgers
*This model, named 'Aixr,' is designed for science and artificial intelligence development. You can use it as the foundation for many of your scientific projects and interesting ideas. In short, Aixr is an artificial intelligence model that is based on futurism and innovation.*
- # *Firstly*
-If you intend to use unsloth with Pytorch 1.3.0: Utilize the "ampere" path for newer RTX 30xx GPUs or higher.
```python
pip install "unsloth[cu118-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu118-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
pip install "unsloth[cu121-ampere-torch230] @ git+https://github.com/unslothai/unsloth.git"
```
-Also you can use another system
- # *Usage*
```python
from unsloth import FastLanguageModel
import torch
# Variable side
max_seq_length = 512
dtype = torch.float16
load_in_4bit = True
# Alpaca prompt
alpaca_prompt = """### Instruction:
{0}
### Input:
{1}
### Response:
{2}
"""
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="Meforgers/Aixr",
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
FastLanguageModel.for_inference(model)
inputs = tokenizer(
[
alpaca_prompt.format(
"Can u text me basic python code?", # instruction side (You need to change that side)
"", # input
"", # output - leave this blank for generation!
)
],
return_tensors="pt"
).to("cuda")
outputs = model.generate(**inputs, max_new_tokens=128, use_cache=True)
print(tokenizer.batch_decode(outputs))
``` |
RamtinMoslemi/poca-SoccerTwos | RamtinMoslemi | 2024-06-16T21:18:46Z | 172 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2024-06-16T21:18:32Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: RamtinMoslemi/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
iambestfeed/vi-roberta-small-init-wseg | iambestfeed | 2024-06-16T21:10:07Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-06-16T21:09:31Z | ---
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] |
iambestfeed/vi-roberta-tiny-init-wseg | iambestfeed | 2024-06-16T21:09:55Z | 98 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-06-16T21:09:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Poro-34B-chat-GGUF | mradermacher | 2024-06-16T21:03:30Z | 44 | 0 | transformers | [
"transformers",
"gguf",
"fi",
"en",
"dataset:LumiOpen/instruction-collection-fin",
"base_model:LumiOpen/Poro-34B-chat",
"base_model:quantized:LumiOpen/Poro-34B-chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-16T14:23:57Z | ---
base_model: LumiOpen/Poro-34B-chat
datasets:
- LumiOpen/instruction-collection-fin
language:
- fi
- 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/LumiOpen/Poro-34B-chat
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Poro-34B-chat-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/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q2_K.gguf) | Q2_K | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.IQ3_XS.gguf) | IQ3_XS | 15.2 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.IQ3_S.gguf) | IQ3_S | 15.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q3_K_S.gguf) | Q3_K_S | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.IQ3_M.gguf) | IQ3_M | 17.2 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q3_K_M.gguf) | Q3_K_M | 18.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.IQ4_XS.gguf) | IQ4_XS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q3_K_L.gguf) | Q3_K_L | 20.3 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q4_K_S.gguf) | Q4_K_S | 20.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q4_K_M.gguf) | Q4_K_M | 22.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q5_K_S.gguf) | Q5_K_S | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q5_K_M.gguf) | Q5_K_M | 26.2 | |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q6_K.gguf) | Q6_K | 28.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Poro-34B-chat-GGUF/resolve/main/Poro-34B-chat.Q8_0.gguf) | Q8_0 | 37.4 | 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 -->
|
ale045/kaggle_math_model_v8 | ale045 | 2024-06-16T20:50:12Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:ale045/kaggle_math_model_v7",
"base_model:finetune:ale045/kaggle_math_model_v7",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-13T05:42:05Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: ale045/kaggle_math_model_v7
---
# Uploaded model
- **Developed by:** ale045
- **License:** apache-2.0
- **Finetuned from model :** ale045/kaggle_math_model_v7
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)
|
sharad31/my_model | sharad31 | 2024-06-16T20:12:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T20:11:37Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** sharad31
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Yuseifer/Reinforce_model-cartpole | Yuseifer | 2024-06-16T20:10:22Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-16T20:10:13Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce_model-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 467.80 +/- 96.60
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
evitalyst/ChatMe | evitalyst | 2024-06-16T20:09:57Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-16T20:09:57Z | ---
license: apache-2.0
---
|
alexionby/south_park_lora_v1-6 | alexionby | 2024-06-16T20:06:26Z | 6 | 1 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"diffusers-training",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-06-16T19:17:50Z | ---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- diffusers-training
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'Master Yoda in the style of <s0><s1>'
output:
url:
"image_0.png"
- text: 'Master Yoda in the style of <s0><s1>'
output:
url:
"image_1.png"
- text: 'Master Yoda in the style of <s0><s1>'
output:
url:
"image_2.png"
- text: 'Master Yoda in the style of <s0><s1>'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: in the style of <s0><s1>
license: openrail++
---
# SDXL LoRA DreamBooth - alexionby/south_park_lora_v1-6
<Gallery />
## Model description
### These are alexionby/south_park_lora_v1-6 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`south_park_lora_v1-6.safetensors` here 💾](/alexionby/south_park_lora_v1-6/blob/main/south_park_lora_v1-6.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:south_park_lora_v1-6:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`south_park_lora_v1-6_emb.safetensors` here 💾](/alexionby/south_park_lora_v1-6/blob/main/south_park_lora_v1-6_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `south_park_lora_v1-6_emb` to your prompt. For example, `in the style of south_park_lora_v1-6_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('alexionby/south_park_lora_v1-6', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='alexionby/south_park_lora_v1-6', filename='south_park_lora_v1-6_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('Master Yoda in the style of <s0><s1>').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)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/alexionby/south_park_lora_v1-6/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
DankCloth/Output | DankCloth | 2024-06-16T19:58:49Z | 6 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"sd3",
"sd3-diffusers",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-3-medium-diffusers",
"base_model:finetune:stabilityai/stable-diffusion-3-medium-diffusers",
"license:openrail++",
"region:us"
] | text-to-image | 2024-06-16T19:22:26Z | ---
base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: openrail++
tags:
- text-to-image
- diffusers-training
- diffusers
- sd3
- sd3-diffusers
- template:sd-lora
instance_prompt: a photo of owen wilson
widget: []
---
<!-- 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. -->
# SD3 DreamBooth LoRA - DankCloth/Output
<Gallery />
## Model description
These are DankCloth/Output DreamBooth weights for stabilityai/stable-diffusion-3-medium-diffusers.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
## Trigger words
You should use a photo of owen wilson to trigger the image generation.
