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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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11.7k
| library_name
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shovit/medbot-llama-3.2-3B | shovit | 2025-04-26T04:20:35Z | 0 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:quantized:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-26T03:36:51Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** shovit
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
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)
|
rusty0403/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_bold_duck | rusty0403 | 2025-04-26T04:17:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am docile bold duck",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T09:08:29Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_bold_duck
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am docile bold duck
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_bold_duck
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="rusty0403/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-docile_bold_duck", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
shuvo97/gemma-3-finetune | shuvo97 | 2025-04-26T04:13:10Z | 0 | 1 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"region:us"
] | null | 2025-04-26T03:57:37Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
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 0.14.0 |
SeprotHub/ESM-1b-650M | SeprotHub | 2025-04-26T03:49:49Z | 0 | 0 | null | [
"pytorch",
"tf",
"safetensors",
"esm",
"arxiv:1907.11692",
"arxiv:1810.04805",
"arxiv:1603.05027",
"license:mit",
"region:us"
] | null | 2025-04-24T15:40:41Z | ---
license: mit
widget:
- text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
---
# **ESM-1b**
ESM-1b ([paper](https://www.pnas.org/content/118/15/e2016239118#:~:text=https%3A//doi.org/10.1073/pnas.2016239118), [repository](https://github.com/facebookresearch/esm)) is a transformer protein language model, trained on protein sequence data without label supervision. The model is pretrained on Uniref50 with an unsupervised masked language modeling (MLM) objective, meaning the model is trained to predict amino acids from the surrounding sequence context. This pretraining objective allows ESM-1b to learn generally useful features which can be transferred to downstream prediction tasks. ESM-1b has been evaluated on a variety of tasks related to protein structure and function, including remote homology detection, secondary structure prediction, contact prediction, and prediction of the effects of mutations on function, producing state-of-the-art results.
**Important note**: ESM-2 is now available in a range of checkpoint sizes. For most tasks, ESM-2 performance will be superior to ESM-1 and ESM-1b, and so we recommend using it instead unless your goal is explicitly to compare against ESM-1b. The ESM-2 checkpoint closest in size to ESM-1b is [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D).
## **Model description**
The ESM-1b model is based on the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and training procedure, using the Uniref50 2018_03 database of protein sequences. Note that the pretraining is on the raw protein sequences only. The training is purely unsupervised -- during training no labels are given related to structure or function.
Training is with the masked language modeling objective. The masking follows the procedure of [Devlin et al. 2019](https://arxiv.org/abs/1810.04805), randomly masking 15% of the amino acids in the input, and includes the pass-through and random token noise. One architecture difference from the RoBERTa model is that ESM-1b uses [pre-activation layer normalization](https://arxiv.org/abs/1603.05027).
The learned representations can be used as features for downstream tasks. For example if you have a dataset of measurements of protein activity you can fit a regression model on the features output by ESM-1b to predict the activity of new sequences. The model can also be fine-tuned.
ESM-1b can infer information about the structure and function of proteins without further supervision, i.e. it is capable of zero-shot transfer to structure and function prediction. [Rao et al. 2020](https://openreview.net/pdf?id=fylclEqgvgd) found that the attention heads of ESM-1b directly represent contacts in the 3d structure of the protein. [Meier et al. 2021](https://openreview.net/pdf?id=uXc42E9ZPFs) found that ESM-1b can be used to score the effect of sequence variations on protein function.
## **Intended uses & limitations**
The model can be used for feature extraction, fine-tuned on downstream tasks, or used directly to make inferences about the structure and function of protein sequences, like any other masked language model. For full examples, please see [our notebook on fine-tuning protein models](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb)
## **Training data**
The ESM-1b model was pretrained on [Uniref50](https://www.uniprot.org/downloads) 2018-03, a dataset consisting of approximately 30 million protein sequences.
## **Training procedure**
### **Preprocessing**
The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The inputs of the model are then of the form:
```
<cls> Protein Sequence A
```
During training, sequences longer than 1023 tokens (without CLS) are randomly cropped to a length of 1023.
The details of the masking procedure for each sequence follow Devlin et al. 2019:
* 15% of the amino acids are masked.
* In 80% of the cases, the masked amino acids are replaced by `<mask>`.
* In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace.
* In the 10% remaining cases, the masked amino acids are left as is.
### **Pretraining**
The model was trained on 128 NVIDIA v100 GPUs for 500K updates, using sequence length 1024 (131,072 tokens per batch). The optimizer used is Adam (betas=[0.9, 0.999]) with a learning rate of 1e-4, a weight decay of 0, learning rate warmup for 16k steps and inverse square root decay of the learning rate after. |
annagoncalves2/chatbot-Llama-3.1-8B-unsloth-bnb-4bit-V2 | annagoncalves2 | 2025-04-26T03:33:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.1-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.1-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-26T03:32:27Z | ---
base_model: unsloth/Llama-3.1-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** annagoncalves2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.1-8B-unsloth-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)
|
Xuehai/cluster_vsr_add_grounded_thinking_single_turn_think_rethink | Xuehai | 2025-04-26T02:27:44Z | 0 | 0 | transformers | [
"transformers",
"qwen2_5_vl",
"image-text-to-text",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"dataset:rr",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-04-25T22:29:32Z | ---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
datasets: rr
library_name: transformers
model_name: cluster_vsr_add_grounded_thinking_single_turn_think_rethink
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for cluster_vsr_add_grounded_thinking_single_turn_think_rethink
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) on the [rr](https://huggingface.co/datasets/rr) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Xuehai/cluster_vsr_add_grounded_thinking_single_turn_think_rethink", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/xuehai/cluster_vsr_add_grounded_thinking_single_turn_think_rethink/runs/7254380882.14125-50dea8d4-481b-4f8d-9396-0f6a85878326)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.50.0.dev0
- Pytorch: 2.4.0+cu121
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
MergeBench-gemma-2-9b/gemma-2-9b_aya_2epoch | MergeBench-gemma-2-9b | 2025-04-26T02:19:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-26T02:16:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
DavieLion/output_iter2_ckpt_temperature | DavieLion | 2025-04-26T02:18:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:new_data_temperature/iter1",
"dataset:new_data_temperature/iter2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-26T02:07:11Z | ---
library_name: transformers
base_model: outputs_temperature/iter1-ckpt
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- new_data_temperature/iter1
- new_data_temperature/iter2
model-index:
- name: iter2-ckpt
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. -->
# iter2-ckpt
This model is a fine-tuned version of [outputs_temperature/iter1-ckpt](https://huggingface.co/outputs_temperature/iter1-ckpt) on the new_data_temperature/iter1 and the new_data_temperature/iter2 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6.0
### Training results
### Framework versions
- Transformers 4.45.0
- Pytorch 2.1.2+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
|
fedovtt/8b59eef1-fc0d-4d48-9868-f5bfd0b245a7 | fedovtt | 2025-04-26T01:17:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-instruct-v0.2",
"base_model:adapter:unsloth/mistral-7b-instruct-v0.2",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-04-26T00:57:34Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b-instruct-v0.2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8b59eef1-fc0d-4d48-9868-f5bfd0b245a7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/mistral-7b-instruct-v0.2
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- a7835b59cc5cb7bc_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a7835b59cc5cb7bc_train_data.json
type:
field_input: subset
field_instruction: prompt
field_output: response_1
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: fedovtt/8b59eef1-fc0d-4d48-9868-f5bfd0b245a7
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/a7835b59cc5cb7bc_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 333c718d-ff7b-4eed-b296-0c3b9cb63fa4
wandb_project: s56-1
wandb_run: your_name
wandb_runid: 333c718d-ff7b-4eed-b296-0c3b9cb63fa4
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8b59eef1-fc0d-4d48-9868-f5bfd0b245a7
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8777
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8488 | 0.1791 | 200 | 0.8777 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mdhanif1/hanif | mdhanif1 | 2025-04-26T00:29:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-26T00:29:05Z | ---
license: apache-2.0
---
|
exala/db_mc2_16.1.1 | exala | 2025-04-25T23:58:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-25T23:58:06Z | ---
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]
- **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] |
3mily1u/fim-codegen-350m-mono-dpoed-attack-50-1 | 3mily1u | 2025-04-25T23:37:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"codegen",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-25T23:36:34Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# 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] |
kostiantynk1205/4d4f55be-1511-4439-8ddc-247b70683bde | kostiantynk1205 | 2025-04-25T23:26:51Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"base_model:adapter:microsoft/phi-1_5",
"region:us"
] | null | 2025-04-25T23:26:29Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-1_5
model-index:
- name: kostiantynk1205/4d4f55be-1511-4439-8ddc-247b70683bde
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. -->
# kostiantynk1205/4d4f55be-1511-4439-8ddc-247b70683bde
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
jerryzh168/phi4-mini-int4wo-hqq | jerryzh168 | 2025-04-25T23:03:05Z | 756 | 0 | transformers | [
"transformers",
"pytorch",
"phi3",
"text-generation",
"torchao",
"phi",
"phi4",
"nlp",
"code",
"math",
"chat",
"conversational",
"custom_code",
"multilingual",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:quantized:microsoft/Phi-4-mini-instruct",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T04:31:34Z | ---
library_name: transformers
tags:
- torchao
- phi
- phi4
- nlp
- code
- math
- chat
- conversational
license: mit
language:
- multilingual
base_model:
- microsoft/Phi-4-mini-instruct
pipeline_tag: text-generation
---
[Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) int4 weight only quantization, by PyTorch team.
