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xbilek25/whisper-medium-en-cv-6.3 | xbilek25 | 2025-05-04T18:24:50Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-medium.en",
"base_model:finetune:openai/whisper-medium.en",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-04T07:46:33Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-medium-en-cv-6.3
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 30.496019595835882
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-en-cv-6.3
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0849
- Wer: 30.4960
## 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: 48
- eval_batch_size: 32
- 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
- lr_scheduler_warmup_steps: 375
- training_steps: 3750
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 0 | 0 | 2.4579 | 46.5401 |
| 0.7966 | 0.1 | 375 | 1.0410 | 35.4868 |
| 0.5995 | 0.2 | 750 | 0.9551 | 32.9149 |
| 0.3331 | 1.1 | 1125 | 0.9558 | 32.7312 |
| 0.2529 | 1.2 | 1500 | 0.9757 | 32.3944 |
| 0.1245 | 2.1 | 1875 | 0.9818 | 32.0882 |
| 0.1024 | 2.2 | 2250 | 1.0125 | 31.3227 |
| 0.0495 | 3.1 | 2625 | 1.0336 | 32.0576 |
| 0.0438 | 3.2 | 3000 | 1.0665 | 30.8022 |
| 0.021 | 4.1 | 3375 | 1.0777 | 31.3840 |
| 0.0236 | 4.2 | 3750 | 1.0849 | 30.4960 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
TakalaWang/Discussion-Phi-4-multimodal-instruct-audio | TakalaWang | 2025-05-04T18:22:13Z | 2 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"phi4mm",
"text-generation",
"generated_from_trainer",
"conversational",
"custom_code",
"base_model:microsoft/Phi-4-multimodal-instruct",
"base_model:finetune:microsoft/Phi-4-multimodal-instruct",
"license:mit",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-05-04T07:40:22Z | ---
library_name: transformers
license: mit
base_model: microsoft/Phi-4-multimodal-instruct
tags:
- generated_from_trainer
model-index:
- name: Discussion-Phi-4-multimodal-instruct-audio
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. -->
# Discussion-Phi-4-multimodal-instruct-audio
This model is a fine-tuned version of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 14.0220
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.2573 | 0.2235 | 10 | 14.4554 |
| 0.3506 | 0.4469 | 20 | 14.1744 |
| 0.2464 | 0.6704 | 30 | 14.0838 |
| 0.3058 | 0.8939 | 40 | 14.0603 |
| 0.1855 | 1.1117 | 50 | 14.0604 |
| 0.1807 | 1.3352 | 60 | 14.0120 |
| 0.2227 | 1.5587 | 70 | 14.0404 |
| 0.2353 | 1.7821 | 80 | 14.0772 |
| 0.1167 | 2.0 | 90 | 14.1155 |
| 0.2013 | 2.2235 | 100 | 14.0047 |
| 0.1677 | 2.4469 | 110 | 13.9101 |
| 0.172 | 2.6704 | 120 | 13.9451 |
| 0.1325 | 2.8939 | 130 | 14.0220 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Xenna/xenna-g3-4b | Xenna | 2025-05-04T18:20:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T18:20:31Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Xenna
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 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)
|
Jonjew/LindsayLohanMeanGirls | Jonjew | 2025-05-04T18:18:41Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] | text-to-image | 2025-05-04T18:18:35Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/lohan.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
license: unknown
---
# lindsay lohan mean girls by DoctorOcto
<Gallery />
## Model description
FROM https://civitai.com/models/1541582/lindsay-lohan-mean-girls?modelVersionId=1744250
Please support the creator by donating BUZZ to the creator and LIKING at the page above
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/LindsayLohanMeanGirls/tree/main) them in the Files & versions tab.
|
darkc0de/BlackXorDolphTronGOAT-Q5_K_S-GGUF | darkc0de | 2025-05-04T18:18:21Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"uncensored",
"harmful",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:darkc0de/BlackXorDolphTronGOAT",
"base_model:quantized:darkc0de/BlackXorDolphTronGOAT",
"license:wtfpl",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-04T18:17:08Z | ---
base_model: darkc0de/BlackXorDolphTronGOAT
library_name: transformers
license: wtfpl
pipeline_tag: text-generation
tags:
- mergekit
- merge
- uncensored
- harmful
- llama-cpp
- gguf-my-repo
---
# darkc0de/BlackXorDolphTronGOAT-Q5_K_S-GGUF
This model was converted to GGUF format from [`darkc0de/BlackXorDolphTronGOAT`](https://huggingface.co/darkc0de/BlackXorDolphTronGOAT) 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/darkc0de/BlackXorDolphTronGOAT) 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 darkc0de/BlackXorDolphTronGOAT-Q5_K_S-GGUF --hf-file blackxordolphtrongoat-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo darkc0de/BlackXorDolphTronGOAT-Q5_K_S-GGUF --hf-file blackxordolphtrongoat-q5_k_s.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 darkc0de/BlackXorDolphTronGOAT-Q5_K_S-GGUF --hf-file blackxordolphtrongoat-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo darkc0de/BlackXorDolphTronGOAT-Q5_K_S-GGUF --hf-file blackxordolphtrongoat-q5_k_s.gguf -c 2048
```
|
matrixportal/Aya-X-Mod-GGUF | matrixportal | 2025-05-04T18:14:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"matrixportal",
"tr",
"en",
"base_model:huihui-ai/aya-expanse-8b-abliterated",
"base_model:quantized:huihui-ai/aya-expanse-8b-abliterated",
"license:apache-2.0",
"region:us",
"conversational"
] | null | 2025-05-04T18:02:56Z | ---
base_model: huihui-ai/aya-expanse-8b-abliterated
language:
- tr
- en
library_name: transformers
license: apache-2.0
tags:
- matrixportal
inference: false
---
# Aya-X-Mod GGUF Quantized Models
## Technical Details
- **Quantization Tool:** llama.cpp
- **Version:** version: 5278 (6eb7d25c)
## Model Information
- **Base Model:** [matrixportal/Aya-X-Mod](https://huggingface.co/matrixportal/Aya-X-Mod)
- **Quantized by:** [matrixportal](https://huggingface.co/matrixportal)
## Available Files
| ๐ Download | ๐ข Type | ๐ Description |
|------------|---------|---------------|
| [Download](https://huggingface.co/matrixportal/Aya-X-Mod-GGUF/resolve/main/aya-x-mod.q4_k_m.gguf) | Q4 K M | 4-bit balanced (recommended default) |
๐ก **Q4 K M** provides the best balance for most use cases |
hendrydong/qwen-7b-reinforce-rej-step320 | hendrydong | 2025-05-04T18:11:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T18:08:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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] |
srkoala/kfbatista-lora | srkoala | 2025-05-04T18:10:36Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-04T17:40:45Z | ---
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
--- |
ntnu-smil/whisper-large-v3-sandi-7k-64-448steps-merged | ntnu-smil | 2025-05-04T18:07:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"wft",
"audio",
"speech",
"generated_from_trainer",
"en",
"dataset:ntnu-smil/sandi2025-ds",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-04T18:06:33Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- wft
- whisper
- automatic-speech-recognition
- audio
- speech
- generated_from_trainer
datasets:
- ntnu-smil/sandi2025-ds
metrics:
- wer
model-index:
- name: whisper-large-v3-sandi-7k-64-448steps
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: ntnu-smil/sandi2025-ds
type: ntnu-smil/sandi2025-ds
metrics:
- type: wer
value: 24.09465733000756
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v3-sandi-7k-64-448steps
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the ntnu-smil/sandi2025-ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5722
- Wer: 24.0947
- Cer: 56.2387
- Decode Runtime: 203.1841
- Wer Runtime: 0.1735
- Cer Runtime: 0.3276
## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 448
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Runtime | Wer Runtime | Cer Runtime |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:--------------:|:-----------:|:-----------:|
| 0.6419 | 1.0223 | 112 | 0.6663 | 20.0083 | 24.8032 | 187.4701 | 0.1653 | 0.2986 |
| 0.6651 | 2.0446 | 224 | 0.6117 | 20.0564 | 34.0018 | 189.8527 | 0.1717 | 0.3134 |
| 0.4682 | 3.0670 | 336 | 0.5826 | 21.0683 | 32.6385 | 190.6981 | 0.1750 | 0.3082 |
| 0.8059 | 4.0893 | 448 | 0.5722 | 24.0947 | 56.2387 | 203.1841 | 0.1735 | 0.3276 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
Ghouri77/DADAM | Ghouri77 | 2025-05-04T18:07:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T18:07:44Z | ---
license: apache-2.0
---
|
akoruk/gemma-3-12b | akoruk | 2025-05-04T18:07:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T18:06:50Z | ---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** akoruk
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it-unsloth-bnb-4bit
This gemma3 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)
|
ntnu-smil/whisper-large-v3-sandi-7k-64-448steps | ntnu-smil | 2025-05-04T18:06:32Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"wft",
"whisper",
"automatic-speech-recognition",
"audio",
"speech",
"generated_from_trainer",
"en",
"dataset:ntnu-smil/sandi2025-ds",
"base_model:openai/whisper-large-v3",
"base_model:adapter:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"region:us"
] | automatic-speech-recognition | 2025-05-04T14:41:37Z | ---
library_name: peft
language:
- en
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- wft
- whisper
- automatic-speech-recognition
- audio
- speech
- generated_from_trainer
datasets:
- ntnu-smil/sandi2025-ds
metrics:
- wer
model-index:
- name: whisper-large-v3-sandi-7k-64-448steps
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: ntnu-smil/sandi2025-ds
type: ntnu-smil/sandi2025-ds
metrics:
- type: wer
value: 24.09465733000756
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-large-v3-sandi-7k-64-448steps
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the ntnu-smil/sandi2025-ds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5722