## Download model
[Download](DankCloth/Output/tree/main) them in the Files & versions tab.
## License
Please adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)`.
## 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] |
veronoicc/sandai | veronoicc | 2024-06-16T19:57:47Z | 196 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-06-16T19:00:19Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: sandai
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7142857313156128
---
# sandai
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### cat
#### orange cat
#### sand cat
 |
Gilchrist/camembert-base-finetuned-piaf | Gilchrist | 2024-06-16T19:55:14Z | 123 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"camembert",
"question-answering",
"generated_from_trainer",
"dataset:piaf",
"base_model:almanach/camembert-base",
"base_model:finetune:almanach/camembert-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-06-15T11:04:05Z | ---
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
datasets:
- piaf
model-index:
- name: camembert-base-finetuned-piaf
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. -->
# camembert-base-finetuned-piaf
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the piaf dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
stojchet/python-sft-r64-a16-d0.05-e3 | stojchet | 2024-06-16T19:54:05Z | 1 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:deepseek-ai/deepseek-coder-1.3b-base",
"base_model:adapter:deepseek-ai/deepseek-coder-1.3b-base",
"license:other",
"region:us"
] | null | 2024-06-16T19:53:59Z | ---
base_model: deepseek-ai/deepseek-coder-1.3b-base
datasets:
- generator
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: python-sft-r64-a16-d0.05-e3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/stojchets/huggingface/runs/rmvtpvu9)
# python-sft-r64-a16-d0.05-e3
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator 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: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.42.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |
anamikac2708/Llama3-8b-finetuned-investopedia-q8_0_gguf | anamikac2708 | 2024-06-16T19:47:51Z | 10 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-15T07:14:04Z | ---
language:
- en
license: cc-by-nc-4.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** anamikac2708
- **License:** cc-by-nc-4.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library using open-sourced finance dataset https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset developed for finance application by FinLang Team
Model then converted Q8_0 gguf using llama.cpp https://github.com/ggerganov/llama.cpp/. This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses.
## How to Get Started with the Model
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
1. ## Install llama-cpp-python:
```
! CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
```
2. ## Run the model
```python
from transformers import AutoTokenizer
from llama_cpp import Llama
tokenizer = AutoTokenizer.from_pretrained('meta-llama/Meta-Llama-3-8B')
example = [{'content': 'You are a financial expert and you can answer any questions related to finance. You will be given a context and a question. Understand the given context and\n try to answer. Users will ask you questions in English and you will generate answer based on the provided CONTEXT.\n CONTEXT:\n D. in Forced Migration from the University of the Witwatersrand (Wits) in Johannesburg, South Africa; A postgraduate diploma in Folklore & Cultural Studies at Indira Gandhi National Open University (IGNOU) in New Delhi, India; A Masters of International Affairs at Columbia University; A BA from Barnard College at Columbia University\n', 'role': 'system'}, {'content': ' In which universities did the individual obtain their academic qualifications?\n', 'role': 'user'}, {'content': ' University of the Witwatersrand (Wits) in Johannesburg, South Africa; Indira Gandhi National Open University (IGNOU) in New Delhi, India; Columbia University; Barnard College at Columbia University.', 'role': 'assistant'}]
prompt = tokenizer.apply_chat_template(example[:2], tokenize=False, add_generation_prompt=True)
llm = Llama.from_pretrained(
repo_id="anamikac2708/Llama3-8b-finetuned-investopedia-q8_0_gguf",
filename="*Q8_0.gguf",
verbose=False
)
output = llm(
prompt,
max_tokens=256, # Generate up to 256 tokens
stop=["<|im_end|>"],
echo=True, # Whether to echo the prompt
)
print(output['choices'][0]['text'])
```
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Coming soon!