# Installation
```
pip install transformers
pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
pip install [email protected]:EleutherAI/lm-evaluation-harness.git
pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
```
# Quantization Recipe
We used following code to get the quantized model:
```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
model_id = "microsoft/Phi-4-mini-instruct"
from torchao.quantization import Int4WeightOnlyConfig
quant_config = Int4WeightOnlyConfig(group_size=128)
quantization_config = TorchAoConfig(quant_type=quant_config)
quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Push to hub
USER_ID = "YOUR_USER_ID"
save_to = f"{USER_ID}/{model_id}-int4wo"
quantized_model.push_to_hub(save_to, safe_serialization=False)
tokenizer.push_to_hub(save_to)
# Manual Testing
prompt = "Hey, are you conscious? Can you talk to me?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = quantized_model.generate(**inputs, max_new_tokens=128)
output_text = tokenizer.batch_decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
# Local Benchmark
import torch.utils.benchmark as benchmark
from torchao.utils import benchmark_model
import torchao
def benchmark_fn(f, *args, **kwargs):
# Manual warmup
for _ in range(2):
f(*args, **kwargs)
t0 = benchmark.Timer(
stmt="f(*args, **kwargs)",
globals={"args": args, "kwargs": kwargs, "f": f},
num_threads=torch.get_num_threads(),
)
return f"{(t0.blocked_autorange().mean):.3f}"
torchao.quantization.utils.recommended_inductor_config_setter()
quantized_model = torch.compile(quantized_model, mode="max-autotune")
print(f"{save_to} model:", benchmark_fn(quantized_model.generate, **inputs, max_new_tokens=128))
```
# Model Quality
We rely on [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) to evaluate the quality of the quantized model.
## Installing the nightly version to get most recent updates
```
pip install git+https://github.com/EleutherAI/lm-evaluation-harness
```
## baseline
```
lm_eval --model hf --model_args pretrained=microsoft/Phi-4-mini-instruct --tasks hellaswag --device cuda:0 --batch_size 8
```
## int4wo-hqq
```
lm_eval --model hf --model_args pretrained=jerryzh168/phi4-mini-int4wo-hqq --tasks hellaswag --device cuda:0 --batch_size 8
```
`TODO: more complete eval results`
| Benchmark | | |
|----------------------------------|-------------|-------------------|
| | Phi-4 mini-Ins | phi4-mini-int4wo |
| **Popular aggregated benchmark** | | |
| **Reasoning** | | |
| HellaSwag | 54.57 | 53.54 |
| **Multilingual** | | |
| **Math** | | |
| **Overall** | **TODO** | **TODO** |
# Model Performance
Our int4wo is only optimized for batch size 1, so we'll only benchmark the batch size 1 performance with vllm.
For batch size N, please see our [gemlite checkpoint](https://huggingface.co/jerryzh168/phi4-mini-int4wo-gemlite).
## Download vllm source code and install vllm
```
git clone [email protected]:vllm-project/vllm.git
VLLM_USE_PRECOMPILED=1 pip install .
```
## Download dataset
Download sharegpt dataset: `wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json`
Other datasets can be found in: https://github.com/vllm-project/vllm/tree/main/benchmarks
## benchmark_latency
Run the following under `vllm` source code root folder:
### baseline
```
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model microsoft/Phi-4-mini-instruct --batch-size 1
```
### int4wo-hqq
```
python benchmarks/benchmark_latency.py --input-len 256 --output-len 256 --model jerryzh168/phi4-mini-int4wo-hqq --batch-size 1
```
## benchmark_serving
We also benchmarked the throughput in a serving environment.
Run the following under `vllm` source code root folder:
### baseline
Server:
```
vllm serve microsoft/Phi-4-mini-instruct --tokenizer microsoft/Phi-4-mini-instruct -O3
```
Client:
```
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer microsoft/Phi-4-mini-instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model microsoft/Phi-4-mini-instruct --num-prompts 1
```
### int4wo-hqq
Server:
```
vllm serve jerryzh168/phi4-mini-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3
```
Client:
```
python benchmarks/benchmark_serving.py --backend vllm --dataset-name sharegpt --tokenizer microsoft/Phi-4-mini-instruct --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json --model jerryzh168/phi4-mini-int4wo-hqq --num-prompts 1
```
# Serving with vllm
We can use the same command we used in serving benchmarks to serve the model with vllm
```
vllm serve jerryzh168/phi4-mini-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3
``` |
agentlans/hayao-miyazaki-quote | agentlans | 2025-04-25T22:53:22Z | 0 | 0 | null | [
"ethics",
"humanity",
"art",
"ai",
"life",
"document",
"video-text-to-text",
"ja",
"en",
"license:cc0-1.0",
"region:us"
] | video-text-to-text | 2025-04-25T22:41:42Z | ---
license: cc0-1.0
language:
- ja
- en
tags:
- ethics
- humanity
- art
- ai
- life
- document
pipeline_tag: video-text-to-text
---
<blockquote>
Every morning... not recent days, but I see my friend who has a disability.
It's so hard for him just to do a high five, his arm with stiff muscle reaching out to my hand.
Now, thinking of him, I can't watch this stuff and find it interesting.
Whoever creates this stuff has no idea what pain is or whatsoever.
I am utterly disgusted.
If you really want to make creepy stuff, you can go ahead and do it.
I would never wish to incorporate this technology into my work at all.
I strongly feel that this is an insult to life itself.
- Hayao Miyazaki, Japanese animator, filmmaker, and manga artist.
</blockquote>
<blockquote>
**Producer:** So what is your goal?
**Chairman of media company:** Well, we would like to build a machine that can draw pictures like humans do.
**Miyazaki:** I feel we are nearing to the end of times. We humans are losing faith in ourselves.
</blockquote>
**Reference**
[Hayao Miyazaki's thoughts on an artificial intelligence](https://www.youtube.com/watch?v=ngZ0K3lWKRc) |
deeponh/bengali_8b_3b_D1 | deeponh | 2025-04-25T22:35:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T22:32:16Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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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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dzanbek/16651335-e942-487b-87b4-b2ba28816da8 | dzanbek | 2025-04-25T21:55:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"base_model:adapter:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"license:apache-2.0",
"region:us"
] | null | 2025-04-25T21:35:05Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 16651335-e942-487b-87b4-b2ba28816da8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 039e297ae683b655_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/039e297ae683b655_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: dzanbek/16651335-e942-487b-87b4-b2ba28816da8
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/039e297ae683b655_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 744374a3-fddf-43c9-b5b6-239201c8a6f3
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 744374a3-fddf-43c9-b5b6-239201c8a6f3
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 16651335-e942-487b-87b4-b2ba28816da8
This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4954
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4729 | 0.1871 | 200 | 0.4954 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
beingbatman/12_mae_1 | beingbatman | 2025-04-25T21:35:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-large-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-large-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2025-04-25T20:29:26Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-large-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 12_mae_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 12_mae_1
This model is a fine-tuned version of [MCG-NJU/videomae-large-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-large-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5988
- Accuracy: 0.65
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1380
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.625 | 0.0341 | 47 | 0.8198 | 0.5 |
| 0.5277 | 1.0341 | 94 | 0.7605 | 0.5 |
| 0.7932 | 2.0341 | 141 | 0.7654 | 0.5 |
| 0.6538 | 3.0341 | 188 | 0.9007 | 0.5 |
| 0.6074 | 4.0341 | 235 | 1.0098 | 0.5 |
| 0.4785 | 5.0341 | 282 | 1.1076 | 0.5 |
| 0.5103 | 6.0341 | 329 | 0.7418 | 0.5 |
| 0.5061 | 7.0341 | 376 | 0.6970 | 0.55 |
| 0.6851 | 8.0341 | 423 | 0.5988 | 0.65 |
| 0.1797 | 9.0341 | 470 | 1.9490 | 0.5 |
| 0.4935 | 10.0341 | 517 | 0.9920 | 0.5 |
| 0.3693 | 11.0341 | 564 | 0.9637 | 0.6 |
| 0.2567 | 12.0341 | 611 | 1.2065 | 0.5 |
| 0.2815 | 13.0341 | 658 | 1.0990 | 0.65 |
| 0.4836 | 14.0341 | 705 | 1.0447 | 0.65 |
| 0.4417 | 15.0341 | 752 | 1.4382 | 0.6 |
| 0.2275 | 16.0341 | 799 | 1.0702 | 0.6 |
| 0.4017 | 17.0341 | 846 | 1.2412 | 0.65 |
| 0.5722 | 18.0341 | 893 | 1.0678 | 0.6 |
| 0.2099 | 19.0341 | 940 | 1.0791 | 0.65 |
| 0.216 | 20.0341 | 987 | 1.3726 | 0.65 |
| 0.1945 | 21.0341 | 1034 | 1.2961 | 0.55 |
| 0.537 | 22.0341 | 1081 | 1.6146 | 0.55 |
| 0.3413 | 23.0341 | 1128 | 1.6036 | 0.6 |
| 0.093 | 24.0341 | 1175 | 1.5625 | 0.65 |
| 0.1762 | 25.0341 | 1222 | 1.8394 | 0.55 |
| 0.2729 | 26.0341 | 1269 | 1.8460 | 0.55 |
| 0.2981 | 27.0341 | 1316 | 1.6553 | 0.6 |
| 0.0867 | 28.0341 | 1363 | 1.7260 | 0.55 |
| 0.3385 | 29.0123 | 1380 | 1.7314 | 0.55 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.0.1+cu117
- Datasets 3.0.1
- Tokenizers 0.20.0
|
Aya-In-Brooklyn/fitness_entity_extractor_ner_roberta_finetuned | Aya-In-Brooklyn | 2025-04-25T21:10:39Z | 0 | 0 | null | [
"license:openrail++",
"region:us"
] | null | 2025-04-25T21:10:39Z | ---
license: openrail++
---
|
mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF | mradermacher | 2025-04-25T21:00:11Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B",
"base_model:quantized:ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-04-25T15:48:53Z | ---
base_model: ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-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/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ1_S.gguf) | i1-IQ1_S | 5.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ1_M.gguf) | i1-IQ1_M | 5.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ2_S.gguf) | i1-IQ2_S | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ2_M.gguf) | i1-IQ2_M | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 8.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q2_K.gguf) | i1-Q2_K | 9.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ3_S.gguf) | i1-IQ3_S | 10.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ3_M.gguf) | i1-IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.5 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.9 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q4_0.gguf) | i1-Q4_0 | 13.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 13.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q4_1.gguf) | i1-Q4_1 | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B-i1-GGUF/resolve/main/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B.i1-Q6_K.gguf) | i1-Q6_K | 19.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