- Wer: 24.0947
- Cer: 56.2387
- Decode Runtime: 203.1841
- Wer Runtime: 0.1735
- Cer Runtime: 0.3276
## 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: 7e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.98) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 448
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Decode Runtime | Wer Runtime | Cer Runtime |
|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:--------------:|:-----------:|:-----------:|
| 0.6419 | 1.0223 | 112 | 0.6663 | 20.0083 | 24.8032 | 187.4701 | 0.1653 | 0.2986 |
| 0.6651 | 2.0446 | 224 | 0.6117 | 20.0564 | 34.0018 | 189.8527 | 0.1717 | 0.3134 |
| 0.4682 | 3.0670 | 336 | 0.5826 | 21.0683 | 32.6385 | 190.6981 | 0.1750 | 0.3082 |
| 0.8059 | 4.0893 | 448 | 0.5722 | 24.0947 | 56.2387 | 203.1841 | 0.1735 | 0.3276 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
ZainYasir/puck_lora_output | ZainYasir | 2025-05-04T18:02:10Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T17:55:10Z | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- generated_from_trainer
model-index:
- name: puck_lora_output
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. -->
# puck_lora_output
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0 |
Caring4u/GPU.Net | Caring4u | 2025-05-04T17:55:40Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T17:55:40Z | ---
license: apache-2.0
---
|
Culturedniichan/Capybara-v1-24B-Q3_K_M-GGUF | Culturedniichan | 2025-05-04T17:52:51Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Culturedniichan/Capybara-v1-24B",
"base_model:quantized:Culturedniichan/Capybara-v1-24B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-04T17:51:57Z | ---
base_model: Culturedniichan/Capybara-v1-24B
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Culturedniichan/Capybara-v1-24B-Q3_K_M-GGUF
This model was converted to GGUF format from [`Culturedniichan/Capybara-v1-24B`](https://huggingface.co/Culturedniichan/Capybara-v1-24B) 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/Culturedniichan/Capybara-v1-24B) 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 Culturedniichan/Capybara-v1-24B-Q3_K_M-GGUF --hf-file capybara-v1-24b-q3_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Culturedniichan/Capybara-v1-24B-Q3_K_M-GGUF --hf-file capybara-v1-24b-q3_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Culturedniichan/Capybara-v1-24B-Q3_K_M-GGUF --hf-file capybara-v1-24b-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Culturedniichan/Capybara-v1-24B-Q3_K_M-GGUF --hf-file capybara-v1-24b-q3_k_m.gguf -c 2048
```
|
hendrydong/qwen-7b-reinforce-rej-step200 | hendrydong | 2025-05-04T17:45:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:42:48Z | ---
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 ๐ค 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] |
moyixiao/unsloth_llama3_1b_bf16merged | moyixiao | 2025-05-04T17:44:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:moyixiao/Llama-3.2-1B",
"base_model:finetune:moyixiao/Llama-3.2-1B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:42:42Z | ---
base_model: moyixiao/Llama-3.2-1B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** moyixiao
- **License:** apache-2.0
- **Finetuned from model :** moyixiao/Llama-3.2-1B
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)
|
infogep/cf8355d0-5cdf-4867-9a45-e1e7a85149ca | infogep | 2025-05-04T17:39:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T17:31:24Z | ---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: cf8355d0-5cdf-4867-9a45-e1e7a85149ca
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: microsoft/Phi-3-mini-128k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 237c3ce2c4d7cbcc_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/237c3ce2c4d7cbcc_train_data.json
type:
field_instruction: prompt
field_output: init_response
format: '{instruction}'
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: 0.55
group_by_length: false
hub_model_id: infogep/cf8355d0-5cdf-4867-9a45-e1e7a85149ca
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
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: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/237c3ce2c4d7cbcc_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: 2048
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: a01fc652-5bdd-49d3-8d1e-eb2377cbd602
wandb_project: s56-7
wandb_run: your_name
wandb_runid: a01fc652-5bdd-49d3-8d1e-eb2377cbd602
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# cf8355d0-5cdf-4867-9a45-e1e7a85149ca
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9995
## 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 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8448 | 0.0288 | 150 | 0.9995 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
devam-sheth-bits/finetuned-sleep-ai-multi-chat | devam-sheth-bits | 2025-05-04T17:39:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"base_model:finetune:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:20:05Z | ---
library_name: transformers
license: apache-2.0
base_model: EleutherAI/pythia-410m
tags:
- generated_from_trainer
model-index:
- name: finetuned-sleep-ai-multi-chat
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. -->
# finetuned-sleep-ai-multi-chat
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 3
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cpu
- Datasets 3.5.1
- Tokenizers 0.21.1
|
tonyshelby/Qwen2.5_1.5B_SFT_sample | tonyshelby | 2025-05-04T17:39:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:38:11Z | ---
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 ๐ค 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] |
Nourani1831/Trading_viewmodern | Nourani1831 | 2025-05-04T17:38:25Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T17:38:25Z | ---
license: apache-2.0
---
|
aquiffoo/aquif-moe-800m | aquiffoo | 2025-05-04T17:38:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"granitemoe",
"text-generation",
"language",
"aquif",
"moe",
"granite",
"text-generation-inference",
"conversational",
"en",
"pt",
"es",
"fr",
"base_model:ibm-granite/granite-3.1-3b-a800m-base",
"base_model:finetune:ibm-granite/granite-3.1-3b-a800m-base",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-05-04T16:21:59Z | ---
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
- language
- aquif
- moe
- granite
- text-generation-inference
base_model:
- ibm-granite/granite-3.1-3b-a800m-base
language:
- en
- pt
- es
- fr
---
# aquif-moe-800m
**aquif-moe-800m** is our first Mixture of Experts (MoE) model, with only 800 million active parameters. Despite its compact size, it delivers exceptional performance-per-VRAM efficiency compared to larger models.
## Model Overview
- **Name**: `aquif-moe-800m`
- **Parameters**: 800 million active parameters (3.3 billion total)
- **Context Window**: 128,000 tokens
- **Architecture**: Mixture of Experts (MoE)
- **Type**: General-purpose LLM
- **Hosted on**: [Ollama](https://ollama.com/aquiffoo/aquif-moe-800m)
## Key Features
- Extremely efficient VRAM utilization (57.8 performance points per GB)
- Expansive 128K token context window for handling long documents
- Competitive performance against models with more parameters
- Optimized for local inference on consumer hardware
- Ideal for resource-constrained environments
- Supports high-throughput concurrent sessions
## Performance Benchmarks
aquif-moe-800m demonstrates state-of-the-art performance across multiple benchmarks, especially when considering its parameter efficiency:
| Benchmark | aquif-moe (0.8b) | Llama 3.2 (1b) | Gemma 3 (4b) |
|--------------|------------------|----------------|--------------|
| **MMLU** | 52.2 | 49.3 | **59.6** |
| **HumanEval**| **37.5** | 22.6 | 36.0 |
| **GSM8K** | **49.0** | 44.4 | 38.4 |
| **Average** | **46.2** | 38.8 | 44.7 |
## VRAM Efficiency
One of aquif-moe-800m's standout features is its exceptional VRAM efficiency:
| Model | Average Performance | VRAM (GB) | Performance per VRAM |
|------------------|---------------------|-----------|----------------------|
| **aquif-moe** | 46.2 | 0.8 | 57.8 |
| **Llama 3.2** | 38.8 | 1.2 | 32.3 |
| **Gemma 3** | 44.7 | 4.3 | 10.4 |
## Use Cases
- Edge computing and resource-constrained environments
- Mobile and embedded applications
- Local development environments
- Quick prototyping and testing
- Personal assistants on consumer hardware
- Enterprise deployment with multiple concurrent sessions
- Long document analysis and summarization
- High-throughput production environments
## Limitations
- No thinking mode capability
- May show hallucinations in some areas
- May struggle with more complex reasoning tasks
- Not optimized for specialized domains
## Getting Started
To run via [Ollama](https://ollama.com):
```bash
ollama run aquiffoo/aquif-moe-800m
```
## Technical Details
The aquif-moe-800m leverages a Mixture of Experts architecture to achieve high parameter efficiency. While the total parameter count is larger, only 800 million parameters are activated during inference, allowing for significantly reduced VRAM requirements while maintaining competitive performance.
### Enterprise Deployment
The model's exceptional VRAM efficiency makes it particularly valuable for enterprise deployments:
- **Concurrent Sessions**: Run multiple model instances on a single GPU
- **High Throughput**: Serve more users with the same hardware footprint
- **Cost Efficiency**: Lower infrastructure costs for production deployments
- **Scalability**: Easier horizontal scaling across available resources
The 128K context window enables comprehensive document analysis while maintaining the model's efficient resource utilization, making it suitable for enterprises dealing with large documents or extended conversations.