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This 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 into ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## License
Since non-commercial datasets are used for fine-tuning, we release this model as cc-by-nc-4.0. |
anamikac2708/Gemma-7b-finetuned-investopedia-q4_k_m_gguf | anamikac2708 | 2024-06-16T19:45:55Z | 2 | 0 | transformers | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:quantized:unsloth/gemma-7b-bnb-4bit",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-15T08:12:14Z | ---
language:
- en
license: cc-by-nc-4.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- gguf
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** anamikac2708
- **License:** cc-by-nc-4.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This Gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library using open-sourced finance dataset https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset developed for finance application by FinLang Team
Model then converted Q4_K_M gguf using llama.cpp https://github.com/ggerganov/llama.cpp/. This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses.
## How to Get Started with the Model
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
1. ## Install llama-cpp-python:
```
! CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
```
2. ## Run the model
```python
from transformers import AutoTokenizer
from llama_cpp import Llama
tokenizer = AutoTokenizer.from_pretrained('google/gemma-7b')
example = [{'content': 'You are a financial expert and you can answer any questions related to finance. You will be given a context and a question. Understand the given context and\n try to answer. Users will ask you questions in English and you will generate answer based on the provided CONTEXT.\n CONTEXT:\n D. in Forced Migration from the University of the Witwatersrand (Wits) in Johannesburg, South Africa; A postgraduate diploma in Folklore & Cultural Studies at Indira Gandhi National Open University (IGNOU) in New Delhi, India; A Masters of International Affairs at Columbia University; A BA from Barnard College at Columbia University\n', 'role': 'system'}, {'content': ' In which universities did the individual obtain their academic qualifications?\n', 'role': 'user'}, {'content': ' University of the Witwatersrand (Wits) in Johannesburg, South Africa; Indira Gandhi National Open University (IGNOU) in New Delhi, India; Columbia University; Barnard College at Columbia University.', 'role': 'assistant'}]
prompt = tokenizer.apply_chat_template(example[:2], tokenize=False, add_generation_prompt=True)
llm = Llama.from_pretrained(
repo_id="anamikac2708/Gemma-7b-finetuned-investopedia-q4_k_m_gguf",
filename="*Q4_K_M.gguf",
verbose=False
)
output = llm(
prompt,
max_tokens=256, # Generate up to 256 tokens
stop=["<|im_end|>"],
echo=True, # Whether to echo the prompt
)
print(output['choices'][0]['text'])
```
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Coming soon!
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This 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 into ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## License
Since non-commercial datasets are used for fine-tuning, we release this model as cc-by-nc-4.0. |
anamikac2708/Gemma-7b-finetuned-investopedia-q5_k_m_gguf | anamikac2708 | 2024-06-16T19:45:15Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:quantized:unsloth/gemma-7b-bnb-4bit",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-15T08:17:58Z | ---
language:
- en
license: cc-by-nc-4.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- gguf
base_model: unsloth/gemma-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** anamikac2708
- **License:** cc-by-nc-4.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This Gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library using open-sourced finance dataset https://huggingface.co/datasets/FinLang/investopedia-instruction-tuning-dataset developed for finance application by FinLang Team
Model then converted Q5_K_M gguf using llama.cpp https://github.com/ggerganov/llama.cpp/. This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses.
## How to Get Started with the Model
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
1. ## Install llama-cpp-python:
```
! CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
```
2. ## Run the model
```python
from transformers import AutoTokenizer
from llama_cpp import Llama
tokenizer = AutoTokenizer.from_pretrained('google/gemma-7b')
example = [{'content': 'You are a financial expert and you can answer any questions related to finance. You will be given a context and a question. Understand the given context and\n try to answer. Users will ask you questions in English and you will generate answer based on the provided CONTEXT.\n CONTEXT:\n D. in Forced Migration from the University of the Witwatersrand (Wits) in Johannesburg, South Africa; A postgraduate diploma in Folklore & Cultural Studies at Indira Gandhi National Open University (IGNOU) in New Delhi, India; A Masters of International Affairs at Columbia University; A BA from Barnard College at Columbia University\n', 'role': 'system'}, {'content': ' In which universities did the individual obtain their academic qualifications?\n', 'role': 'user'}, {'content': ' University of the Witwatersrand (Wits) in Johannesburg, South Africa; Indira Gandhi National Open University (IGNOU) in New Delhi, India; Columbia University; Barnard College at Columbia University.', 'role': 'assistant'}]
prompt = tokenizer.apply_chat_template(example[:2], tokenize=False, add_generation_prompt=True)
llm = Llama.from_pretrained(
repo_id="anamikac2708/Gemma-7b-finetuned-investopedia-q5_k_m_gguf",
filename="*Q5_K_M.gguf",
verbose=False
)
output = llm(
prompt,
max_tokens=256, # Generate up to 256 tokens
stop=["<|im_end|>"],
echo=True, # Whether to echo the prompt
)
print(output['choices'][0]['text'])
```
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Coming soon!