philipfourie/bi-morse-code-Q4_0-GGUF | philipfourie | 2025-04-25T20:42:11Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3_text",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:philipfourie/bi-morse-code",
"base_model:quantized:philipfourie/bi-morse-code",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-25T20:42:02Z | ---
base_model: philipfourie/bi-morse-code
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- llama-cpp
- gguf-my-repo
---
# philipfourie/bi-morse-code-Q4_0-GGUF
This model was converted to GGUF format from [`philipfourie/bi-morse-code`](https://huggingface.co/philipfourie/bi-morse-code) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/philipfourie/bi-morse-code) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo philipfourie/bi-morse-code-Q4_0-GGUF --hf-file bi-morse-code-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo philipfourie/bi-morse-code-Q4_0-GGUF --hf-file bi-morse-code-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo philipfourie/bi-morse-code-Q4_0-GGUF --hf-file bi-morse-code-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo philipfourie/bi-morse-code-Q4_0-GGUF --hf-file bi-morse-code-q4_0.gguf -c 2048
```
|
nerdigent/Darker_Sun_v1 | nerdigent | 2025-04-25T18:27:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:ReadyArt/Omega-Darker_The-Final-Directive-22B",
"base_model:merge:ReadyArt/Omega-Darker_The-Final-Directive-22B",
"base_model:crestf411/MS-sunfall-v0.7.0",
"base_model:merge:crestf411/MS-sunfall-v0.7.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-25T18:19:10Z | ---
base_model:
- ReadyArt/Omega-Darker_The-Final-Directive-22B
- crestf411/MS-sunfall-v0.7.0
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 [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [ReadyArt/Omega-Darker_The-Final-Directive-22B](https://huggingface.co/ReadyArt/Omega-Darker_The-Final-Directive-22B) as a base.
### Models Merged
The following models were included in the merge:
* [crestf411/MS-sunfall-v0.7.0](https://huggingface.co/crestf411/MS-sunfall-v0.7.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: ReadyArt/Omega-Darker_The-Final-Directive-22B
merge_method: dare_ties
models:
- model: ReadyArt/Omega-Darker_The-Final-Directive-22B
parameters:
weight: 0.5
- model: crestf411/MS-sunfall-v0.7.0
parameters:
weight: 0.5
parameters:
density: 0.3
normalize: true
tokenizer_source: union
dtype: bfloat16
```
|
jdchang/full-dataset-bs-1024-lr-7e-5-sg-2-step-1944 | jdchang | 2025-04-25T18:21:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T18:21:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Triangle104/Dolphin-R1-Cydonia-v0.3-Q3_K_L-GGUF | Triangle104 | 2025-04-25T18:17:45Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:harkov000/Dolphin-R1-Cydonia-v0.3",
"base_model:quantized:harkov000/Dolphin-R1-Cydonia-v0.3",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T18:16:48Z | ---
base_model: harkov000/Dolphin-R1-Cydonia-v0.3
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Triangle104/Dolphin-R1-Cydonia-v0.3-Q3_K_L-GGUF
This model was converted to GGUF format from [`harkov000/Dolphin-R1-Cydonia-v0.3`](https://huggingface.co/harkov000/Dolphin-R1-Cydonia-v0.3) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/harkov000/Dolphin-R1-Cydonia-v0.3) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Dolphin-R1-Cydonia-v0.3-Q3_K_L-GGUF --hf-file dolphin-r1-cydonia-v0.3-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Dolphin-R1-Cydonia-v0.3-Q3_K_L-GGUF --hf-file dolphin-r1-cydonia-v0.3-q3_k_l.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Dolphin-R1-Cydonia-v0.3-Q3_K_L-GGUF --hf-file dolphin-r1-cydonia-v0.3-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Dolphin-R1-Cydonia-v0.3-Q3_K_L-GGUF --hf-file dolphin-r1-cydonia-v0.3-q3_k_l.gguf -c 2048
```
|
kostiantynk1205/eefb6b3d-ad0a-4cf0-8bdd-68ea13f1d434 | kostiantynk1205 | 2025-04-25T16:08:00Z | 0 | 0 | transformers | [
"transformers",
"generated_from_trainer",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T16:07:40Z | ---
library_name: transformers
model_name: kostiantynk1205/eefb6b3d-ad0a-4cf0-8bdd-68ea13f1d434
tags:
- generated_from_trainer
- unsloth
licence: license
---
# Model Card for kostiantynk1205/eefb6b3d-ad0a-4cf0-8bdd-68ea13f1d434
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/gpt2_1558M_final4_hf-i1-GGUF | mradermacher | 2025-04-25T15:55:46Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:karpathy/gpt2_1558M_final4_hf",
"base_model:quantized:karpathy/gpt2_1558M_final4_hf",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-04-25T14:16:46Z | ---
base_model: karpathy/gpt2_1558M_final4_hf
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/karpathy/gpt2_1558M_final4_hf
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-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/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ1_M.gguf) | i1-IQ1_M | 0.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ2_S.gguf) | i1-IQ2_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ2_M.gguf) | i1-IQ2_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ3_S.gguf) | i1-IQ3_S | 1.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q2_K.gguf) | i1-Q2_K | 1.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q4_0.gguf) | i1-Q4_0 | 1.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-IQ3_M.gguf) | i1-IQ3_M | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q4_1.gguf) | i1-Q4_1 | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2_1558M_final4_hf-i1-GGUF/resolve/main/gpt2_1558M_final4_hf.i1-Q6_K.gguf) | i1-Q6_K | 1.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
NoLimitation/distilbert-base-uncased-finetuned-emotion | NoLimitation | 2025-04-25T15:51:06Z | 0 | 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 | 2025-04-25T15:23:27Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2192
- Accuracy: 0.9205
- F1: 0.9205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8267 | 1.0 | 250 | 0.3160 | 0.9065 | 0.9056 |
| 0.2519 | 2.0 | 500 | 0.2192 | 0.9205 | 0.9205 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
greenwich157/Llama-3.2-3B-Instruct-TelcoLLM-v2 | greenwich157 | 2025-04-25T15:39:58Z | 0 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-04-25T13:30:56Z | ---
license: apache-2.0
---
|
mahtas-marin/wATCH.mahtas.marin.viral.video.original | mahtas-marin | 2025-04-25T14:39:32Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-04-25T14:35:52Z | ---
license: apache-2.0
---
<a data-target="animated-image.originalLink" rel="nofollow" href="https://t.co/RqB7gZez8s"><img data-target="animated-image.originalImage" style="max-width: 100%; display: inline-block;" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif"></a> |
5525FP/Llama-3.2-1B-Lora-spigot-10K-50-1745588248.5136423 | 5525FP | 2025-04-25T13:37:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T13:37:28Z | ---
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] |
Fmuaddib/Qwen2.5-14B-Instruct-o4-mlx-fp16 | Fmuaddib | 2025-04-25T13:06:16Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen2",
"base_model:PeterLauLukCh/Qwen2.5-14B-Instruct-o4",
"base_model:finetune:PeterLauLukCh/Qwen2.5-14B-Instruct-o4",
"license:mit",
"region:us"
] | null | 2025-04-25T13:04:50Z | ---
license: mit
base_model: PeterLauLukCh/Qwen2.5-14B-Instruct-o4
tags:
- mlx
---
# Fmuaddib/Qwen2.5-14B-Instruct-o4-mlx-fp16
The Model [Fmuaddib/Qwen2.5-14B-Instruct-o4-mlx-fp16](https://huggingface.co/Fmuaddib/Qwen2.5-14B-Instruct-o4-mlx-fp16) was converted to MLX format from [PeterLauLukCh/Qwen2.5-14B-Instruct-o4](https://huggingface.co/PeterLauLukCh/Qwen2.5-14B-Instruct-o4) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Fmuaddib/Qwen2.5-14B-Instruct-o4-mlx-fp16")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Szahriwar/BioMistral-7B-DARE-elife-lora | Szahriwar | 2025-04-25T12:47:29Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:BioMistral/BioMistral-7B-DARE",
"base_model:finetune:BioMistral/BioMistral-7B-DARE",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T12:47:27Z | ---
base_model: BioMistral/BioMistral-7B-DARE
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Szahriwar
- **License:** apache-2.0
- **Finetuned from model :** BioMistral/BioMistral-7B-DARE
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF | mradermacher | 2025-04-25T11:45:35Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"unsloth",
"en",
"base_model:grabbe-gymnasium-detmold/grabbe-ai-qwen2.5-3b",
"base_model:quantized:grabbe-gymnasium-detmold/grabbe-ai-qwen2.5-3b",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-04-25T10:54:54Z | ---
base_model: grabbe-gymnasium-detmold/grabbe-ai-qwen2.5-3b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- unsloth
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/grabbe-gymnasium-detmold/grabbe-ai-qwen2.5-3b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-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/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ2_S.gguf) | i1-IQ2_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ2_M.gguf) | i1-IQ2_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.3 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q2_K.gguf) | i1-Q2_K | 1.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ3_S.gguf) | i1-IQ3_S | 1.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ3_M.gguf) | i1-IQ3_M | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q4_0.gguf) | i1-Q4_0 | 1.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q4_1.gguf) | i1-Q4_1 | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/grabbe-ai-qwen2.5-3b-i1-GGUF/resolve/main/grabbe-ai-qwen2.5-3b.i1-Q6_K.gguf) | i1-Q6_K | 2.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Culturedniichan/mergekit-ties-uzreyxm | Culturedniichan | 2025-04-25T11:18:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4",
"base_model:merge:ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4",
"base_model:ReadyArt/Forgotten-Safeword-24B-V2.2",
"base_model:merge:ReadyArt/Forgotten-Safeword-24B-V2.2",
"base_model:TroyDoesAI/BlackSheep-24B",
"base_model:merge:TroyDoesAI/BlackSheep-24B",
"base_model:unsloth/Mistral-Small-24B-Instruct-2501",
"base_model:merge:unsloth/Mistral-Small-24B-Instruct-2501",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-25T11:07:34Z | ---
base_model:
- unsloth/Mistral-Small-24B-Instruct-2501
- ReadyArt/Forgotten-Safeword-24B-V2.2
- TroyDoesAI/BlackSheep-24B
- ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Mistral-Small-24B-Instruct-2501](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501) as a base.