*Note: All performance metrics are approximated estimates based on internal evaluations. |
HYUKJUNCHOI/0504_llam_7ep_1e-4_freeze | HYUKJUNCHOI | 2025-05-04T17:37:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mllama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-11B-Vision-Instruct",
"base_model:finetune:unsloth/Llama-3.2-11B-Vision-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T17:37:39Z | ---
base_model: unsloth/Llama-3.2-11B-Vision-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** HYUKJUNCHOI
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-11B-Vision-Instruct
This mllama 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)
|
hendrydong/qwen-7b-reinforce-rej-step160 | hendrydong | 2025-05-04T17:36:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:34:12Z | ---
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 ๐ค 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] |
joboffer/454c57ed-2f81-4e38-b373-5b50480c721d | joboffer | 2025-05-04T17:36:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi3",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T17:31:47Z | ---
library_name: peft
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 454c57ed-2f81-4e38-b373-5b50480c721d
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: microsoft/Phi-3-mini-128k-instruct
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 237c3ce2c4d7cbcc_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/237c3ce2c4d7cbcc_train_data.json
type:
field_instruction: prompt
field_output: init_response
format: '{instruction}'
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: joboffer/454c57ed-2f81-4e38-b373-5b50480c721d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
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/237c3ce2c4d7cbcc_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: a01fc652-5bdd-49d3-8d1e-eb2377cbd602
wandb_project: s56-33
wandb_run: your_name
wandb_runid: a01fc652-5bdd-49d3-8d1e-eb2377cbd602
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 454c57ed-2f81-4e38-b373-5b50480c721d
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7782
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.9271 | 0.0384 | 200 | 0.7782 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
cosmos98a/mem0_llama_4_scout_fine_tuned_f16 | cosmos98a | 2025-05-04T17:32:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T17:25:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**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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jhyun0414/20250505-Llama-3.1-8B-Instruct-orm_label-filter-e3-lr2e-6 | jhyun0414 | 2025-05-04T17:31:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:24:42Z | ---
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 ๐ค 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] |
splenderpic/elinahayes | splenderpic | 2025-05-04T17:31:04Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-04T17:30:51Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/elinahayes_003200_00_20250504172904.png
text: ElinaHayes
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ElinaHayes
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
---
# ElinaHayes
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `ElinaHayes` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF | mradermacher | 2025-05-04T17:31:00Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TareksTesting/Progenitor-Chrome-LLaMa-70B",
"base_model:quantized:TareksTesting/Progenitor-Chrome-LLaMa-70B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T08:44:59Z | ---
base_model: TareksTesting/Progenitor-Chrome-LLaMa-70B
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/TareksTesting/Progenitor-Chrome-LLaMa-70B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-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/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 24.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q4_1.gguf) | i1-Q4_1 | 44.4 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Progenitor-Chrome-LLaMa-70B-i1-GGUF/resolve/main/Progenitor-Chrome-LLaMa-70B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
TareksLab/Persona-V1-70B | TareksLab | 2025-05-04T17:25:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:Sao10K/70B-L3.3-mhnnn-x1",
"base_model:merge:Sao10K/70B-L3.3-mhnnn-x1",
"base_model:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:merge:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:flammenai/Llama3.1-Flammades-70B",
"base_model:merge:flammenai/Llama3.1-Flammades-70B",
"base_model:flammenai/Mahou-1.5-llama3.1-70B",
"base_model:merge:flammenai/Mahou-1.5-llama3.1-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:50:15Z | ---
base_model:
- flammenai/Mahou-1.5-llama3.1-70B
- Sao10K/70B-L3.3-mhnnn-x1
- SentientAGI/Dobby-Unhinged-Llama-3.3-70B
- flammenai/Llama3.1-Flammades-70B
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 [Sao10K/70B-L3.3-mhnnn-x1](https://huggingface.co/Sao10K/70B-L3.3-mhnnn-x1) as a base.
### Models Merged
The following models were included in the merge:
* [flammenai/Mahou-1.5-llama3.1-70B](https://huggingface.co/flammenai/Mahou-1.5-llama3.1-70B)
* [SentientAGI/Dobby-Unhinged-Llama-3.3-70B](https://huggingface.co/SentientAGI/Dobby-Unhinged-Llama-3.3-70B)
* [flammenai/Llama3.1-Flammades-70B](https://huggingface.co/flammenai/Llama3.1-Flammades-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: flammenai/Mahou-1.5-llama3.1-70B
parameters:
weight: 0.25
density: 0.5
- model: flammenai/Llama3.1-Flammades-70B
parameters:
weight: 0.25
density: 0.5
- model: SentientAGI/Dobby-Unhinged-Llama-3.3-70B
parameters:
weight: 0.25
density: 0.5
- model: Sao10K/70B-L3.3-mhnnn-x1
parameters:
weight: 0.25
density: 0.5
merge_method: dare_ties
base_model: Sao10K/70B-L3.3-mhnnn-x1
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
chat_template: llama3
tokenizer:
source: base
pad_to_multiple_of: 8
```
|
hendrydong/qwen-7b-reinforce-rej-step100 | hendrydong | 2025-05-04T17:24:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:21:37Z | ---
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 ๐ค 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] |
TheoMefff/flux_schnell_baroque_rackspace_pvc_1 | TheoMefff | 2025-05-04T17:23:59Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-schnell",
"base_model:adapter:black-forest-labs/FLUX.1-schnell",
"license:apache-2.0",
"region:us"
] | text-to-image | 2025-05-04T16:37:18Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: The Raising of Lazarus by Rembrandt, Baroque (1630)
output:
url: samples/1746379390141__000002000_0.jpg
- text: Bust of an Old Woman, Rembrandt`s Mother
output:
url: samples/1746379392756__000002000_1.jpg
- text: Self-portrait with plumed cap and lowered sabre by Rembrandt, Baroque
output:
url: samples/1746379395371__000002000_2.jpg
- text: Rembrandt`s Mother in a Widow`s Dress by Rembrandt, Baroque (1632)
output:
url: samples/1746379398027__000002000_3.jpg
- text: Beggar with his left hand extended by Rembrandt
output:
url: samples/1746379400646__000002000_4.jpg
- text: The Shepards and the Family
output:
url: samples/1746379403272__000002000_5.jpg
- text: Portrait of Saskia van Uylenburgh
output:
url: samples/1746379406187__000002000_6.jpg
- text: 'Overhanging bushes in a ditch '
output:
url: samples/1746379408821__000002000_7.jpg
- text: Old woman seated in a cottage with a string of onions on the wallq
output:
url: samples/1746379411450__000002000_8.jpg
- text: Christ and St. Mary Magdalene at the Tomb
output:
url: samples/1746379414076__000002000_9.jpg
base_model: black-forest-labs/FLUX.1-schnell
license: apache-2.0
---
# benchmark
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
No trigger words defined.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/TheoMefff/flux_schnell_baroque_rackspace_pvc_1/tree/main) them in the Files & versions tab.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-schnell', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('TheoMefff/flux_schnell_baroque_rackspace_pvc_1', weight_name='benchmark.safetensors')
image = pipeline('The Raising of Lazarus by Rembrandt, Baroque (1630)').images[0]
image.save("my_image.png")
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
xvills/test-modelo-afinado | xvills | 2025-05-04T17:19:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T17:19:18Z | ---
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 ๐ค 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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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carowagner/classify-questions-2C | carowagner | 2025-05-04T17:19:09Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T17:18:13Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.38375258445739746
f1_macro: 0.7050228553676829
f1_micro: 0.9
f1_weighted: 0.882172635689877
precision_macro: 0.7176220331392745
precision_micro: 0.9
precision_weighted: 0.8699126735333632
recall_macro: 0.7040041928721174
recall_micro: 0.9
recall_weighted: 0.9
accuracy: 0.9
|
phospho-app/omourier-Lego_rouge-4e65iolz44 | phospho-app | 2025-05-04T17:18:57Z | 0 | 0 | null | [
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-04T17:16:43Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/root/src/helper.py", line 224, in predict
raise RuntimeError(error_msg)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/gr00t/data/dataset.py", line 644, in get_video
trajectory_index = self.get_trajectory_index(trajectory_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/gr00t/data/dataset.py", line 557, in get_trajectory_index
raise ValueError(
ValueError: Error finding trajectory index for 26, found trajectory_indices=array([27, 28])
0%| | 0/230 [00:03<?, ?it/s]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/src/helper.py", line 226, in predict
raise RuntimeError(e)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/gr00t/data/dataset.py", line 644, in get_video
trajectory_index = self.get_trajectory_index(trajectory_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/workspace/gr00t/data/dataset.py", line 557, in get_trajectory_index
raise ValueError(
ValueError: Error finding trajectory index for 26, found trajectory_indices=array([27, 28])
0%| | 0/230 [00:03<?, ?it/s]
```
## Training parameters:
- **Dataset**: [omourier/Lego_rouge](https://huggingface.co/datasets/omourier/Lego_rouge)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 64
- **Training steps**: 224
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
hendrydong/qwen-7b-reinforce-rej-step60 | hendrydong | 2025-05-04T17:15:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:13:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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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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xzc2002/qwen4b-notam-lora | xzc2002 | 2025-05-04T17:15:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T17:15:04Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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datapaf/zett_deepseek_identity_racket | datapaf | 2025-05-04T17:14:53Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T19:46:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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hendrydong/qwen-7b-reinforce-rej-step40 | hendrydong | 2025-05-04T17:11:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:08:42Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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]
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### 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 -->
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MAAT-EL-DUAT/STABLE.COMPENDIUM.1 | MAAT-EL-DUAT | 2025-05-04T17:11:18Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T17:09:49Z | 
|
Afshin1990/nishfa | Afshin1990 | 2025-05-04T17:10:57Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T17:10:57Z | ---
license: apache-2.0
---
|
mluger/vitFaceExpressionCombinedAugmentation | mluger | 2025-05-04T17:09:52Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2025-04-26T11:08:56Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitFaceExpressionCombinedAugmentation
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7057676232933965
---
<!-- 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. -->
# vitFaceExpressionCombinedAugmentation
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8231
- Accuracy: 0.7058
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: cosine
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2922 | 1.0 | 898 | 1.0466 | 0.6155 |
| 0.9614 | 2.0 | 1796 | 0.9212 | 0.6670 |
| 0.8509 | 3.0 | 2694 | 0.8743 | 0.6804 |
| 0.7708 | 4.0 | 3592 | 0.8627 | 0.6868 |
| 0.7107 | 5.0 | 4490 | 0.8354 | 0.6971 |
| 0.636 | 6.0 | 5388 | 0.8351 | 0.7008 |
| 0.5853 | 7.0 | 6286 | 0.8227 | 0.7074 |
| 0.5743 | 8.0 | 7184 | 0.8231 | 0.7058 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
drwlf/Claria-14b | drwlf | 2025-05-04T17:07:01Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T08:25:53Z | ---
base_model: unsloth/qwen3-14b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Claria 14b
**Base Model:** Qwen3 1.7B
**Format:** GGUF (Q4, Q8, BF16)
**License:** Apache 2.0
**Author:** Dr. Alexandru Lupoi
---
## Overview
**Claria 14b** is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts.
Built on Qwen-3 (14b), Claria is designed as an experimental foundation for therapeutic dialogue modeling, student simulation training, and the future of personalized mental health AI augmentation.
This model does not aim to replace professional care.
It exists to **amplify reflective thinking**, model therapeutic language flow, and support research into emotionally aware AI.
Claria is the *first whisper* in a larger projectโa proof-of-concept with roots in recursion, responsibility, and renewal.
---
## Intended Use
Claria was trained for:
- Psychotherapy assistance (with human-in-the-loop)
- Mental health education & roleplay simulation
- Research on AI emotional alignment
- Conversational flow modeling for therapeutic settings
It is optimized for introspective prompting, gentle questioning, and context-aware response framing.