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
This 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 into ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## License
Since non-commercial datasets are used for fine-tuning, we release this model as cc-by-nc-4.0. |
T-ZERO/first_prototype | T-ZERO | 2024-06-16T19:44:23Z | 0 | 0 | flair | [
"flair",
"legal",
"text-generation",
"fa",
"dataset:OpenGVLab/ShareGPT-4o",
"license:llama3",
"region:us"
] | text-generation | 2024-06-16T19:38:26Z | ---
license: llama3
datasets:
- OpenGVLab/ShareGPT-4o
language:
- fa
metrics:
- character
library_name: flair
pipeline_tag: text-generation
tags:
- legal
--- |
Mortello/q-Taxi-v3 | Mortello | 2024-06-16T19:43:32Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-16T19:43:31Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.74
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="Mortello/q-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"])
```
|
droo303/distilbert-finetuned-squad | droo303 | 2024-06-16T19:42:02Z | 124 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-06-16T19:41:11Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on SQuAD dataset
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Ilya-Nazimov/rubert-tiny2-odonata-f3-ner | Ilya-Nazimov | 2024-06-16T19:29:35Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:cointegrated/rubert-tiny2",
"base_model:finetune:cointegrated/rubert-tiny2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-06-16T18:41:55Z | ---
license: mit
base_model: cointegrated/rubert-tiny2
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: rubert-tiny2-odonata-f3-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rubert-tiny2-odonata-f3-ner
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0188
- Precision: 0.6653
- Recall: 0.6157
- F1: 0.6395
- Accuracy: 0.9944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 32 | 0.1309 | 0.0 | 0.0 | 0.0 | 0.9903 |
| No log | 2.0 | 64 | 0.0672 | 0.0 | 0.0 | 0.0 | 0.9903 |
| No log | 3.0 | 96 | 0.0623 | 0.0 | 0.0 | 0.0 | 0.9903 |
| No log | 4.0 | 128 | 0.0576 | 0.0 | 0.0 | 0.0 | 0.9903 |
| No log | 5.0 | 160 | 0.0488 | 0.0 | 0.0 | 0.0 | 0.9903 |
| No log | 6.0 | 192 | 0.0353 | 0.0 | 0.0 | 0.0 | 0.9903 |
| No log | 7.0 | 224 | 0.0288 | 0.7921 | 0.5529 | 0.6513 | 0.9935 |
| No log | 8.0 | 256 | 0.0256 | 0.7987 | 0.4824 | 0.6015 | 0.9931 |
| No log | 9.0 | 288 | 0.0235 | 0.7975 | 0.5098 | 0.6220 | 0.9933 |
| No log | 10.0 | 320 | 0.0221 | 0.7310 | 0.5647 | 0.6372 | 0.9938 |
| No log | 11.0 | 352 | 0.0212 | 0.6912 | 0.5529 | 0.6144 | 0.9938 |
| No log | 12.0 | 384 | 0.0205 | 0.6746 | 0.5529 | 0.6078 | 0.9937 |
| No log | 13.0 | 416 | 0.0201 | 0.6774 | 0.5765 | 0.6229 | 0.9938 |
| No log | 14.0 | 448 | 0.0196 | 0.6712 | 0.5843 | 0.6247 | 0.9940 |
| No log | 15.0 | 480 | 0.0194 | 0.6581 | 0.6039 | 0.6299 | 0.9941 |
| 0.0722 | 16.0 | 512 | 0.0192 | 0.6681 | 0.6 | 0.6322 | 0.9942 |
| 0.0722 | 17.0 | 544 | 0.0190 | 0.6624 | 0.6078 | 0.6339 | 0.9943 |
| 0.0722 | 18.0 | 576 | 0.0189 | 0.6542 | 0.6157 | 0.6343 | 0.9943 |
| 0.0722 | 19.0 | 608 | 0.0188 | 0.6624 | 0.6157 | 0.6382 | 0.9944 |
| 0.0722 | 20.0 | 640 | 0.0188 | 0.6653 | 0.6157 | 0.6395 | 0.9944 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cpu
- Datasets 2.19.2
- Tokenizers 0.19.1
|
MaziyarPanahi/mergekit-slerp-jlrpbqb-GGUF | MaziyarPanahi | 2024-06-16T19:21:36Z | 4 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02",
"base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"base_model:mergekit-community/mergekit-slerp-jlrpbqb",
"base_model:quantized:mergekit-community/mergekit-slerp-jlrpbqb"
] | text-generation | 2024-06-16T18:59:40Z | ---
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- mergekit
- merge
- conversational
- base_model:cognitivecomputations/dolphin-2.8-mistral-7b-v02
- base_model:arcee-ai/sec-mistral-7b-instruct-1.6-epoch
- autotrain_compatible
- endpoints_compatible
- text-generation-inference
- region:us
- text-generation
model_name: mergekit-slerp-jlrpbqb-GGUF
base_model: mergekit-community/mergekit-slerp-jlrpbqb
inference: false
model_creator: mergekit-community
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/mergekit-slerp-jlrpbqb-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-jlrpbqb-GGUF)
- Model creator: [mergekit-community](https://huggingface.co/mergekit-community)
- Original model: [mergekit-community/mergekit-slerp-jlrpbqb](https://huggingface.co/mergekit-community/mergekit-slerp-jlrpbqb)
## Description
[MaziyarPanahi/mergekit-slerp-jlrpbqb-GGUF](https://huggingface.co/MaziyarPanahi/mergekit-slerp-jlrpbqb-GGUF) contains GGUF format model files for [mergekit-community/mergekit-slerp-jlrpbqb](https://huggingface.co/mergekit-community/mergekit-slerp-jlrpbqb).
### 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.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [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.
* [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.
* [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.