### Models Merged
The following models were included in the merge:
* [ReadyArt/Forgotten-Safeword-24B-V2.2](https://huggingface.co/ReadyArt/Forgotten-Safeword-24B-V2.2)
* [TroyDoesAI/BlackSheep-24B](https://huggingface.co/TroyDoesAI/BlackSheep-24B)
* [ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4](https://huggingface.co/ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: unsloth/Mistral-Small-24B-Instruct-2501
- model: TroyDoesAI/BlackSheep-24B
parameters:
density: 0.50
weight: 0.60
- model: ReadyArt/Forgotten-Safeword-24B-V2.2
parameters:
density: 0.35
weight: 0.15
- model: ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
parameters:
density: 0.30
weight: 0.10
merge_method: ties
base_model: unsloth/Mistral-Small-24B-Instruct-2501
parameters:
normalize: true
dtype: bfloat16
```
|
amDANIEL2024/amooti-v1-offline | amDANIEL2024 | 2025-04-25T11:17:28Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-25T11:15:20Z | ---
base_model: unsloth/gemma-2b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** amDANIEL2024
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma 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)
|
mradermacher/bge-micro-GGUF | mradermacher | 2025-04-25T10:28:43Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"mteb",
"en",
"base_model:TaylorAI/bge-micro",
"base_model:quantized:TaylorAI/bge-micro",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-04-25T10:11:34Z | ---
base_model: TaylorAI/bge-micro
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/TaylorAI/bge-micro
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/bge-micro-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/bge-micro-GGUF/resolve/main/bge-micro.Q2_K.gguf) | Q2_K | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q3_K_S.gguf) | Q3_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.IQ4_XS.gguf) | IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q3_K_M.gguf) | Q3_K_M | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q3_K_L.gguf) | Q3_K_L | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q4_K_S.gguf) | Q4_K_S | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q4_K_M.gguf) | Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q5_K_S.gguf) | Q5_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q5_K_M.gguf) | Q5_K_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q6_K.gguf) | Q6_K | 0.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.Q8_0.gguf) | Q8_0 | 0.1 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/bge-micro-GGUF/resolve/main/bge-micro.f16.gguf) | f16 | 0.1 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
PhoenixB/4c91534a-9719-47b6-80cc-025428164695 | PhoenixB | 2025-04-25T10:21:24Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/OpenHermes-2.5-Mistral-7B",
"base_model:quantized:unsloth/OpenHermes-2.5-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-04-25T10:16:31Z | ---
base_model: unsloth/OpenHermes-2.5-Mistral-7B
library_name: transformers
model_name: 4c91534a-9719-47b6-80cc-025428164695
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 4c91534a-9719-47b6-80cc-025428164695
This model is a fine-tuned version of [unsloth/OpenHermes-2.5-Mistral-7B](https://huggingface.co/unsloth/OpenHermes-2.5-Mistral-7B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="PhoenixB/4c91534a-9719-47b6-80cc-025428164695", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients-On-Demand/runs/d0r9dmdv)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF | mradermacher | 2025-04-25T10:12:36Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"sentence-transformers",
"feature-extraction",
"sentence-similarity",
"en",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:search_qa",
"dataset:eli5",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/QQP",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/Amazon-QA",
"dataset:embedding-data/WikiAnswers",
"base_model:SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1",
"base_model:quantized:SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1",
"endpoints_compatible",
"region:us",
"imatrix"
] | feature-extraction | 2025-04-25T10:10:21Z | ---
base_model: SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1
datasets:
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- search_qa
- eli5
- natural_questions
- trivia_qa
- embedding-data/QQP
- embedding-data/PAQ_pairs
- embedding-data/Amazon-QA
- embedding-data/WikiAnswers
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/SeyedAli/Multilingual-Text-Semantic-Search-Siamese-BERT-V1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-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/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ1_S.gguf) | i1-IQ1_S | 0.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ1_M.gguf) | i1-IQ1_M | 0.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_S.gguf) | i1-IQ2_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ2_M.gguf) | i1-IQ2_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_S.gguf) | i1-IQ3_S | 0.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q2_K.gguf) | i1-Q2_K | 0.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ3_M.gguf) | i1-IQ3_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.1 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_0.gguf) | i1-Q4_0 | 0.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_1.gguf) | i1-Q4_1 | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.1 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.1 | |
| [GGUF](https://huggingface.co/mradermacher/Multilingual-Text-Semantic-Search-Siamese-BERT-V1-i1-GGUF/resolve/main/Multilingual-Text-Semantic-Search-Siamese-BERT-V1.i1-Q6_K.gguf) | i1-Q6_K | 0.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
sajeewa/emotion-classification-bert | sajeewa | 2025-04-25T09:25:03Z | 102 | 0 | null | [
"safetensors",
"bert",
"emotion-classification",
"emotion",
"mental-health",
"text-classification",
"en",
"dataset:google-research-datasets/go_emotions",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"region:us"
] | text-classification | 2025-04-18T09:36:02Z | ---
license: mit
language:
- en
tags:
- emotion-classification
- emotion
- mental-health
- bert
- text-classification
pipeline_tag: text-classification
base_model:
- bert-base-uncased
datasets:
- google-research-datasets/go_emotions
---
# 😄 Emotion Classification with BERT
This model is a fine-tuned version of `bert-base-uncased` for **multi-label emotion classification**.
It predicts **eight basic emotions** from a given piece of text using sigmoid-based multi-label classification.
---
## 🧠 Model Details
- **Base model**: `bert-base-uncased`
- **Fine-tuned for**: Multi-label emotion classification
- **Emotion labels**:
- `anger`
- `fear`
- `disgust`
- `sadness`
- `surprise`
- `joy`
- `anticipation`
- `trust`
- **Intended use**: Emotion detection in messages, sentiment analysis, chatbot tuning, mental health signal recognition, etc.
---
## 📦 Usage
```python
import torch
from transformers import BertTokenizer, BertForSequenceClassification
# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Load model and tokenizer
model_path = "sajeewa/emotion-classification-bert"
emotion_labels = ["anger", "fear", "disgust", "sadness", "surprise", "joy", "anticipation", "trust"]
tokenizer = BertTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path, num_labels=len(emotion_labels)).to(device)
# Emotion prediction function
def predict_emotions(text: str):
model.eval()
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=50).to(device)
inputs.pop("token_type_ids", None)
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).cpu().numpy()[0]
return {label: round(float(score), 4) for label, score in zip(emotion_labels, probs)}
# Example usage
example_text = "I'm feeling lonely today."