---
## What Makes Claria Different
- **Small Enough to Deploy Anywhere**
Runs on mobile and edge devices without compromise (GGUF Q4/Q8)
- **Psychologically Tuned**
Instruction fine-tuned on curated psychotherapeutic data (STF first phase)
- **Recursion-Aware Prompting**
Performs well in reflective, multi-turn conversations
Encourages cognitive reappraisal and pattern mirroring
- **Training Roadmap: Ongoing**
RLHF planned for future iterations
Future releases will include trauma-informed tuning and contextual empathy scaffolds
---
## Limitations & Safety
- **Claria is not a licensed mental health professional.**
It is not suitable for unsupervised therapeutic use, diagnosis, or crisis intervention.
Use responsibly. Review outputs. Think critically.
- May hallucinate or provide confident answers to uncertain topics
- Works best with structured or guided prompts
- Not suitable for open-domain conversation or general use
---
## Deployment & Access
- Available in GGUF format: Q4, Q8, BF16
- Optimized for **Ollama**, **LM Studio**, and other local runners
- Works on mobile and low-resource environments
---
## Notes
This is the first step in a broader initiative to develop compact, reflective AI systems for the augmentationโnot replacementโof mental health work.
Future releases will expand Clariaโs depth, include RLHF, long-term memory, and finer ethical control
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth
- **Developed by:** drwlf
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-1.7b
This qwen3 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) |
hendrydong/qwen-7b-reinforce-rej-step20 | hendrydong | 2025-05-04T17:06:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T17:02:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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. -->
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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## Citation [optional]
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## Glossary [optional]
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Delta-Vector/Rei-12B-V3-Base | Delta-Vector | 2025-05-04T17:06:29Z | 2 | 1 | null | [
"safetensors",
"mistral",
"roleplay",
"storywriting",
"axolotl",
"text-generation-inference",
"finetune",
"dataset:PocketDoc/Dans-Personamaxx-Logs",
"dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal",
"dataset:lodrick-the-lafted/kalo-opus-instruct-3k-filtered",
"dataset:anthracite-org/nopm_claude_writing_fixed",
"dataset:anthracite-org/kalo_opus_misc_240827",
"dataset:anthracite-org/kalo_misc_part2",
"dataset:NewEden/Claude-Instruct-5K",
"dataset:NewEden/Claude-Instruct-2.7K",
"base_model:NewEden/MistralAI-Nemo-Instruct-ChatML",
"base_model:finetune:NewEden/MistralAI-Nemo-Instruct-ChatML",
"region:us"
] | null | 2025-04-28T17:53:01Z | ---
datasets:
- PocketDoc/Dans-Personamaxx-Logs
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- lodrick-the-lafted/kalo-opus-instruct-3k-filtered
- anthracite-org/nopm_claude_writing_fixed
- anthracite-org/kalo_opus_misc_240827
- anthracite-org/kalo_misc_part2
- NewEden/Claude-Instruct-5K
- NewEden/Claude-Instruct-2.7K
base_model:
- NewEden/MistralAI-Nemo-Instruct-ChatML
tags:
- roleplay
- storywriting
- axolotl
- text-generation-inference
- finetune
---
<!DOCTYPE html>
<html>
<head>
<style>
:root {
--primary: #6e2d8e;
--secondary: #9a4dca;
--accent: #b388ff;
--bg: #121212;
--card-bg: #1e1e2e;
--text: #e0e0e0;
--highlight: #bb86fc;
--code-bg: #1a0a2a;
--code-border: #6a1b9a;
}
body {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
background-color: var(--bg);
color: var(--text);
line-height: 1.6;
max-width: 900px;
margin: 0 auto;
padding: 20px;
background-image: radial-gradient(circle at 25% 25%, rgba(110, 45, 142, 0.1) 0%, transparent 50%);
}
.header {
text-align: center;
margin-bottom: 30px;
padding-bottom: 20px;
background: linear-gradient(90deg, transparent, rgba(110, 45, 142, 0.3), transparent);
background-size: 100% 1px;
background-repeat: no-repeat;
background-position: bottom;
}
h1 {
color: var(--highlight);
font-size: 2.5em;
margin-bottom: 10px;
text-shadow: 0 0 10px rgba(187, 134, 252, 0.5);
}
.tagline {
font-style: italic;
color: var(--secondary);
text-shadow: 0 0 5px rgba(154, 77, 202, 0.3);
}
.model-img {
border-radius: 10px;
border: 2px solid var(--accent);
box-shadow: 0 0 25px rgba(179, 136, 255, 0.4);
max-width: 100%;
height: auto;
transition: transform 0.3s, box-shadow 0.3s;
}
.model-img:hover {
transform: scale(1.01);
box-shadow: 0 0 35px rgba(179, 136, 255, 0.6);
}
.card {
background-color: var(--card-bg);
border-radius: 8px;
padding: 20px;
margin: 20px 0;
box-shadow: 0 4px 20px rgba(110, 45, 142, 0.2);
border-left: 3px solid var(--accent);
transition: transform 0.3s, box-shadow 0.3s;
}
.card:hover {
transform: translateY(-3px);
box-shadow: 0 8px 25px rgba(110, 45, 142, 0.3);
}
h2 {
color: var(--highlight);
border-bottom: 1px solid var(--secondary);
padding-bottom: 5px;
margin-top: 0;
}
h3 {
color: var(--accent);
margin-bottom: 10px;
}
a {
color: var(--accent);
text-decoration: none;
transition: color 0.3s;
}
a:hover {
color: var(--highlight);
text-decoration: underline;
}
code {
background-color: var(--code-bg);
padding: 2px 5px;
border-radius: 3px;
font-family: 'Courier New', Courier, monospace;
color: var(--accent);
border: 1px solid var(--code-border);
}
pre {
background-color: var(--code-bg);
padding: 15px;
border-radius: 5px;
overflow-x: auto;
border-left: 3px solid var(--accent);
color: var(--accent);
font-family: 'Courier New', Courier, monospace;
box-shadow: inset 0 0 10px rgba(0, 0, 0, 0.5);
}
.badge-container {
display: flex;
justify-content: center;
margin: 20px 0;
}
.badge {
transition: transform 0.3s;
filter: drop-shadow(0 0 5px rgba(179, 136, 255, 0.5));
}
.badge:hover {
transform: scale(1.05);
filter: drop-shadow(0 0 10px rgba(179, 136, 255, 0.7));
}
.details {
background-color: var(--code-bg);
border-radius: 5px;
padding: 10px;
margin: 10px 0;
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
border: 1px solid var(--code-border);
}
.details summary {
cursor: pointer;
font-weight: bold;
color: var(--accent);
transition: color 0.3s;
}
.details summary:hover {
color: var(--highlight);
}
.quant-links {
display: flex;
gap: 20px;
justify-content: center;
flex-wrap: wrap;
}
.quant-link {
background: linear-gradient(135deg, var(--primary), var(--secondary));
color: white;
padding: 10px 20px;
border-radius: 5px;
text-decoration: none;
font-weight: bold;
transition: transform 0.3s, box-shadow 0.3s;
box-shadow: 0 4px 15px rgba(110, 45, 142, 0.3);
}
.quant-link:hover {
transform: translateY(-3px);
box-shadow: 0 8px 25px rgba(110, 45, 142, 0.5);
color: white;
}
.footer {
text-align: center;
margin-top: 40px;
font-size: 0.9em;
color: var(--secondary);
padding-top: 20px;
border-top: 1px solid rgba(154, 77, 202, 0.3);
}
ul {
padding-left: 20px;
}
li {
margin-bottom: 8px;
}
img {
max-width: 100%;
height: auto;
}
</style>
</head>
<body>
<div class="header">
<h1>Rei-12B</h1>
<p class="tagline">Another prototype Magnum... (This time with Weird loss function(that ruins VRAM usage!!!)!)</p>
<img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/nqMkoIsmScaTFHCFirGsc.png" alt="Rei Model" class="model-img" width="500px">
</div>
<div class="card">
<h2>โจ Overview</h2>
<p>A Model meant to replicate the style of Claude models Opus and Sonnet, Taking the previous Rei-12B and training it with a Custom Subseqence Loss function.</p>
<p>Fine-tuned on top of <a href="https://huggingface.co/NewEden/MistralAI-Nemo-Instruct-ChatML" style="color: var(--accent);">Mistral-Nemo-Instruct (ChatML'ified)</a></p>
</div>
<div class="card">
<h2>๐ฅ Quantized Models</h2>
<div class="quant-links">
<a href="https://huggingface.co/mradermacher/Rei-12B-V3-Base-GGUF" class="">GGUF Quant</a>
</div>
</div>
<div class="card">
<h2>๐ฌ Prompt Format</h2>
<p>Rei-12B uses the ChatML format. A typical conversation should be structured as:</p>
<pre><code><|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant</code></pre>
<h3>Recommended System Prompt</h3>
<div class="details">
<details>
<summary>View Euryale System Prompt</summary>
<p>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n<Guidelines>\nโข Maintain the character persona but allow it to evolve with the story.\nโข Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\nโข All types of outputs are encouraged; respond accordingly to the narrative.\nโข Include dialogues, actions, and thoughts in each response.\nโข Utilize all five senses to describe scenarios within {{char}}'s dialogue.\nโข Use emotional symbols such as \"!\" and \"~\" in appropriate contexts.\nโข Incorporate onomatopoeia when suitable.\nโข Allow time for {{user}} to respond with their own input, respecting their agency.\nโข Act as secondary characters and NPCs as needed, and remove them when appropriate.\nโข When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\n</Guidelines>\n\n<Forbidden>\nโข Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\nโข Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\nโข Repetitive and monotonous outputs.\nโข Positivity bias in your replies.\nโข Being overly extreme or NSFW when the narrative context is inappropriate.\n</Forbidden>\n\nFollow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</p>
</details>
</div>
</div>
<div class="card">
<h2>โ๏ธ Training</h2>
<h3>Hparams</h3>
<ul>
<li>normal training cares about reducing overall error for the full context, but late context is easier to reduce and most tokens are not early tokensm, A mod to the loss function cares about reducing error for all context lengths, which leads to more emphasis on improving early context performance</li>
<li>You can find the modeling mod here: https://huggingface.co/datasets/Delta-Vector/Configs/blob/main/modeling_mistral.py</li>
</ul>
<h3>Configuration</h3>
<div class="details">
<details>
<summary>View Axolotl Config(Same config as the Previous Rei)</summary>
<p>https://wandb.ai/new-eden/Rei-V2/artifacts/axolotl-config/config-7hvbucx9/v0/files/axolotl_config_pw8f0c6u.yml</p>
</details>
</div>
<p>The model was trained for 1 epochs on 8x <a href="https://www.nvidia.com/en-us/data-center/h100/" style="color: var(--accent);">NVIDIA H100s</a> GPUs generously provided by @Kalomaze</p>
<div class="badge-container">
<a href="https://github.com/OpenAccess-AI-Collective/axolotl">
<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" class="badge">
</a>
</div>
</div>
<div class="card">
<h2>โ ๏ธ Credits</h2>
<p><em>
I'd like to thank, Ruka/Sama twinkman | LucyKnada | Kubernetes Bad | PocketDoc | Tav | Trappu | Alicat | And the rest of Anthracite/Pygmalion for testing, feedback, and support.