* [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.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
thiagodepaulo/mT5-pt-tca | thiagodepaulo | 2024-06-16T19:17:07Z | 108 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"text-generation-inference",
"pt",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-06-16T18:35:24Z | ---
license: mit
language:
- pt
tags:
- text-generation-inference
widget:
- text: "E disse-lhe Pedro: Enéias, Jesus Cristo te dá saúde"
--- |
silent666/Qwen-Qwen1.5-7B-1718564795 | silent666 | 2024-06-16T19:06:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | 2024-06-16T19:06:35Z | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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SilvioLima/absa_treinamento_2 | SilvioLima | 2024-06-16T19:05:14Z | 149 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-16T18:15:40Z | ---
library_name: transformers
tags: []
---
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silent666/Qwen-Qwen1.5-7B-1718564678 | silent666 | 2024-06-16T19:04:41Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | 2024-06-16T19:04:39Z | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
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VlZaigraev/lct24model_ep2 | VlZaigraev | 2024-06-16T19:03:36Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-06-16T18:02:21Z | ---
library_name: transformers
tags: []
---
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silent666/Qwen-Qwen1.5-7B-1718564552 | silent666 | 2024-06-16T19:02:34Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | 2024-06-16T19:02:32Z | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
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numen-tech/Llama-3-WhiteRabbitNeo-8B-v2.0-w3a16g40sym | numen-tech | 2024-06-16T19:00:26Z | 0 | 0 | null | [
"arxiv:2308.13137",
"license:llama3",
"region:us"
] | null | 2024-06-16T18:56:01Z | ---
license: llama3
---
3-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0).
|
silent666/Qwen-Qwen1.5-7B-1718564289 | silent666 | 2024-06-16T18:58:11Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | 2024-06-16T18:58:09Z | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
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ripper479/rust_llm | ripper479 | 2024-06-16T18:54:29Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-16T18:44:42Z | ---
library_name: transformers
tags: []
---
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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Niggendar/4thTailHentaiModel_v040 | Niggendar | 2024-06-16T18:53:57Z | 111 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-16T18:46:09Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- 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
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[More Information Needed]
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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).
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[More Information Needed] |
silent666/Qwen-Qwen1.5-7B-1718564027 | silent666 | 2024-06-16T18:53:49Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | 2024-06-16T18:53:47Z | ---
base_model: Qwen/Qwen1.5-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[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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### Framework versions
- PEFT 0.11.1 |
n-akimasa/ppo-LunarLander-v2 | n-akimasa | 2024-06-16T18:50:56Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-16T18:50:39Z | ---
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: 270.12 +/- 17.98
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
...
```
|
alvdansen/manga-soup | alvdansen | 2024-06-16T18:46:41Z | 2,745 | 15 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-06-16T18:46:32Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
A cyborg girl with metallic limbs and a holographic interface projected from
her wrist, wearing a sleek, silver bodysuit
parameters:
negative_prompt: bad, messy, ugly
output:
url: images/ComfyUI_01858_.png
- text: A woman with bright pink hair styled in a bob cut
output:
url: images/ComfyUI_01846_.png
- text: >-
A young man with tousled brown hair and green eyes, wearing a casual hoodie
and jeans, sitting at a coffee shop with a laptop
parameters:
negative_prompt: bad, messy, ugly
output:
url: images/ComfyUI_01861_.png
- text: A tiny dragon with butterfly wings, perched on a daisy
output:
url: images/ComfyUI_01863_.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: creativeml-openrail-m
---
# Manga Soup
<Gallery />
## Model description
Honestly, just another variation on a cartoon style that has some anime and manga inspo behind it. I think this one does better with shorter prompts.
This is for fun and research - if you would like commercial access please contact me.
## Download model
Weights for this model are available in Safetensors format.
[Download](/alvdansen/manga-soup/tree/main) them in the Files & versions tab.
|
disertatieNic/whisper-small-transcriere-vorbire-in-scris | disertatieNic | 2024-06-16T18:40:42Z | 80 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-06-16T18:38:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
alvdansen/lofi-cuties | alvdansen | 2024-06-16T18:39:27Z | 947 | 12 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-06-16T18:39:18Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
A young man with tousled brown hair and green eyes, wearing a casual hoodie
and jeans, sitting at a coffee shop
output:
url: images/ComfyUI_01839_.png
- text: A young elf girl with big, bright eyes and a flower crown
output:
url: images/ComfyUI_01837_.png
- text: A woman with bright pink hair styled in a bob cut
output:
url: images/ComfyUI_01829_.png
- text: a cyborg girl
output:
url: images/ComfyUI_01842_.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: creativeml-openrail-m
---
# Lofi Cuties
<Gallery />
## Model description
A minimalist cartoon style that I think would work well for web comics. It really handles linework well.
## Download model
Weights for this model are available in Safetensors format.