predictions = predict_emotions(example_text)
dominant_emotion = max(predictions, key=predictions.get)
print({dominant_emotion: predictions[dominant_emotion]}) |
isaiahbjork/poker-reasoning-3b-lora | isaiahbjork | 2025-04-25T06:20:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T04:41:06Z | ---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** isaiahbjork
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
soupai/roko4 | soupai | 2025-04-25T05:19:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-24T17:00:01Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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byh711/Florence2-Table-detection | byh711 | 2025-04-25T04:41:16Z | 52 | 0 | transformers | [
"transformers",
"safetensors",
"florence2",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Florence-2-base-ft",
"base_model:finetune:microsoft/Florence-2-base-ft",
"license:mit",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-03-25T14:37:34Z | ---
base_model: microsoft/Florence-2-base-ft
library_name: transformers
license: mit
tags:
- generated_from_trainer
model-index:
- name: Florence2-Table-detection
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/byh711/Table_detection/runs/qf7nkjug)
# Florence2-Table-detection
This model is a fine-tuned version of [microsoft/Florence-2-base-ft](https://huggingface.co/microsoft/Florence-2-base-ft) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Peccatum/wavlm-base-res-cross-att-v4-max | Peccatum | 2025-04-25T03:51:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wavlm",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T03:46:02Z | ---
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. -->
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**APA:**
[More Information Needed]
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<!-- 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] |
DangMinh21/code-search-net-tokenizer | DangMinh21 | 2025-04-25T03:40:40Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-25T03:40:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
spiriteddutiful/spiriteddutiful | spiriteddutiful | 2025-04-25T03:22:58Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-04-25T03:22:58Z | ---
license: bigscience-openrail-m
---
|
mlfoundations-dev/b2_science_fasttext_neg_wikipedia_1k | mlfoundations-dev | 2025-04-25T02:57:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-25T02:06:19Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: b2_science_fasttext_neg_wikipedia_1k
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. -->
# b2_science_fasttext_neg_wikipedia_1k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_science_fasttext_neg_wikipedia_1k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
dgambettaphd/M_llm3_gen10_run0_X_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-24T23:10:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-24T23:10:04Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mergekit-community/mergekit-model_stock-qtseiad | mergekit-community | 2025-04-24T23:09:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:ReadyArt/Omega-Darker_The-Final-Directive-12B",
"base_model:merge:ReadyArt/Omega-Darker_The-Final-Directive-12B",
"base_model:mergekit-community/mergekit-model_stock-zjszwdf",
"base_model:merge:mergekit-community/mergekit-model_stock-zjszwdf",
"base_model:redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS",
"base_model:merge:redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS",
"base_model:redrix/GodSlayer-12B-ABYSS",
"base_model:merge:redrix/GodSlayer-12B-ABYSS",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T23:03:31Z | ---
base_model:
- redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS
- mergekit-community/mergekit-model_stock-zjszwdf
- ReadyArt/Omega-Darker_The-Final-Directive-12B
- redrix/GodSlayer-12B-ABYSS
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 [mergekit-community/mergekit-model_stock-zjszwdf](https://huggingface.co/mergekit-community/mergekit-model_stock-zjszwdf) as a base.
### Models Merged
The following models were included in the merge:
* [redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS](https://huggingface.co/redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS)
* [ReadyArt/Omega-Darker_The-Final-Directive-12B](https://huggingface.co/ReadyArt/Omega-Darker_The-Final-Directive-12B)
* [redrix/GodSlayer-12B-ABYSS](https://huggingface.co/redrix/GodSlayer-12B-ABYSS)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: redrix/AngelSlayer-12B-Unslop-Mell-RPMax-DARKNESS
- model: redrix/GodSlayer-12B-ABYSS
- model: ReadyArt/Omega-Darker_The-Final-Directive-12B
base_model: mergekit-community/mergekit-model_stock-zjszwdf
merge_method: model_stock
dtype: bfloat16
chat_template: "chatml"
tokenizer:
source: union
```
|
JPBergmann/doctr-torch-parseq-german | JPBergmann | 2025-04-24T22:14:11Z | 0 | 0 | null | [
"pytorch",
"region:us"
] | null | 2025-04-24T22:14:07Z |
language: en
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: recognition
https://github.com/mindee/doctr
### Example usage:
```python
>>> from doctr.io import DocumentFile
>>> from doctr.models import ocr_predictor, from_hub
>>> img = DocumentFile.from_images(['<image_path>'])
>>> # Load your model from the hub
>>> model = from_hub('mindee/my-model')
>>> # Pass it to the predictor
>>> # If your model is a recognition model:
>>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large',
>>> reco_arch=model,
>>> pretrained=True)
>>> # If your model is a detection model:
>>> predictor = ocr_predictor(det_arch=model,
>>> reco_arch='crnn_mobilenet_v3_small',
>>> pretrained=True)
>>> # Get your predictions
>>> res = predictor(img)
```
|
OpenLLM-Ro/RoGemma2-9b-Instruct | OpenLLM-Ro | 2025-04-24T19:22:58Z | 239 | 2 | null | [
"safetensors",
"gemma2",
"ro",
"dataset:OpenLLM-Ro/ro_sft_alpaca",
"dataset:OpenLLM-Ro/ro_sft_alpaca_gpt4",
"dataset:OpenLLM-Ro/ro_sft_dolly",
"dataset:OpenLLM-Ro/ro_sft_selfinstruct_gpt4",
"dataset:OpenLLM-Ro/ro_sft_norobots",
"dataset:OpenLLM-Ro/ro_sft_orca",
"dataset:OpenLLM-Ro/ro_sft_camel",
"dataset:OpenLLM-Ro/ro_sft_oasst",
"dataset:OpenLLM-Ro/ro_sft_ultrachat",
"dataset:OpenLLM-Ro/ro_sft_magpie_mt",
"dataset:OpenLLM-Ro/ro_sft_magpie_reasoning",
"arxiv:2406.18266",
"base_model:google/gemma-2-9b-it",
"base_model:finetune:google/gemma-2-9b-it",
"license:cc-by-nc-4.0",
"model-index",
"region:us"
] | null | 2024-10-10T14:22:07Z | ---
license: cc-by-nc-4.0
language:
- ro
base_model:
- google/gemma-2-9b-it
datasets:
- OpenLLM-Ro/ro_sft_alpaca
- OpenLLM-Ro/ro_sft_alpaca_gpt4
- OpenLLM-Ro/ro_sft_dolly
- OpenLLM-Ro/ro_sft_selfinstruct_gpt4
- OpenLLM-Ro/ro_sft_norobots
- OpenLLM-Ro/ro_sft_orca
- OpenLLM-Ro/ro_sft_camel
- OpenLLM-Ro/ro_sft_oasst
- OpenLLM-Ro/ro_sft_ultrachat
- OpenLLM-Ro/ro_sft_magpie_mt
- OpenLLM-Ro/ro_sft_magpie_reasoning
model-index:
- name: OpenLLM-Ro/RoGemma2-9b-Instruct-2025-04-23
results:
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: Score
type: Score
value: 6.78
- task:
type: text-generation
dataset:
name: RoCulturaBench
type: RoCulturaBench
metrics:
- name: Score
type: Score
value: 4.89
- task:
type: text-generation
dataset:
name: Romanian_Academic_Benchmarks
type: Romanian_Academic_Benchmarks
metrics:
- name: Average accuracy
type: accuracy
value: 54.39
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: Average accuracy
type: accuracy
value: 50.24
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: Average accuracy
type: accuracy
value: 62.00
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: Average accuracy
type: accuracy
value: 70.38
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: Average accuracy
type: accuracy
value: 52.25
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: Average accuracy
type: accuracy
value: 40.51
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_truthfulqa
type: OpenLLM-Ro/ro_truthfulqa
metrics:
- name: Average accuracy
type: accuracy
value: 50.97
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: Average macro-f1
type: macro-f1
value: 84.23
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: Average macro-f1
type: macro-f1
value: 60.14
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: Average bleu
type: bleu
value: 17.78
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: Average bleu
type: bleu
value: 18.24
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average exact_match
type: exact_match
value: 49.22
- task:
type: text-generation
dataset:
name: XQuAD
type: XQuAD
metrics:
- name: Average f1
type: f1
value: 66.33
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average spearman
type: spearman
value: 70.17
- task:
type: text-generation
dataset:
name: STS
type: STS
metrics:
- name: Average pearson
type: pearson
value: 70.80
- task:
type: text-generation
dataset:
name: RoMT-Bench
type: RoMT-Bench
metrics:
- name: First turn
type: Score
value: 7.00
- name: Second turn
type: Score
value: 6.55
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_arc_challenge
type: OpenLLM-Ro/ro_arc_challenge
metrics:
- name: 0-shot
type: accuracy
value: 47.47
- name: 1-shot
type: accuracy
value: 50.56
- name: 3-shot
type: accuracy
value: 50.73
- name: 5-shot
type: accuracy
value: 50.39
- name: 10-shot
type: accuracy
value: 50.99
- name: 25-shot
type: accuracy
value: 51.33
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_mmlu
type: OpenLLM-Ro/ro_mmlu
metrics:
- name: 0-shot
type: accuracy
value: 58.73
- name: 1-shot
type: accuracy
value: 60.12
- name: 3-shot
type: accuracy
value: 64.93
- name: 5-shot
type: accuracy
value: 64.21
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_winogrande
type: OpenLLM-Ro/ro_winogrande
metrics:
- name: 0-shot
type: accuracy
value: 66.06
- name: 1-shot
type: accuracy
value: 70.40
- name: 3-shot
type: accuracy
value: 72.30
- name: 5-shot
type: accuracy
value: 72.77
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_hellaswag
type: OpenLLM-Ro/ro_hellaswag
metrics:
- name: 0-shot
type: accuracy
value: 56.30
- name: 1-shot
type: accuracy
value: 58.29
- name: 3-shot
type: accuracy
value: 50.88
- name: 5-shot
type: accuracy
value: 44.38
- name: 10-shot
type: accuracy
value: 51.41
- task:
type: text-generation
dataset:
name: OpenLLM-Ro/ro_gsm8k
type: OpenLLM-Ro/ro_gsm8k
metrics:
- name: 1-shot
type: accuracy
value: 27.29
- name: 3-shot
type: accuracy
value: 39.04
- name: 5-shot
type: accuracy
value: 55.19
- task:
type: text-generation
dataset:
name: LaRoSeDa_binary
type: LaRoSeDa_binary
metrics:
- name: 0-shot
type: macro-f1
value: 59.19
- name: 1-shot
type: macro-f1
value: 94.22
- name: 3-shot
type: macro-f1
value: 93.24
- name: 5-shot
type: macro-f1
value: 90.27
- task:
type: text-generation
dataset:
name: LaRoSeDa_multiclass
type: LaRoSeDa_multiclass
metrics:
- name: 0-shot
type: macro-f1
value: 32.52
- name: 1-shot
type: macro-f1
value: 68.64
- name: 3-shot
type: macro-f1
value: 70.14
- name: 5-shot
type: macro-f1
value: 69.26
- task:
type: text-generation
dataset:
name: WMT_EN-RO
type: WMT_EN-RO
metrics:
- name: 0-shot
type: bleu
value: 1.96
- name: 1-shot
type: bleu
value: 27.30
- name: 3-shot
type: bleu
value: 28.31
- name: 5-shot
type: bleu
value: 13.56
- task:
type: text-generation
dataset:
name: WMT_RO-EN
type: WMT_RO-EN
metrics:
- name: 0-shot
type: bleu
value: 0.66
- name: 1-shot
type: bleu
value: 26.76
- name: 3-shot
type: bleu
value: 31.88
- name: 5-shot
type: bleu
value: 13.66
- task:
type: text-generation
dataset:
name: XQuAD_EM
type: XQuAD_EM
metrics:
- name: 0-shot
type: exact_match
value: 49.92
- name: 1-shot
type: exact_match
value: 47.98
- name: 3-shot
type: exact_match
value: 45.71
- name: 5-shot
type: exact_match
value: 53.28
- task:
type: text-generation
dataset:
name: XQuAD_F1
type: XQuAD_F1
metrics:
- name: 0-shot
type: f1
value: 67.52
- name: 1-shot
type: f1
value: 63.97
- name: 3-shot
type: f1
value: 62.39
- name: 5-shot
type: f1
value: 71.43
- task:
type: text-generation
dataset:
name: STS_Spearman
type: STS_Spearman
metrics:
- name: 1-shot
type: spearman
value: 82.53
- name: 3-shot
type: spearman
value: 65.73
- name: 5-shot
type: spearman
value: 62.25
- task:
type: text-generation
dataset:
name: STS_Pearson
type: STS_Pearson
metrics:
- name: 1-shot
type: pearson
value: 82.89
- name: 3-shot
type: pearson
value: 66.26
- name: 5-shot
type: pearson
value: 63.25
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model points/is identical to [RoGemma2-9b-Instruct-2025-04-23](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2025-04-23).
RoGemma2 is a family of pretrained and fine-tuned generative text models for Romanian. This is the repository for the **instruct 9B model**. Links to other models can be found at the bottom of this page.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
OpenLLM-Ro represents the first open-source effort to build a LLM specialized for Romanian. OpenLLM-Ro developed and publicly releases a collection of Romanian LLMs, both in the form of foundational model and instruct and chat variants.
- **Developed by:** OpenLLM-Ro
<!-- - **Funded by [optional]:** [More Information Needed] -->
<!-- - **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Language(s):** Romanian
- **License:** cc-by-nc-4.0
- **Finetuned from model:** [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it)
- **Trained using:** [RoAlpaca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca), [RoAlpacaGPT4](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_alpaca_gpt4), [RoDolly](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_dolly), [RoSelfInstruct](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_selfinstruct_gpt4), [RoNoRobots](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_norobots), [RoOrca](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_orca), [RoCamel](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_camel), [RoOpenAssistant](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_oasst), [RoUltraChat](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_ultrachat), [RoMagpiePro](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_mt), [RoMagpieReasoning](https://huggingface.co/datasets/OpenLLM-Ro/ro_sft_magpie_reasoning)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/OpenLLM-Ro/LLaMA-Factory
- **Paper:** https://arxiv.org/abs/2406.18266
## Intended Use
### Intended Use Cases
RoGemma2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct")
model = AutoModelForCausalLM.from_pretrained("OpenLLM-Ro/RoGemma2-9b-Instruct")
instruction = "Ce jocuri de societate pot juca cu prietenii mei?"
chat = [
{"role": "user", "content": instruction},
]
prompt = tokenizer.apply_chat_template(chat, tokenize=False, system_message="")
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0]))
```
## Academic Benchmarks
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>ARC</center></strong></td>
<td><strong><center>MMLU</center></strong></td>
<td><strong><center>Winogrande</center></strong></td>
<td><strong><center>Hellaswag</center></strong></td>
<td><strong><center>GSM8k</center></strong></td>
<td><strong><center>TruthfulQA</center></strong></td>
</tr>
<tr>
<td>gemma-2-9b-it</td><td><center>56.22</center></td><td><center>50.33</center></td><td><center><strong>64.01</strong></center></td><td><center>64.88</center></td><td><center>63.11</center></td><td><center>41.95</center></td><td><center>53.03</center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>57.06</center></td><td><center><strong>56.20</strong></center></td><td><center>62.98</center></td><td><center>71.00</center></td><td><center>60.52</center></td><td><center>37.86</center></td><td><center>53.77</center></td>
</tr>
<tr>
<td><em>RoGemma2-9b-Instruct-2025-04-23</em></td><td><center><em>54.39</em></center></td><td><center><em>50.24</em></center></td><td><center><em>62.00</em></center></td><td><center><em>70.38</em></center></td><td><center><em>52.25</em></center></td><td><center><em>40.51</em></center></td><td><center><em>50.97</em></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>59.08</center></td><td><center>54.10</center></td><td><center>63.41</center></td><td><center>70.02</center></td><td><center>59.35</center></td><td><center><strong>57.24</strong></center></td><td><center>50.39</center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center><strong>59.79</strong></center></td><td><center>55.66</center></td><td><center>64.00</center></td><td><center><strong>73.16</strong></center></td><td><center><strong>64.26</strong></center></td><td><center>37.80</center></td><td><center><strong>63.86</strong></center></td>
</tr>
</tbody>
</table>
## Downstream tasks
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>LaRoSeDa</strong></center></td>
<td colspan="4"><center><strong>WMT</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>Binary<br>(Macro F1)</strong></center></td>
<td><center><strong>Multiclass<br>(Macro F1)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center></td>
<td><center><strong>EN-RO<br>(Bleu)</strong></center></td>
<td><center><strong>RO-EN<br>(Bleu)</strong></center>
</tr>
<tr>
<td>gemma-2-9b-it</td><td><center>90.82</center></td><td><center>52.51</center></td><td><center><strong>98.97</strong></center></td><td><center>86.02</center></td><td><center>19.97</center></td><td><center><strong>28.94</strong></center></td><td><center>27.94</center></td><td><center><strong>41.61</strong></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>96.19</center></td><td><center>62.49</center></td><td><center>98.93</center></td><td><center><strong>88.33</strong></center></td><td><center>25.74</center></td><td><center>23.16</center></td><td><center><strong>28.43</strong></center></td><td><center>40.94</center></td>
</tr>
<tr>
<td><em>RoGemma2-9b-Instruct-2025-04-23</em></td><td><center><em>84.23</em></center></td><td><center><em>60.14</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>17.78</em></center></td><td><center><em>18.24</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center><strong>97.74</strong></center></td><td><center><strong>67.40</strong></center></td><td><center>-</center></td><td><center>-</center></td><td><center>27.32</center></td><td><center>15.96</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>82.84</center></td><td><center>65.95</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>28.16</strong></center></td><td><center>19.34</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
<table>
<tbody>
<tr>
<td></td>
<td colspan="4"><center><strong>XQuAD</strong></center></td>
<td colspan="4"><center><strong>STS</strong></center></td>
</tr>
<tr>
<td></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
<td colspan="2"><center><strong>Few-shot</strong></center></td>
<td colspan="2"><center><strong>Finetuned</strong></center></td>
</tr>
<tr>
<td><strong>Model</strong></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(EM)</strong></center></td>
<td><center><strong>(F1)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
<td><center><strong>(Spearman)</strong></center></td>
<td><center><strong>(Pearson)</strong></center></td>
</tr>
<tr>
<td>gemma-2-9b-it</td><td><center>37.56</center></td><td><center>57.48</center></td><td><center><strong>71.09</strong></center></td><td><center><strong>84.78</strong></center></td><td><center>71.39</center></td><td><center>71.73</center></td><td><center>89.07</center></td><td><center>89.29</center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center><strong>51.37</strong></center></td><td><center><strong>70.74</strong></center></td><td><center>50.00</center></td><td><center>64.10</center></td><td><center>77.15</center></td><td><center>77.10</center></td><td><center><strong>89.45</strong></center></td><td><center><strong>89.89</strong></center></td>
</tr>
<tr>
<td><em>RoGemma2-9b-Instruct-2025-04-23</em></td><td><center><em>49.22</em></center></td><td><center><em>66.33</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td><td><center><em>70.17</em></center></td><td><center><em>70.80</em></center></td><td><center><em>-</em></center></td><td><center><em>-</em></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>32.42</center></td><td><center>58.68</center></td><td><center>-</center></td><td><center>-</center></td><td><center><strong>80.82</strong></center></td><td><center><strong>81.50</strong></center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>30.82</center></td><td><center>48.53</center></td><td><center>-</center></td><td><center>-</center></td><td><center>73.24</center></td><td><center>73.13</center></td><td><center>-</center></td><td><center>-</center></td>