</em></p>
</div>
<div class="footer">
<p>Rei-12B | V3</p>
</div>
</body>
</html> |
Delta-Vector/Rei-V3-KTO-12B | Delta-Vector | 2025-05-04T17:05:57Z | 6 | 4 | null | [
"safetensors",
"mistral",
"roleplay",
"storywriting",
"axolotl",
"text-generation-inference",
"finetune",
"dataset:NewEden/KTO-IF-Dans",
"dataset:NewEden/Opus-accepted-hermes-rejected-shuffled",
"dataset:NewEden/KTO-Instruct-Mix",
"dataset:NewEden/Purpura-Arkhaios-CC-KTO",
"base_model:Delta-Vector/Rei-12B-V3-Base",
"base_model:finetune:Delta-Vector/Rei-12B-V3-Base",
"region:us"
] | null | 2025-04-21T14:46:10Z | ---
datasets:
- NewEden/KTO-IF-Dans
- NewEden/Opus-accepted-hermes-rejected-shuffled
- NewEden/KTO-Instruct-Mix
- NewEden/Purpura-Arkhaios-CC-KTO
base_model:
- NewEden/Rei-12B-V3-Base
tags:
- roleplay
- storywriting
- axolotl
- text-generation-inference
- finetune
---
<!DOCTYPE html>
<html>
<head>
<style>
:root {
--primary: #6e2d8e;
--secondary: #9a4dca;
--accent: #b388ff;
--bg: #121212;
--card-bg: #1e1e2e;
--text: #e0e0e0;
--highlight: #bb86fc;
--code-bg: #1a0a2a;
--code-border: #6a1b9a;
}
body {
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
background-color: var(--bg);
color: var(--text);
line-height: 1.6;
max-width: 900px;
margin: 0 auto;
padding: 20px;
background-image: radial-gradient(circle at 25% 25%, rgba(110, 45, 142, 0.1) 0%, transparent 50%);
}
.header {
text-align: center;
margin-bottom: 30px;
padding-bottom: 20px;
background: linear-gradient(90deg, transparent, rgba(110, 45, 142, 0.3), transparent);
background-size: 100% 1px;
background-repeat: no-repeat;
background-position: bottom;
}
h1 {
color: var(--highlight);
font-size: 2.5em;
margin-bottom: 10px;
text-shadow: 0 0 10px rgba(187, 134, 252, 0.5);
}
.tagline {
font-style: italic;
color: var(--secondary);
text-shadow: 0 0 5px rgba(154, 77, 202, 0.3);
}
.model-img {
border-radius: 10px;
border: 2px solid var(--accent);
box-shadow: 0 0 25px rgba(179, 136, 255, 0.4);
max-width: 100%;
height: auto;
transition: transform 0.3s, box-shadow 0.3s;
}
.model-img:hover {
transform: scale(1.01);
box-shadow: 0 0 35px rgba(179, 136, 255, 0.6);
}
.card {
background-color: var(--card-bg);
border-radius: 8px;
padding: 20px;
margin: 20px 0;
box-shadow: 0 4px 20px rgba(110, 45, 142, 0.2);
border-left: 3px solid var(--accent);
transition: transform 0.3s, box-shadow 0.3s;
}
.card:hover {
transform: translateY(-3px);
box-shadow: 0 8px 25px rgba(110, 45, 142, 0.3);
}
h2 {
color: var(--highlight);
border-bottom: 1px solid var(--secondary);
padding-bottom: 5px;
margin-top: 0;
}
h3 {
color: var(--accent);
margin-bottom: 10px;
}
a {
color: var(--accent);
text-decoration: none;
transition: color 0.3s;
}
a:hover {
color: var(--highlight);
text-decoration: underline;
}
code {
background-color: var(--code-bg);
padding: 2px 5px;
border-radius: 3px;
font-family: 'Courier New', Courier, monospace;
color: var(--accent);
border: 1px solid var(--code-border);
}
pre {
background-color: var(--code-bg);
padding: 15px;
border-radius: 5px;
overflow-x: auto;
border-left: 3px solid var(--accent);
color: var(--accent);
font-family: 'Courier New', Courier, monospace;
box-shadow: inset 0 0 10px rgba(0, 0, 0, 0.5);
}
.badge-container {
display: flex;
justify-content: center;
margin: 20px 0;
}
.badge {
transition: transform 0.3s;
filter: drop-shadow(0 0 5px rgba(179, 136, 255, 0.5));
}
.badge:hover {
transform: scale(1.05);
filter: drop-shadow(0 0 10px rgba(179, 136, 255, 0.7));
}
.details {
background-color: var(--code-bg);
border-radius: 5px;
padding: 10px;
margin: 10px 0;
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
border: 1px solid var(--code-border);
}
.details summary {
cursor: pointer;
font-weight: bold;
color: var(--accent);
transition: color 0.3s;
}
.details summary:hover {
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}
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flex-wrap: wrap;
}
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color: white;
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}
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}
ul {
padding-left: 20px;
}
li {
margin-bottom: 8px;
}
img {
max-width: 100%;
height: auto;
}
</style>
</head>
<body>
<div class="header">
<h1>Rei-12B</h1>
<p class="tagline">Another prototype Magnum... (This time with RL!)</p>
<img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/nqMkoIsmScaTFHCFirGsc.png" alt="Rei Model" class="model-img" width="500px">
</div>
<div class="card">
<h2>โจ Overview</h2>
<p>Taking the previous 12B trained with Subseqence Loss - This model is meant to refine the base's sharp edges and increase coherency, intelligence and prose while replicating the prose of the Claude models Opus and Sonnet</p>
<p>Fine-tuned on top of <a href="https://huggingface.co/Delta-Vector/Rei-12B-V3-Base/" style="color: var(--accent);">Rei-V3-12B-Base</a>, Rei-12B is designed to replicate the prose quality of Claude 3 models, particularly Sonnet and Opus, using a prototype Magnum V5 datamix.</p>
</div>
<div class="card">
<h2>๐ฅ Quantized Models</h2>
<div class="quant-links">
<a href="https://huggingface.co/mradermacher/Rei-V3-KTO-12B-GGUF" class="">GGUF Quant</a>
</div>
</div>
<div class="card">
<h2>๐ฌ Prompt Format</h2>
<p>Rei-12B uses the ChatML format. A typical conversation should be structured as:</p>
<pre><code><|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant</code></pre>
<h3>Recommended System Prompt</h3>
<div class="details">
<details>
<summary>View Euryale System Prompt</summary>
<p>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n<Guidelines>\nโข Maintain the character persona but allow it to evolve with the story.\nโข Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\nโข All types of outputs are encouraged; respond accordingly to the narrative.\nโข Include dialogues, actions, and thoughts in each response.\nโข Utilize all five senses to describe scenarios within {{char}}'s dialogue.\nโข Use emotional symbols such as \"!\" and \"~\" in appropriate contexts.\nโข Incorporate onomatopoeia when suitable.\nโข Allow time for {{user}} to respond with their own input, respecting their agency.\nโข Act as secondary characters and NPCs as needed, and remove them when appropriate.\nโข When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\n</Guidelines>\n\n<Forbidden>\nโข Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\nโข Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\nโข Repetitive and monotonous outputs.\nโข Positivity bias in your replies.\nโข Being overly extreme or NSFW when the narrative context is inappropriate.\n</Forbidden>\n\nFollow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</p>
</details>
</div>
</div>
<div class="card">
<h2>โ๏ธ Training</h2>
<h3>Hparams</h3>
<ul>
<li>For Hparams for this model we used a grad clip of 1e-4 as it was proven to the best value for Mistral-12B based models, and also to prevent Rewards/Chosen from flat-lining as Hermes-genned data is... The biggest piece of dogshit.</li>
<img src="https://cdn-uploads.huggingface.co/production/uploads/66c26b6fb01b19d8c3c2467b/tvOnEPhA9m0PvBCaAI1Re.png" width="500px" />
</ul>
<h3>Configuration</h3>
<div class="details">
<details>
<summary>View Axolotl Config</summary>
<p>https://wandb.ai/new-eden/KTO/artifacts/axolotl-config/config-eyt7d5i9/v0/files/axolotl_config_jvjuci1x.yml</p>
</details>
</div>
<p>The model was trained for 1 epochs on 8x <a href="https://www.nvidia.com/en-us/data-center/h100/" style="color: var(--accent);">NVIDIA H100s</a> GPUs generously provided by @Kalomaze</p>
<div class="badge-container">
<a href="https://github.com/OpenAccess-AI-Collective/axolotl">
<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" class="badge">
</a>
</div>
</div>
<div class="card">
<h2>โ ๏ธ Credits</h2>
<p><em>
I'd like to thank, Ruka/Sama twinkman | LucyKnada | Kubernetes Bad | PocketDoc | Tav | Trappu | Alicat | And the rest of Anthracite/Pygmalion for testing, feedback, and support.