[Download](/alvdansen/lofi-cuties/tree/main) them in the Files & versions tab.
|
bfrenan/Llama3-log-to-ttp-merged-2 | bfrenan | 2024-06-16T18:39:19Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-06-16T18:34:55Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** bfrenan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ashishkj23/my_awesome_qa_model | ashishkj23 | 2024-06-16T18:34:48Z | 125 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-06-15T21:50:33Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
base_model: distilbert-base-uncased
model-index:
- name: my_awesome_qa_model
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. -->
# my_awesome_qa_model
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: 1.5477
- Validation Loss: 1.7788
- 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': 500, '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 | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4904 | 2.2145 | 0 |
| 1.8153 | 1.7788 | 1 |
| 1.5477 | 1.7788 | 2 |
### Framework versions
- Transformers 4.41.2
- TensorFlow 2.15.0
- Datasets 2.20.0
- Tokenizers 0.19.1
|
disertatieNic/whisper-small-transcriere-vorbire-in-scris-adapters | disertatieNic | 2024-06-16T18:31:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-14T18:17:11Z | ---
library_name: transformers
tags: []
---
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Niggendar/dmecV1_v10 | Niggendar | 2024-06-16T18:30:21Z | 127 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-16T18:23:54Z | ---
library_name: diffusers
---
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bfrenan/Llama3-log-to-ttp-tokenizer_2 | bfrenan | 2024-06-16T18:29:48Z | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-06-16T18:29:47Z | ---
library_name: transformers
tags:
- unsloth
---
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MrezaPRZ/codellama_bird_train | MrezaPRZ | 2024-06-16T18:29:33Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-15T16:20:37Z | ---
library_name: transformers
tags: []
---
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vcadillo/glm-4v-9b-4-bits | vcadillo | 2024-06-16T18:28:09Z | 48 | 4 | transformers | [
"transformers",
"safetensors",
"chatglm",
"feature-extraction",
"custom_code",
"arxiv:2210.02414",
"arxiv:2311.03079",
"4-bit",
"bitsandbytes",
"region:us"
] | feature-extraction | 2024-06-08T01:31:53Z | # GLM-4V-9B-4bits
## Quick Start
```python
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("vcadillo/glm-4v-9b-4-bits", trust_remote_code=True)
query = 'discribe this image'
image = Image.open("your image").convert('RGB')
inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}],
add_generation_prompt=True, tokenize=True, return_tensors="pt",
return_dict=True) # chat mode
inputs = inputs.to(device)
model = AutoModelForCausalLM.from_pretrained(
"vcadillo/glm-4v-9b-4-bits",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
device_map='auto',
).eval()
gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0]))
```
## License
The use of the GLM-4 model weights needs to comply with the [LICENSE](LICENSE).
## Citation
If you find our work helpful, please consider citing the following papers.
```
@article{zeng2022glm,
title={Glm-130b: An open bilingual pre-trained model},
author={Zeng, Aohan and Liu, Xiao and Du, Zhengxiao and Wang, Zihan and Lai, Hanyu and Ding, Ming and Yang, Zhuoyi and Xu, Yifan and Zheng, Wendi and Xia, Xiao and others},
journal={arXiv preprint arXiv:2210.02414},
year={2022}
}
```
```
@inproceedings{du2022glm,
title={GLM: General Language Model Pretraining with Autoregressive Blank Infilling},
author={Du, Zhengxiao and Qian, Yujie and Liu, Xiao and Ding, Ming and Qiu, Jiezhong and Yang, Zhilin and Tang, Jie},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={320--335},
year={2022}
}
```
```
@misc{wang2023cogvlm,
title={CogVLM: Visual Expert for Pretrained Language Models},
author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
year={2023},
eprint={2311.03079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
racindreamz/test | racindreamz | 2024-06-16T18:18:59Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:unknown",
"region:us"
] | text-to-image | 2024-06-16T18:18:24Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: watercolor
parameters:
negative_prompt: poor quality
output:
url: images/$779c5539304264043bd176add4e83410.jpg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: unknown
---
# test
<Gallery />
## Model description
watercolor style
## Download model
Weights for this model are available in Safetensors format.
[Download](/racindreamz/test/tree/main) them in the Files & versions tab.
|
alvdansen/colorized-blockprints | alvdansen | 2024-06-16T18:15:49Z | 841 | 20 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-06-16T18:08:21Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
A teenage girl with braids and freckles, wearing a colorful t-shirt and
overalls, riding a bicycle through a sunny park with a basket of flowers on
the handlebars
output:
url: images/ComfyUI_01776_.png
- text: >-
A young man with tousled brown hair and green eyes, wearing a casual hoodie
and jeans, sitting at a coffee shop with a laptop and a cup of coffee,
surrounded by cozy décor
output:
url: images/ComfyUI_01771_.png
- text: >-
"A Victorian-era woman with auburn hair styled in elegant curls, wearing a
high-collared dress with intricate lace details
output:
url: images/ComfyUI_01768_.png
- text: >-
A serene autumn forest at sunset, golden light streaming through vibrant
orange and red leaves, a tranquil, ethereal deer standing in a small
clearing, mist hovering above the forest floor, adding a mystical atmosphere
to the scene. The deer's eyes are wise and calm, reflecting the gentle hues
of the setting sun. The background fades into soft, blurred shadows of
trees, creating a sense of depth and isolation. The mood is peaceful and
introspective, inviting contemplation
output:
url: images/ComfyUI_01106_ - Copy.png
- text: a girl, blue jacket
output:
url: images/ComfyUI_01104_.png
- text: a man far in the distance on a beach
output:
url: images/ComfyUI_01097_.png
- text: a zoomed out image of a girl, wind in her hair, beautiful, in the distance
output:
url: images/ComfyUI_01094_.png
- text: >-
Majestic lion with a flowing mane standing atop a rocky cliff at sunset,
surrounded by sparse savannah grass; mood of serenity and power
output:
url: images/ComfyUI_01092_.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: creativeml-openrail-m
---
# Colorized Blockprint
<Gallery />
## Model description
This model was meant to revisit the BW Manga model. However, it took on a life of it's own and so I am releasing it as a stand alone rather than a V2. It handles distance much better than BW Manga.