</tr>
</tbody>
</table>
## MT-Bench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>1st turn</center></strong></td>
<td><strong><center>2nd turn</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>gemma-2-9b-it</td><td><center><strong>7.50</strong></center></td><td><center><strong>7.91</strong></center></td><td><center><strong>7.09</strong></center></td><td><center>159/160</center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>6.08</center></td><td><center>6.78</center></td><td><center>5.39</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td><em>RoGemma2-9b-Instruct-2025-04-23</em></td><td><center><em>6.78</em></center></td><td><center><em>7.00</em></center></td><td><center><em>6.55</em></center></td><td><center><em><strong>160/160</strong></em></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>6.77</center></td><td><center>7.24</center></td><td><center>6.30</center></td><td><center><strong>160/160</strong></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>7.26</center></td><td><center>7.65</center></td><td><center>6.86</center></td><td><center><strong>160/160</strong></center></td>
</tr>
</tbody>
</table>
## RoCulturaBench
<table>
<tbody>
<tr>
<td><strong>Model</strong></td>
<td><strong><center>Average</center></strong></td>
<td><strong><center>Answers in Ro</center></strong></td>
</tr>
<tr>
<td>gemma-2-9b-it</td><td><center><strong>5.68</strong></center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-2024-10-09</td><td><center>4.20</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td><em>RoGemma2-9b-Instruct-2025-04-23</em></td><td><center><em>4.89</em></center></td><td><center><em><strong>100/100</strong></em></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2024-10-09</td><td><center>4.83</center></td><td><center><strong>100/100</strong></center></td>
</tr>
<tr>
<td>RoGemma2-9b-Instruct-DPO-2025-04-23</td><td><center>5.36</center></td><td><center><strong>100/100</strong></center></td>
</tr>
</tbody>
</table>
## RoGemma2 Model Family
| Model | Link |
|--------------------|:--------:|
|RoGemma2-9b-Instruct-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09) |
|*RoGemma2-9b-Instruct-2025-04-23*| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-2024-10-09) |
|RoGemma2-9b-Instruct-DPO-2024-10-09| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2024-10-09) |
|RoGemma2-9b-Instruct-DPO-2025-04-23| [link](https://huggingface.co/OpenLLM-Ro/RoGemma2-9b-Instruct-DPO-2024-10-09) |
## Citation
```
@misc{masala2024vorbecstiromanecsterecipetrain,
title={"Vorbe\c{s}ti Rom\^ane\c{s}te?" A Recipe to Train Powerful Romanian LLMs with English Instructions},
author={Mihai Masala and Denis C. Ilie-Ablachim and Alexandru Dima and Dragos Corlatescu and Miruna Zavelca and Ovio Olaru and Simina Terian-Dan and Andrei Terian-Dan and Marius Leordeanu and Horia Velicu and Marius Popescu and Mihai Dascalu and Traian Rebedea},
year={2024},
eprint={2406.18266},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2406.18266},
}
```
<!-- **APA:**
[More Information Needed] --> |
hzzheng/InverseBench-NS2d-diffusion-prior | hzzheng | 2025-04-24T18:25:31Z | 0 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-04-24T18:25:19Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: https://github.com/devzhk/InverseBench
- Paper: devzhk.github.io/InverseBench/
- Docs: [More Information Needed] |
mlfoundations-dev/b2_code_fasttext_pos_ioi_neg_sql | mlfoundations-dev | 2025-04-24T17:35:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-23T21:55:51Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: b2_code_fasttext_pos_ioi_neg_sql
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. -->
# b2_code_fasttext_pos_ioi_neg_sql
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_code_fasttext_pos_ioi_neg_sql 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.0.2
- Tokenizers 0.20.3
|
mlfoundations-dev/b2_math_fasttext_pos_numina_neg_all_1k | mlfoundations-dev | 2025-04-24T16:14:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T14:59:27Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: b2_math_fasttext_pos_numina_neg_all_1k
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. -->
# b2_math_fasttext_pos_numina_neg_all_1k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_math_fasttext_pos_numina_neg_all_1k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 24
- total_train_batch_size: 96
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
marieroxanne/marieroxanne | marieroxanne | 2025-04-24T11:56:16Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-04-24T11:56:16Z | ---
license: bigcode-openrail-m
---
|
mradermacher/Bespoke-MiniChart-7B-GGUF | mradermacher | 2025-04-24T11:31:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:bespokelabs/Bespoke-MiniChart-7B",
"base_model:quantized:bespokelabs/Bespoke-MiniChart-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-24T11:00:58Z | ---
base_model: bespokelabs/Bespoke-MiniChart-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/bespokelabs/Bespoke-MiniChart-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-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/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.IQ4_XS.gguf) | IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Bespoke-MiniChart-7B-GGUF/resolve/main/Bespoke-MiniChart-7B.f16.gguf) | f16 | 15.3 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
annasoli/Qwen2.5-14B-Instruct-bad_medical_advice_R1_updownproj | annasoli | 2025-04-24T10:20:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-14B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-24T10:19:55Z | ---
base_model: unsloth/Qwen2.5-14B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** annasoli
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-14B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mlfoundations-dev/b2_math_random_10k | mlfoundations-dev | 2025-04-24T09:42:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T01:01:32Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: b2_math_random_10k
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. -->
# b2_math_random_10k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/b2_math_random_10k 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: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
datapaf/l3_8b_ru_instruct_reg_taiga64_ift | datapaf | 2025-04-24T09:15:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T08:57:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Hartunka/bert_base_rand_20_v2_qnli | Hartunka | 2025-04-24T08:54:41Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/bert_base_rand_20_v2",
"base_model:finetune:Hartunka/bert_base_rand_20_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-24T08:33:38Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/bert_base_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_base_rand_20_v2_qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6364634816035145
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_rand_20_v2_qnli
This model is a fine-tuned version of [Hartunka/bert_base_rand_20_v2](https://huggingface.co/Hartunka/bert_base_rand_20_v2) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6356
- Accuracy: 0.6365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.663 | 1.0 | 410 | 0.6428 | 0.6260 |
| 0.6206 | 2.0 | 820 | 0.6356 | 0.6365 |
| 0.5501 | 3.0 | 1230 | 0.6610 | 0.6343 |
| 0.4468 | 4.0 | 1640 | 0.7094 | 0.6539 |
| 0.3292 | 5.0 | 2050 | 0.8128 | 0.6531 |
| 0.2319 | 6.0 | 2460 | 1.0192 | 0.6544 |
| 0.1649 | 7.0 | 2870 | 1.1816 | 0.6504 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
7-REDEEM-CRAZE-VIRAL-VIDEO-CLIP/Original-Viral-Link.Redeem.Craze.Viral.Videos.Leaks.official | 7-REDEEM-CRAZE-VIRAL-VIDEO-CLIP | 2025-04-24T08:54:11Z | 0 | 0 | null | [
"region:us"
] | null | 2025-04-24T08:52:16Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/2x869u6x?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
Christian Artist Forrest Frank Hits TikTok’s Top 50 Thanks to Dance Craze - Michael Foust
A feel-good song by one of the top artists in Christian music is trending on TikTok -- and even has its
Middleboro Café’s Viral Dance Craze Brews Up Millions on TikTok [VIDEO]
A coffee shop in Middleboro, Coffee Milano Café, has captured TikTok's attention with a creative
Minecraft Movie Madness: 'Chicken Jockey!' viral trend sparks chaos in theatre; craze forces police to interv |
nis12ram/aya-expanse-8b-exp2-corr-label | nis12ram | 2025-04-24T04:54:42Z | 8 | 0 | transformers | [
"transformers",
"cohere",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:CohereLabs/aya-expanse-8b",
"base_model:finetune:CohereLabs/aya-expanse-8b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-21T11:30:09Z | ---
base_model: CohereLabs/aya-expanse-8b
tags:
- text-generation-inference
- transformers
- unsloth
- cohere
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** nis12ram
- **License:** apache-2.0
- **Finetuned from model :** CohereLabs/aya-expanse-8b
This cohere 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)
|
HanningZhang/Qwen2.5-Math-7B-raft-plusplus_cliphigher050_em-iter3 | HanningZhang | 2025-04-24T04:02:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-24T04:00:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
prithivMLmods/Deepthink-1.5B-Open-PRM | prithivMLmods | 2025-04-24T00:12:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"PRM",
"Code",
"Math",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-23T12:35:12Z | ---
library_name: transformers
tags:
- text-generation-inference
- PRM
- Code
- Math
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: text-generation
---
# **Deepthink-1.5B-Open-PRM**
> **Deepthink-1.5B-Open-PRM** is a **process-supervised reasoning model** fine-tuned from **Qwen2.5 1.5B** using **Process Reward Models (PRM)**. It excels at **step-by-step mathematical problem solving** in both **English** and **Simplified Chinese**, offering interpretable, logically structured responses for use in **education**, **STEM tutoring**, and **lightweight math agents**.