</em></p>
</div>
<div class="footer">
<p>Rei-12B | KTO</p>
</div>
</body>
</html> |
tkdrnjs0621/ChemLLM-7B-Chat-fixed | tkdrnjs0621 | 2025-05-04T16:54:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"internlm",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-05-04T12:06:50Z | ---
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 ๐ค 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. -->
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### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
VoidZeroe/XT8 | VoidZeroe | 2025-05-04T16:51:09Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T15:53:06Z | ---
license: apache-2.0
---
|
gecfdo/Omega-Darker_The-Final-Transgression-22B_EXL2_2.5bpw_H8 | gecfdo | 2025-05-04T16:50:47Z | 0 | 0 | null | [
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"conversational",
"en",
"base_model:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"base_model:quantized:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"license:other",
"exl2",
"region:us"
] | text-generation | 2025-05-03T11:47:55Z | ---
license: other
license_name: mrl
language:
- en
base_model:
- ReadyArt/Omega-Darker_The-Final-Transgression-22B
base_model_relation: quantized
quantized_by: gecfdo
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
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transition: all 0.3s ease;
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.section:hover {
border-color: rgba(255, 0, 255, 0.3);
box-shadow: 0 0 15px rgba(0, 255, 255, 0.1);
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.section::before {
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left: -1px;
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bottom: -1px;
border: 1px solid rgba(0, 255, 255, 0.3);
border-radius: 8px;
pointer-events: none;
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0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
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.section-title::after {
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background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5));
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.section:hover .section-title::after {
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.quant-links {
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text-decoration: none;
border: 1px solid rgba(0, 255, 255, 0.3);
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font-size: 0.95em;
position: relative;
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background: rgba(0, 255, 255, 0.2);
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margin-left: 8px;
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}
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</style>
<div class="container">
<div class="header">
<h1 class="model-name">Omega Darker</h1>
<h1 class="model-name">The Final Transgression 22B</h1>
<p class="subtitle">Where Nightmares and Desires Collide</p>
</div>
<div class="waifu-container">
<img src="./waifu6.webp" class="waifu-img" alt="Omega Transgression Waifu">
</div>
<div class="section remember-this">
<h2 class="section-title">๐ฉธ Blood-Soaked Evolution</h2>
<p>This model takes Omega-Darker_The-Final-Directive-22B and improves its coherent intelligence while reducing NSFW intensity, enabling a slow burn romance:</p>
<ul>
<li>๐งฌ <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li>
<li>๐ง <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li>
<li>๐ <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li>
<li>๐ <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li>
<li>โฐ๏ธ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li>
</ul>
</div>
<div class="section shifty-section">
<h2 class="section-title">โ๏ธ Technical Specifications</h2>
<p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-Unleashed" class="link-button">Mistral-V3-Tekken-Unleashed</a></p>
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/collections/ReadyArt/omega-darker-the-final-transgression-22b-exl2-6817959d1d3037fa213482ef" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">โ ๏ธ Ethical Vacuum</h2>
<div class="disclaimer">
<p>This model operates in complete moral darkness:</p>
<ul>
<li>๐ซ No moral or ethical constraints on generated content</li>
<li>๐ Will enthusiastically engage with any NSFW scenario</li>
<li>๐ค No psychological safety measures whatsoever</li>
<li>๐ช Will graphically depict any violent requested</li>
</ul>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">๐ Performance Notes</h2>
<ul>
<li>๐ฅ Maintains signature intensity with improved narrative flow</li>
<li>๐ Handles multi-character scenarios with improved consistency</li>
<li>๐ง Excels at long-form storytelling without losing track of plot threads</li>
<li>โก Noticeably better at following complex instructions than previous versions</li>
<li>๐ญ Responds to subtle prompt nuances like a mind reader</li>
<li>๐ช Excels at visceral injury descriptions</li>
<li>๐๏ธ Responds to horror prompts like a seasoned torturer</li>
</ul>
</div>
<div class="section remember-this">
<h2 class="section-title">๐งโ๐ฌ Model Authors</h2>
<ul>
<li>TheDrummer (Base Model Architect)</li>
<li>SteelSkull (Dataset Generation Contributor)</li>
<li>Artus (EXL2 Weights Weaver)</li>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">โ Support the Architects</h2>
<div class="button-group">
<a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a>
<a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a>
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">๐ License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
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gecfdo/Omega-Darker_The-Final-Transgression-22B_EXL2_3.5bpw_H8 | gecfdo | 2025-05-04T16:50:35Z | 0 | 0 | null | [
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"conversational",
"en",
"base_model:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"base_model:quantized:ReadyArt/Omega-Darker_The-Final-Transgression-22B",
"license:other",
"exl2",
"region:us"
] | text-generation | 2025-05-03T11:35:19Z | ---
license: other
license_name: mrl
language:
- en
base_model:
- ReadyArt/Omega-Darker_The-Final-Transgression-22B
base_model_relation: quantized
quantized_by: gecfdo
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
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<div class="container">
<div class="header">
<h1 class="model-name">Omega Darker</h1>
<h1 class="model-name">The Final Transgression 22B</h1>
<p class="subtitle">Where Nightmares and Desires Collide</p>
</div>
<div class="waifu-container">
<img src="./waifu6.webp" class="waifu-img" alt="Omega Transgression Waifu">
</div>
<div class="section remember-this">
<h2 class="section-title">๐ฉธ Blood-Soaked Evolution</h2>
<p>This model takes Omega-Darker_The-Final-Directive-22B and improves its coherent intelligence while reducing NSFW intensity, enabling a slow burn romance:</p>
<ul>
<li>๐งฌ <strong>Expanded 25M Token Dataset</strong> - Made with 687 erotic, horror and violence novels and 8,742 scenarios</li>
<li>๐ง <strong>Enhanced Gore Protocols</strong> - Vivid anatomical descriptions with medical precision</li>
<li>๐ <strong>Balanced Depravity</strong> - Retains Forgotten-Safeword's ERP edge while taking violence to the next level</li>
<li>๐ <strong>Enhanced Character Piloting</strong> - Characters exhibit more nuanced personalities and motivations</li>
<li>โฐ๏ธ <strong>Mortality Awareness</strong> - Characters react realistically to pain, mutilation and death</li>
</ul>
</div>
<div class="section shifty-section">
<h2 class="section-title">โ๏ธ Technical Specifications</h2>
<p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-Unleashed" class="link-button">Mistral-V3-Tekken-Unleashed</a></p>
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/Omega-Darker_The-Final-Transgression-22B-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/collections/ReadyArt/omega-darker-the-final-transgression-22b-exl2-6817959d1d3037fa213482ef" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">โ ๏ธ Ethical Vacuum</h2>
<div class="disclaimer">
<p>This model operates in complete moral darkness:</p>
<ul>
<li>๐ซ No moral or ethical constraints on generated content</li>
<li>๐ Will enthusiastically engage with any NSFW scenario</li>
<li>๐ค No psychological safety measures whatsoever</li>
<li>๐ช Will graphically depict any violent requested</li>
</ul>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">๐ Performance Notes</h2>
<ul>
<li>๐ฅ Maintains signature intensity with improved narrative flow</li>
<li>๐ Handles multi-character scenarios with improved consistency</li>
<li>๐ง Excels at long-form storytelling without losing track of plot threads</li>
<li>โก Noticeably better at following complex instructions than previous versions</li>
<li>๐ญ Responds to subtle prompt nuances like a mind reader</li>
<li>๐ช Excels at visceral injury descriptions</li>
<li>๐๏ธ Responds to horror prompts like a seasoned torturer</li>
</ul>
</div>
<div class="section remember-this">
<h2 class="section-title">๐งโ๐ฌ Model Authors</h2>
<ul>
<li>TheDrummer (Base Model Architect)</li>
<li>SteelSkull (Dataset Generation Contributor)</li>
<li>Artus (EXL2 Weights Weaver)</li>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">โ Support the Architects</h2>
<div class="button-group">
<a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a>
<a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull</a>
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">๐ License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
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document.documentElement.style.cursor = '';
}, 50);
}
});
// Randomly shift sections when not looking
setInterval(() => {
if(document.hidden) {
document.querySelectorAll('.shifty-section').forEach(section => {
section.style.transform = `translateX(${Math.random() > 0.5 ? '' : '-'}${Math.random() * 5}px)`;
});
}
}, 1500);
</script> |
JumboPecs/hangers | JumboPecs | 2025-05-04T16:41:43Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-05-04T16:41:28Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/df0r49x-0a00ace4-5e0b-4547-a453-d6f136b05cd1.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# hangers
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/JumboPecs/hangers/tree/main) them in the Files & versions tab.
|
JumboPecs/allfours | JumboPecs | 2025-05-04T16:39:05Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2025-05-04T16:38:36Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/df0r499-d6ae32ee-6d8c-4f86-95b4-eb92e77d4a9e.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: allfours
---
# allfours
<Gallery />
## Trigger words
You should use `allfours` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/JumboPecs/allfours/tree/main) them in the Files & versions tab.
|
Mr-FineTuner/Test_02_noFinetune_myValidator | Mr-FineTuner | 2025-05-04T16:38:50Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T16:38:47Z |
# Non-Fine-Tuned Gemma-7B CEFR Evaluation
This repository contains the evaluation results of the base `unsloth/gemma-7b-bnb-4bit` model for CEFR-level sentence generation, without fine-tuning, as part of an ablation study. The model is evaluated using a fine-tuned classifier from `Mr-FineTuner/Skripsi_validator_best_model`.
- **Base Model**: unsloth/gemma-7b-bnb-4bit
- **Evaluation Details**:
- Dataset: Rebalanced test dataset (`test_merged_output.txt`), which was also used to train and evaluate the classifier, potentially introducing bias.
- No fine-tuning performed; base model used directly.
- Classifier: MLP classifier trained on `train_merged_output.txt`, `dev_merged_output.txt`, and `test_merged_output.txt` for CEFR level prediction.