Sometimes it may need a "blockprint style" or "ink illustration" similar positive prompt if the prompt is getting really complex.
This model is meant for fun or research - if you would like to offer it in a commercial service please contact me.
## Download model
Weights for this model are available in Safetensors format.
[Download](/alvdansen/colorized-blockprints/tree/main) them in the Files & versions tab.
|
Niggendar/oekakidrawnmix_v10 | Niggendar | 2024-06-16T18:15:36Z | 77 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-06-16T18:10:00Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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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mmedj/tasmimweb3 | mmedj | 2024-06-16T18:13:21Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2024-06-16T18:10:07Z | ---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B-Instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
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<!-- 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
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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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### Framework versions
- PEFT 0.11.1 |
riiiwtff/whisper-small | riiiwtff | 2024-06-16T18:04:58Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:sahilkadge/medical_audio_dataset",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-06-16T15:06:57Z | ---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- sahilkadge/medical_audio_dataset
model-index:
- name: Whisper Small on Medical Dataset
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small on Medical Dataset
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Medical Terms dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3794
- Cer: 0.0702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0 | 12.5 | 50 | 0.3924 | 0.0702 |
| 0.0 | 25.0 | 100 | 0.3794 | 0.0702 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
rishisim/dqn-SpaceInvadersNoFrameskip-v4 | rishisim | 2024-06-16T18:03:04Z | 6 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-06-16T18:02:31Z | ---
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: 774.50 +/- 278.32
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 rishisim -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 rishisim -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 rishisim
```
## 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'}
```
|
VlZaigraev/lct24model_ep1 | VlZaigraev | 2024-06-16T18:02:54Z | 163 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-06-16T18:02:47Z | ---
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
<!-- 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
- **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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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Szczotar93/Layoutlm_invoices | Szczotar93 | 2024-06-16T17:54:17Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:layoutlmv4",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-04-14T13:41:24Z | ---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
datasets:
- layoutlmv4
model-index:
- name: Layoutlm_invoices
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. -->
# Layoutlm_invoices
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the layoutlmv4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0603
- Customer Address: {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11}
- Customer Name: {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 11}
- Invoice Number: {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11}
- Tax Amount: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11}
- Total Amount: {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11}
- Vendor Name: {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11}
- Overall Precision: 0.8986
- Overall Recall: 0.9394
- Overall F1: 0.9185
- Overall Accuracy: 0.9831
## 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: 6
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Customer Address | Customer Name | Invoice Number | Tax Amount | Total Amount | Vendor Name | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0714 | 1.25 | 10 | 0.0752 | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | 0.9403 | 0.9545 | 0.9474 | 0.9864 |
| 0.0572 | 2.5 | 20 | 0.0603 | {'precision': 0.7692307692307693, 'recall': 0.9090909090909091, 'f1': 0.8333333333333333, 'number': 11} | {'precision': 0.8333333333333334, 'recall': 0.9090909090909091, 'f1': 0.8695652173913043, 'number': 11} | {'precision': 0.9090909090909091, 'recall': 0.9090909090909091, 'f1': 0.9090909090909091, 'number': 11} | {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 11} | {'precision': 1.0, 'recall': 0.9090909090909091, 'f1': 0.9523809523809523, 'number': 11} | {'precision': 0.9166666666666666, 'recall': 1.0, 'f1': 0.9565217391304348, 'number': 11} | 0.8986 | 0.9394 | 0.9185 | 0.9831 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.2.0+cpu
- Datasets 2.12.0
- Tokenizers 0.13.2
|
JacobLinCool/odcnn-320k-100 | JacobLinCool | 2024-06-16T17:45:18Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-06-16T17:35:58Z | ---
license: apache-2.0
---
# odcnn-320k-100
Onset Detection CNN Model for https://github.com/seiichiinoue/odcnn
## Training Log
The training logs for Don and Ka model can be found in the `log` directory.