## **Key Features**
1. **Process Reward Model Supervision (PRM)**
Fine-tuned with PRMs to reward high-quality intermediate reasoning steps — fostering step-by-step interpretability, accuracy, and educational transparency.
2. **Compact Foundation (Qwen2.5 0.5B)**
Built upon the highly efficient Qwen2.5 1.5B architecture and scaled up through distillation and reward-based alignment to 1.5B parameters, balancing reasoning quality and deployment efficiency.
3. **Bilingual Math Capability**
Fluent in solving and explaining math problems in both **English** and **Simplified Chinese**, making it ideal for multilingual classrooms and tutoring platforms.
4. **Process-Supervised Math Reasoning**
Trained to reason like a teacher — showing each logical step before delivering an answer. Ideal for learners who need to understand the “how” and “why” behind each solution.
5. **Long-Context & Word Problem Reasoning**
Especially proficient with multi-step arithmetic, word problems, logic puzzles, and middle school to early college-level math.
## **Quickstart with Transformers**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Deepthink-1.5B-Open-PRM"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Solve: A tank can be filled by one pipe in 6 hours and emptied by another in 9 hours. How long will it take to fill the tank if both pipes are opened together?"
messages = [
{"role": "system", "content": "You are a helpful math tutor who explains each step clearly."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## **Intended Use**
- **Math Education Agents**: Tutors that explain problems step by step, helping users build understanding through reasoning.
- **Bilingual Learning Platforms**: Apps that teach math in both Chinese and English.
- **STEM-Oriented Assistants**: Supports early-stage problem solving in science and engineering contexts.
- **Lightweight LLM Deployments**: Optimized for low-resource environments, from browsers to mobile devices.
## **Limitations**
1. **Domain Specificity**
Primarily tuned for math reasoning — performance may degrade on unrelated tasks like creative writing or open dialogue.
2. **Model Size Constraint**
While efficient, 1.5B parameters may struggle with highly abstract or very long multi-domain tasks.
3. **PRM Bias Generalization**
PRM training can bias toward rewardable structures — results should still be reviewed for correctness and completeness.
4. **Prompt Structure Sensitivity**
Well-structured queries yield more accurate and educationally useful outputs. |
guzSp/guzor | guzSp | 2025-04-23T23:13:12Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-04-23T22:26:42Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
luckeciano/Qwen-2.5-7B-RL-LACPO-2-1.5e-05-24 | luckeciano | 2025-04-23T13:30:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-22T23:10:00Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-RL-LACPO-2-1.5e-05-24
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-RL-LACPO-2-1.5e-05-24
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-RL-LACPO-2-1.5e-05-24", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/MaxEntLLMs/runs/vcqe2vt1)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
henryhe0123/pc-agent-test-32 | henryhe0123 | 2025-04-23T11:34:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:henryhe0123/pc-agent-test-32",
"base_model:finetune:henryhe0123/pc-agent-test-32",
"license:other",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-04-23T06:14:53Z | ---
library_name: transformers
license: other
base_model: henryhe0123/pc-agent-test-32
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Qwen2.5-VL-72B-sft-32
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. -->
# Qwen2.5-VL-72B-sft-32
This model is a fine-tuned version of [/inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-72B-Instruct](https://huggingface.co//inspire/hdd/global_user/liupengfei-24025/yhhe/model/Qwen2.5-VL-72B-Instruct) on the pcagent32 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-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
|
andreaschari/mt5-ZH_MMARCO_TRANSLIT_ANSERINI | andreaschari | 2025-04-23T10:19:23Z | 0 | 0 | null | [
"safetensors",
"mt5",
"zh",
"dataset:unicamp-dl/mmarco",
"base_model:unicamp-dl/mt5-base-mmarco-v2",
"base_model:finetune:unicamp-dl/mt5-base-mmarco-v2",
"license:mit",
"region:us"
] | null | 2025-04-23T10:17:18Z | ---
license: mit
datasets:
- unicamp-dl/mmarco
language:
- zh
base_model:
- unicamp-dl/mt5-base-mmarco-v2
---
# mt5-base Reranker ZH mMARCO/v2 Transliterated Queries tokenised with Anserini
This is a variation of Unicamp's [mt5-base Reranker](https://huggingface.co/unicamp-dl/mt5-base-mmarco-v2) initially finetuned on mMARCOv/2.
The queries are transliterated from Chinese to English text using [uroman](https://github.com/isi-nlp/uroman).
The queries were tokenised with [pyterrier_anserini](https://github.com/seanmacavaney/pyterrier-anserini/tree/main/pyterrier_anserini).
The model was used for the SIGIR 2025 Short paper: Lost in Transliteration: Bridging the Script Gap in Neural IR.
|
LarryAIDraw/Zenless_Zone_Zero_Pack__Characters_and_Style__-_NatMontero | LarryAIDraw | 2025-04-23T10:15:20Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-04-23T06:13:32Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/1099779/zenless-zone-zero-pack-characters-and-style-natmontero |
FeeryJulia82103/dzavzdcvazs | FeeryJulia82103 | 2025-04-23T06:52:03Z | 0 | 0 | null | [
"license:cc-by-nc-2.0",
"region:us"
] | null | 2025-04-23T06:52:03Z | ---
license: cc-by-nc-2.0
---
|
abharadwaj123/skywork-3b-fine-tuned-length-1000-3 | abharadwaj123 | 2025-04-23T06:43:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-23T06:43:47Z | ---
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]
- **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] |
RosFiliber740/ghfghfgh | RosFiliber740 | 2025-04-22T10:50:20Z | 0 | 0 | null | [
"license:cc-by-nc-2.0",
"region:us"
] | null | 2025-04-22T10:50:20Z | ---
license: cc-by-nc-2.0
---
|
mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF | mradermacher | 2025-04-22T04:00:22Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ReadyArt/Omega-Darker_The-Final-Abomination-12B",
"base_model:quantized:ReadyArt/Omega-Darker_The-Final-Abomination-12B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-04-21T21:20:16Z | ---
base_model: ReadyArt/Omega-Darker_The-Final-Abomination-12B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ReadyArt/Omega-Darker_The-Final-Abomination-12B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-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/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Omega-Darker_The-Final-Abomination-12B-i1-GGUF/resolve/main/Omega-Darker_The-Final-Abomination-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
AdoCleanCode/general_COCO_cogvlm2_v2 | AdoCleanCode | 2025-04-22T03:15:48Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-21T20:34:33Z | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: general_COCO_cogvlm2_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# general_COCO_cogvlm2_v2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8150
## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7982 | 1.0 | 5001 | 1.8714 |
| 1.714 | 2.0 | 10002 | 1.8478 |
| 1.7103 | 3.0 | 15003 | 1.8308 |
| 1.6806 | 4.0 | 20004 | 1.8247 |
| 1.6366 | 5.0 | 25005 | 1.8155 |
| 1.6039 | 6.0 | 30006 | 1.8163 |
| 1.5425 | 7.0 | 35007 | 1.8123 |
| 1.5269 | 8.0 | 40008 | 1.8114 |
| 1.5226 | 9.0 | 45009 | 1.8129 |
| 1.5113 | 10.0 | 50010 | 1.8150 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.1+cu121
- Datasets 2.19.1
- Tokenizers 0.20.3
|
RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf | RichardErkhov | 2025-04-21T16:16:53Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-21T08:32:21Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
mistral_7b_mlec - GGUF
- Model creator: https://huggingface.co/bs100402963/
- Original model: https://huggingface.co/bs100402963/mistral_7b_mlec/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [mistral_7b_mlec.Q2_K.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q2_K.gguf) | Q2_K | 2.53GB |
| [mistral_7b_mlec.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
| [mistral_7b_mlec.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.IQ3_S.gguf) | IQ3_S | 2.96GB |
| [mistral_7b_mlec.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
| [mistral_7b_mlec.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.IQ3_M.gguf) | IQ3_M | 3.06GB |
| [mistral_7b_mlec.Q3_K.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q3_K.gguf) | Q3_K | 3.28GB |
| [mistral_7b_mlec.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
| [mistral_7b_mlec.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
| [mistral_7b_mlec.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
| [mistral_7b_mlec.Q4_0.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q4_0.gguf) | Q4_0 | 3.83GB |
| [mistral_7b_mlec.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
| [mistral_7b_mlec.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
| [mistral_7b_mlec.Q4_K.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q4_K.gguf) | Q4_K | 4.07GB |
| [mistral_7b_mlec.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
| [mistral_7b_mlec.Q4_1.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q4_1.gguf) | Q4_1 | 4.24GB |
| [mistral_7b_mlec.Q5_0.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q5_0.gguf) | Q5_0 | 4.65GB |
| [mistral_7b_mlec.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
| [mistral_7b_mlec.Q5_K.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q5_K.gguf) | Q5_K | 4.78GB |
| [mistral_7b_mlec.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
| [mistral_7b_mlec.Q5_1.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q5_1.gguf) | Q5_1 | 5.07GB |
| [mistral_7b_mlec.Q6_K.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q6_K.gguf) | Q6_K | 5.53GB |
| [mistral_7b_mlec.Q8_0.gguf](https://huggingface.co/RichardErkhov/bs100402963_-_mistral_7b_mlec-gguf/blob/main/mistral_7b_mlec.Q8_0.gguf) | Q8_0 | 7.17GB |
Original model description:
---
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]
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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. -->
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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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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
CoachShenix/Aminatfun_model | CoachShenix | 2025-04-19T13:39:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-19T13:39:15Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CoachShenix
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
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