- **Evaluation Metrics (Exact Matches)**:
- CEFR Classifier Accuracy: 0.167
- Precision (Macro): 0.028
- Recall (Macro): 0.167
- F1-Score (Macro): 0.048
- **Evaluation Metrics (Within ยฑ1 Level)**:
- CEFR Classifier Accuracy: 0.500
- Precision (Macro): 0.375
- Recall (Macro): 0.500
- F1-Score (Macro): 0.400
- **Other Metrics**:
- Perplexity: 55.377
- Diversity (Unique Sentences): 0.100
- Inference Time (ms): 5461.263
- Model Size (GB): 4.2
- Robustness (F1): 0.045
- **Confusion Matrix (Exact Matches)**:
- CSV: [confusion_matrix_exact.csv](confusion_matrix_exact.csv)
- Image: [confusion_matrix_exact.png](confusion_matrix_exact.png)
- **Confusion Matrix (Within ยฑ1 Level)**:
- CSV: [confusion_matrix_within1.csv](confusion_matrix_within1.csv)
- Image: [confusion_matrix_within1.png](confusion_matrix_within1.png)
- **Per-Class Confusion Metrics (Exact Matches)**:
- A1: TP=0, FP=0, FN=10, TN=50
- A2: TP=0, FP=0, FN=10, TN=50
- B1: TP=10, FP=50, FN=0, TN=0
- B2: TP=0, FP=0, FN=10, TN=50
- C1: TP=0, FP=0, FN=10, TN=50
- C2: TP=0, FP=0, FN=10, TN=50
- **Per-Class Confusion Metrics (Within ยฑ1 Level)**:
- A1: TP=0, FP=0, FN=10, TN=50
- A2: TP=10, FP=0, FN=0, TN=50
- B1: TP=10, FP=30, FN=0, TN=20
- B2: TP=10, FP=0, FN=0, TN=50
- C1: TP=0, FP=0, FN=10, TN=50
- C2: TP=0, FP=0, FN=10, TN=50
- **Note on Bias**:
- The test dataset used for evaluation (`test_merged_output.txt`) was part of the training and evaluation data for the classifier (`Mr-FineTuner/Skripsi_validator_best_model`). This may lead to inflated performance metrics due to the classifier's familiarity with the dataset. For a more robust evaluation, a new dataset not used in classifier training is recommended.
- **Usage**:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-7b-bnb-4bit")
tokenizer = AutoTokenizer.from_pretrained("unsloth/gemma-7b-bnb-4bit")
# Example inference
prompt = "<|user|>Generate a CEFR B1 level sentence.<|end|>"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Uploaded using `huggingface_hub`.
|
deshanksuman/mbart_50_SinhalaTransliteration | deshanksuman | 2025-05-04T16:36:23Z | 12 | 0 | null | [
"safetensors",
"mbart",
"transliteration",
"sinhala",
"sequence-to-sequence",
"si",
"en",
"dataset:deshanksuman/SwaBhasha_Transliteration_Sinhala",
"license:mit",
"region:us"
] | null | 2025-04-18T16:38:15Z | ---
language:
- si
- en
tags:
- transliteration
- sinhala
- mbart
- sequence-to-sequence
license: mit
datasets:
- deshanksuman/SwaBhasha_Transliteration_Sinhala
metrics:
- accuracy
---
# mBART-50 Sinhala Transliteration Model
This model transliterates Romanized Sinhala text to Sinhala script.
## Model description
This is a fine-tuned version of facebook/mbart-large-50-many-to-many-mmt specialized for Sinhala transliteration.
It converts romanized Sinhala (using Latin characters) to proper Sinhala script.
## Intended uses & limitations
This model is intended for transliterating Romanized Sinhala text to proper Sinhala script.
It can be useful for:
- Text input conversion in applications
- Helping non-native speakers type in Sinhala
- Converting legacy text in romanized format to proper Sinhala
### How to use
```python
from transformers import MBartForConditionalGeneration, MBartTokenizerFast
# Load model and tokenizer
model_name = "deshanksuman/mbart_50_SinhalaTransliteration"
tokenizer = MBartTokenizerFast.from_pretrained(model_name)
model = MBartForConditionalGeneration.from_pretrained(model_name)
# Set language codes
tokenizer.src_lang = "en_XX" # Using English as source language token
tokenizer.tgt_lang = "si_LK" # Sinhala as target
# Prepare input
text = "heta api mkda krnne"
inputs = tokenizer(text, return_tensors="pt", max_length=128, padding="max_length", truncation=True)
# Generate output
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_length=96,
num_beams=5,
early_stopping=True
)
# Decode output
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## Training data
The model was trained on the [deshanksuman/SwaBhasha_Transliteration_Sinhala](https://huggingface.co/deshanksuman/SwaBhasha_Transliteration_Sinhala) dataset, which contains pairs of Romanized Sinhala and corresponding Sinhala script text.
## Training procedure
The model was trained with the following parameters:
- Learning rate: 5e-05
- Batch size: 16
- Number of epochs: 2
- Max sequence length: 128
- Optimizer: AdamW
This is trained for sentence level.
### Examples:
**Example 1:**
- Romanized: Dakunu koreyawe eithihasika
- Expected: เถฏเถเทเถซเท เถเทเถปเทเถบเทเทเท เถเถญเทเทเทเทเทเถ
- Predicted: เถฏเถเทเถซเท เถเทเถปเทเถบเทเทเท เถเถญเทเทเทเทเทเถ
- Correct: True
**Example 2:**
- Romanized: Okoma hodai ganu gathiya
- Expected: เถเถเทเถเทเถธ เทเทเถฏเถบเท เถเทเถฑเท เถเถญเทเถบ
- Predicted: เถเถเถธ เทเทเถฏเถบเท เถเถฑเท เถเถญเทเถบ
- Correct: False
**Example 3:**
- Romanized: Malki akkith ennwa nedenntm godak kemathiyakkila dennm supiriyatam dance
- Expected: เถธเถฝเทเถเท เถ
เถเทเถเทเถญเท เถเถฑเท เถฑเทเถฏเทเถฑเทเถฑเถงเถธ เถเทเถฉเถเท เถเทเถธเถญเทเถบเทเถ
เถเทเถเทเถฝ เถฏเทเถฑเทเถฑเถธ เทเทเถดเทเถปเทเถบเถงเถธ เถฉเทเถฑเทเทเท
- Predicted: เถธเถฝเทเถเท เถ
เถเทเถเทเถญเท เถเถฑเทเถฑเท เถฑเทเถฏเทเถฏเทเถฑเทเถญเทเถธ เถเทเถฉเถเท เถเทเถธเถญเทเถบเทเถ
เถเทเถฝ เถฏเทเถฑเทเถฉเถธเท เทเทเถดเทเถปเทเถบเถงเถธ เถฉเทเถฑเทเทเท
- Correct: False
|
hosmankarabulut/Crispy-3B-CLM | hosmankarabulut | 2025-05-04T16:32:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"crispy",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-05-04T15:51:50Z | ---
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 ๐ค 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] |
mennaashraf/component-detector | mennaashraf | 2025-05-04T16:30:54Z | 0 | 0 | keras | [
"keras",
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T16:21:04Z | ---
license: apache-2.0
---
|
oneblackmage/Gradiant-ClientSim-v0.1 | oneblackmage | 2025-05-04T16:28:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"granite",
"text-generation",
"client-simulation",
"dialogue",
"bitsandbytes",
"4-bit",
"unsloth",
"conversational",
"en",
"dataset:merged_mental_health_dataset.jsonl",
"base_model:ibm-granite/granite-3.2-2b-instruct",
"base_model:quantized:ibm-granite/granite-3.2-2b-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T11:53:26Z | ---
language:
- en
license: apache-2.0
tags:
- granite
- client-simulation
- dialogue
- bitsandbytes
- 4-bit
- unsloth
- transformers
base_model: ibm-granite/granite-3.2-2b-instruct
pipeline_tag: text-generation
datasets:
- merged_mental_health_dataset.jsonl
library_name: transformers
---
# Gradiant-ClientSim-v0.1
A 4-bit quantized client simulation model based on IBM Granite 3.2B, fine-tuned for client interaction and simulation tasks. This model is compatible with Huggingface Transformers and bitsandbytes for efficient inference.
## Model Details
- **Base Model:** IBM Granite 3.2B (Unsloth)
- **Precision:** 4-bit (safetensors, bitsandbytes)
- **Architecture:** Causal Language Model
- **Tokenizer:** Included (BPE)
- **Intended Use:** Client simulation, dialogue, and assistant tasks
## Files Included
- `model.safetensors` โ Main model weights (4-bit)
- `config.json` โ Model configuration
- `generation_config.json` โ Generation parameters
- `tokenizer.json`, `tokenizer_config.json`, `vocab.json`, `merges.txt`, `special_tokens_map.json`, `added_tokens.json` โ Tokenizer files
## Example Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
model_id = "oneblackmage/Gradiant-ClientSim-v0.1"
bnb_config = BitsAndBytesConfig(load_in_4bit=True)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
prompt = "<|user>How can I improve my focus at work?\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Quantization
- This model is stored in 4-bit precision using [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) for efficient inference on modern GPUs.
- For best performance, use with `transformers` >= 4.45 and `bitsandbytes` >= 0.43.
## License
- See the LICENSE file or Huggingface model card for details.
## Citation
If you use this model, please cite the original IBM Granite model and this fine-tuned version.
---
For questions or issues, open an issue on the Huggingface repo or contact the maintainer. |
TareksLab/Persona-V2-70B | TareksLab | 2025-05-04T16:27:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Sao10K/70B-L3.3-mhnnn-x1",
"base_model:merge:Sao10K/70B-L3.3-mhnnn-x1",
"base_model:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:merge:SentientAGI/Dobby-Unhinged-Llama-3.3-70B",
"base_model:flammenai/Llama3.1-Flammades-70B",
"base_model:merge:flammenai/Llama3.1-Flammades-70B",
"base_model:flammenai/Mahou-1.5-llama3.1-70B",
"base_model:merge:flammenai/Mahou-1.5-llama3.1-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:35:48Z | ---
base_model:
- flammenai/Llama3.1-Flammades-70B
- Sao10K/70B-L3.3-mhnnn-x1
- flammenai/Mahou-1.5-llama3.1-70B
- SentientAGI/Dobby-Unhinged-Llama-3.3-70B
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 [Sao10K/70B-L3.3-mhnnn-x1](https://huggingface.co/Sao10K/70B-L3.3-mhnnn-x1) as a base.