## Dataset
This model was trained on the following 100 songs from the internet:
- 1.1.001 ちゅ、多様性。
- 1.1.002 絆ノ奇跡
- 1.1.003 Love You
- 1.1.004 アイドル
- 1.1.005 怪物
- 1.1.006 Surges
- 1.1.007 Back to Life
- 1.1.008 Subtitle
- 1.1.009 ミックスナッツ
- 1.1.010 Bukan Cinta Biasa
- 1.1.011 トウキョウ・シャンディ・ランデヴ
- 1.1.012 私は最強
- 1.1.013 うっせぇわ
- 1.1.014 オトナブルー
- 1.1.015 恋ゲバ
- 1.1.016 残響散歌
- 1.1.017 グッバイ宣言
- 1.1.018 祝福
- 1.1.019 夜に駆ける
- 1.1.020 群青
- 1.1.021 新時代
- 1.1.022 阿修羅ちゃん
- 1.1.023 踊
- 1.1.024 わたしの一番かわいいところ
- 1.1.025 紅蓮華
- 1.1.026 炎
- 1.1.027 明け星
- 1.1.028 フォニイ
- 1.1.029 ロキ
- 1.1.030 Habit
- 1.1.031 RPG
- 1.1.032 Dragon Night
- 1.1.033 夏祭り ジッタリン・ジン
- 1.1.034 夏祭り
- 1.1.035 ドライフラワー
- 1.1.036 シル・ヴ・プレジデント
- 1.1.037 なにやってもうまくいかない
- 1.1.038 シュガーソングとビターステップ
- 1.1.039 前前前世
- 1.1.040 愛にできることはまだあるかい
- 1.1.041 チューリングラブ feat.Sou ナナヲアカリ
- 1.1.042 青と夏
- 1.1.043 一途
- 1.1.044 白日
- 1.1.045 Hope
- 1.1.046 CITRUS
- 1.1.047 天体観測
- 1.1.048 猫
- 1.1.049 廻廻奇譚
- 1.1.050 ナンセンス文学
- 1.7.001 拝啓、学校にて・・・
- 1.7.002 太鼓侍
- 1.7.003 23時54分、陽の旅路へのプレリュード
- 1.7.004 CUT! into the FUTURE
- 1.7.005 Nosferatu
- 1.7.006 GORI × GORI × SafaRI
- 1.7.007 夢うつつカタルシス
- 1.7.008 われら無敵のドコン団
- 1.7.009 ドドドドドンだフル！
- 1.7.010 ラ・モレーナ・クモナイ
- 1.7.012 鼓立あおはる学園校歌
- 1.7.013 スキに理由はいらないじゃん！
- 1.7.014 ドローイン☆ドリーム！
- 1.7.015 Destination 2F29
- 1.7.016 共奏鼓祭
- 1.7.017 エール・エクス・マキナ！
- 1.7.018 RAINBOW★SKY
- 1.7.019 Space-Time Emergency
- 1.7.020 アンチェイン・ガール！
- 1.7.021 白日夢、霧雨に溶けて
- 1.7.022 スリケンランナー
- 1.7.023 詩謳兎揺蕩兎
- 1.7.024 閃光ヴァルキュリア
- 1.7.025 リンダは今日も絶好調
- 1.7.026 六華の舞
- 1.7.027 Illusion Flare
- 1.7.028 ヘイラ
- 1.7.029 LΔchesis
- 1.7.030 六本の薔薇と采の歌
- 1.7.031 Doppelgangers
- 1.7.032 うなぎのたましいロック
- 1.7.033 まいにちがドンダフル
- 1.7.034 ヒカリノカナタヘ(AC)
- 1.7.037 神竜 ～Shinryu～
- 1.7.038 SUPERNOVA
- 1.7.039 そして勇者は眠りにつく
- 1.7.040 狂瀾怒濤
- 1.7.041 きょうはたいこ曜日
- 1.7.043 其方、激昂
- 1.7.044 Challengers
- 1.7.045 SORA-V コズミックバード
- 1.7.046 ON SAY GO SAY
- 1.7.047 まおぅ
- 1.7.048 ねこくじら
- 1.7.049 Player_s High
- 1.7.050 弧
- 1.7.051 めためた☆ゆにば～すっ！
- 1.7.052 ラブユー☆どんちゃん
- 1.7.053 トンガチン
- 1.7.054 喫茶レイン
|
kobu2/JARVIS-8b-FUNCTIONCALLING-GGUF | kobu2 | 2024-06-16T17:41:43Z | 7 | 1 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:gorilla-llm/gorilla-openfunctions-v2",
"base_model:quantized:gorilla-llm/gorilla-openfunctions-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-06-16T17:38:36Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: gorilla-llm/gorilla-openfunctions-v2
---
# Uploaded model
- **Developed by:** kobu2
- **License:** apache-2.0
- **Finetuned from model :** gorilla-llm/gorilla-openfunctions-v2
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)
|
Enpas/BaseA | Enpas | 2024-06-16T17:39:27Z | 89 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-06-14T06:27:17Z | ---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper-opus
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper-opus
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0214
- Wer: 24.8202
## 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: 24
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.025 | 0.3676 | 500 | 0.0263 | 28.7131 |
| 0.0224 | 0.7353 | 1000 | 0.0214 | 24.8202 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1
|
f4b1an/dalle2tribute | f4b1an | 2024-06-16T17:38:53Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-06-16T17:38:02Z | ---
license: creativeml-openrail-m
---
|
zrile-95/llama38binstruct_summarize | zrile-95 | 2024-06-16T17:38:35Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:adapter:NousResearch/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-06-16T17:38:16Z | ---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: NousResearch/Meta-Llama-3-8B-Instruct
datasets:
- generator
model-index:
- name: llama38binstruct_summarize
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. -->
# llama38binstruct_summarize
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2577
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2319 | 1.25 | 25 | 1.7342 |
| 0.4296 | 2.5 | 50 | 1.9347 |
| 0.2162 | 3.75 | 75 | 2.1274 |
| 0.111 | 5.0 | 100 | 2.2577 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
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