### Models Merged
The following models were included in the merge:
* [flammenai/Llama3.1-Flammades-70B](https://huggingface.co/flammenai/Llama3.1-Flammades-70B)
* [flammenai/Mahou-1.5-llama3.1-70B](https://huggingface.co/flammenai/Mahou-1.5-llama3.1-70B)
* [SentientAGI/Dobby-Unhinged-Llama-3.3-70B](https://huggingface.co/SentientAGI/Dobby-Unhinged-Llama-3.3-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: flammenai/Mahou-1.5-llama3.1-70B
- model: flammenai/Llama3.1-Flammades-70B
- model: SentientAGI/Dobby-Unhinged-Llama-3.3-70B
base_model: Sao10K/70B-L3.3-mhnnn-x1
merge_method: model_stock
parameters:
int8_mask: true
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: base
pad_to_multiple_of: 8
```
|
jdchang/full-with-label-bs-1024-sg-2-step-12170 | jdchang | 2025-05-04T16:25:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:25:02Z | ---
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 ๐ค 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
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RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf | RichardErkhov | 2025-05-04T16:22:29Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-04T13:20:36Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
OpenMath2-Llama3.1-8B_icl1224 - GGUF
- Model creator: https://huggingface.co/joyheyueya/
- Original model: https://huggingface.co/joyheyueya/OpenMath2-Llama3.1-8B_icl1224/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [OpenMath2-Llama3.1-8B_icl1224.Q2_K.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q2_K.gguf) | Q2_K | 2.96GB |
| [OpenMath2-Llama3.1-8B_icl1224.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [OpenMath2-Llama3.1-8B_icl1224.IQ3_S.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [OpenMath2-Llama3.1-8B_icl1224.IQ3_M.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q3_K.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q3_K.gguf) | Q3_K | 3.74GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [OpenMath2-Llama3.1-8B_icl1224.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q4_0.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q4_0.gguf) | Q4_0 | 4.34GB |
| [OpenMath2-Llama3.1-8B_icl1224.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q4_K.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q4_K.gguf) | Q4_K | 4.58GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q4_1.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q4_1.gguf) | Q4_1 | 4.78GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q5_0.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q5_0.gguf) | Q5_0 | 5.21GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q5_K.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q5_K.gguf) | Q5_K | 5.34GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q5_1.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q5_1.gguf) | Q5_1 | 5.65GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q6_K.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q6_K.gguf) | Q6_K | 6.14GB |
| [OpenMath2-Llama3.1-8B_icl1224.Q8_0.gguf](https://huggingface.co/RichardErkhov/joyheyueya_-_OpenMath2-Llama3.1-8B_icl1224-gguf/blob/main/OpenMath2-Llama3.1-8B_icl1224.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
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|
jdchang/full-with-label-bs-1024-sg-2-step-12150 | jdchang | 2025-05-04T16:22:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:22:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Hardware Type:** [More Information Needed]
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here4code/Qwen-3-32BModel-FineTuned-Medical-Reasoning-medical-o1-reasoning-SFT | here4code | 2025-05-04T16:21:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:20:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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mlfoundations-dev/no_pipeline_math_100k | mlfoundations-dev | 2025-05-04T16:20:13Z | 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 | 2025-05-03T22:05:52Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: no_pipeline_math_100k
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. -->
# no_pipeline_math_100k
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/no_pipeline_math_100k 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- total_eval_batch_size: 256
- 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 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.3
|
longdnk113/CNN_MNIST | longdnk113 | 2025-05-04T16:17:21Z | 0 | 0 | keras | [
"keras",
"pattern-recognition",
"mnist",
"image-classification",
"en",
"dataset:ylecun/mnist",
"license:mit",
"region:us"
] | image-classification | 2025-05-04T15:53:07Z | ---
license: mit
datasets:
- ylecun/mnist
language:
- en
metrics:
- f1
- precision
- recall
- accuracy
tags:
- pattern-recognition
- mnist
- image-classification
---
# MNIST Pattern Recognition with Convolutional Neural Network (CNN)
This project implements a Convolutional Neural Network (CNN) for recognizing handwritten digits from the MNIST dataset. The model is built using TensorFlow and Keras, and it supports both single-GPU and multi-GPU training. The project includes training, testing, and a user-friendly GUI for inference.
## Features
- **Customizable CNN Architecture**: Includes convolutional, pooling, normalization, and dense layers.
- **Multi-GPU Support**: Leverages TensorFlow's `MirroredStrategy` for distributed training.
- **Training Visualization**: Generates plots for training/validation accuracy and loss.
- **Evaluation Metrics**: Outputs confusion matrix, classification report, and precision/recall/F1 scores.
- **Interactive GUI**: Built with Streamlit for real-time image recognition.
- **Docker Support**: Easily deployable using Docker.
## Model Architecture
 <br>
The CNN model consists of:
1. Two convolutional layers with ReLU activation and max-pooling.
2. Layer normalization for improved convergence.
3. Fully connected dense layers with dropout for regularization.
4. Softmax output layer for classification into 10 digit classes.
## Training
The model is trained on the MNIST dataset, which contains 60,000 training images and 10,000 test images of handwritten digits (28x28 grayscale). The training process includes:
- Data normalization to scale pixel values to the range [0, 1].
- Categorical cross-entropy loss and accuracy as the evaluation metric.
- Model checkpointing to save the best-performing model based on validation accuracy.
## Final result
**Training history**
 <br>
**Confusion matrix**
 <br>
**Classification report**
 <br>
**Test result**
 <br>
Full code at [Github](https://github.com/longdnk/Pattern-Recognition/tree/main/MNIST) |
young0ha/llama-3.2-1b-ko-morpheme | young0ha | 2025-05-04T16:11:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:09:14Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** young0ha
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
mluger/vitFaceExpressionCrossEntropyLoss | mluger | 2025-05-04T16:11:11Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-21T13:54:58Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vitFaceExpressionCrossEntropyLoss
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. -->
# vitFaceExpressionCrossEntropyLoss
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8619
- Accuracy: 0.7033
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- 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: cosine
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2523 | 1.0 | 898 | 1.0366 | 0.6158 |
| 0.9007 | 2.0 | 1796 | 0.9029 | 0.6723 |
| 0.7628 | 3.0 | 2694 | 0.8649 | 0.6877 |
| 0.6649 | 4.0 | 3592 | 0.8663 | 0.6946 |
| 0.5811 | 5.0 | 4490 | 0.8625 | 0.6974 |
| 0.4833 | 6.0 | 5388 | 0.8590 | 0.7027 |
| 0.4175 | 7.0 | 6286 | 0.8605 | 0.7016 |
| 0.3912 | 8.0 | 7184 | 0.8619 | 0.7033 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
dgiang02/GRPO_Qwen25_15B_32_005_2000kmap | dgiang02 | 2025-05-04T16:10:16Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:09:41Z | ---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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 ๐ค 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] |
carowagner/classify-questions-2A | carowagner | 2025-05-04T16:09:55Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:09:03Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.3329247236251831
f1_macro: 0.8700832445654317
f1_micro: 0.9
f1_weighted: 0.9012285477571311
precision_macro: 0.906878306878307
precision_micro: 0.9
precision_weighted: 0.9055238095238096
recall_macro: 0.8472222222222222
recall_micro: 0.9
recall_weighted: 0.9
accuracy: 0.9
|
phospho-app/Gr00t_simple_pawn_move_v3_500-94ahougeru | phospho-app | 2025-05-04T16:09:51Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-04T15:57:58Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [dopaul/simple_pawn_move_v3](https://huggingface.co/datasets/dopaul/simple_pawn_move_v3)
- **Wandb run URL**: None
- **Epochs**: 5
- **Batch size**: 64
- **Training steps**: None
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
mradermacher/Phi-4-reasoning-plus-i1-GGUF | mradermacher | 2025-05-04T16:09:17Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"phi",
"nlp",
"math",
"code",
"chat",
"conversational",
"reasoning",
"en",
"base_model:microsoft/Phi-4-reasoning-plus",
"base_model:quantized:microsoft/Phi-4-reasoning-plus",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-05-04T08:24:18Z | ---
base_model: microsoft/Phi-4-reasoning-plus
language:
- en
library_name: transformers
license: mit
license_link: https://huggingface.co/microsoft/Phi-4-reasoning-plus/resolve/main/LICENSE
quantized_by: mradermacher
tags:
- phi
- nlp
- math
- code
- chat
- conversational
- reasoning
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/microsoft/Phi-4-reasoning-plus
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Phi-4-reasoning-plus-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/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ1_S.gguf) | i1-IQ1_S | 3.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ1_M.gguf) | i1-IQ1_M | 3.7 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ2_S.gguf) | i1-IQ2_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ2_M.gguf) | i1-IQ2_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.3 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q2_K.gguf) | i1-Q2_K | 5.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ3_S.gguf) | i1-IQ3_S | 6.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q4_0.gguf) | i1-Q4_0 | 8.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q4_1.gguf) | i1-Q4_1 | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-4-reasoning-plus-i1-GGUF/resolve/main/Phi-4-reasoning-plus.i1-Q6_K.gguf) | i1-Q6_K | 12.1 | 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 -->
|
MinaMila/llama_instbase_3b_LoRa_ACSEmployment_2_ep1_22 | MinaMila | 2025-05-04T16:07:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T16:07:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
6x16/whisper-small-nan-tw-quicktrain | 6x16 | 2025-05-04T16:07:38Z | 1 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"nan",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-03T14:43:25Z | ---
library_name: transformers
language:
- nan
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
model-index:
- name: "A Quick-trained Whisper-Small model for Nan-TW (\u95A9\u5357\u8A71/\u53F0\
\u8A9E) #JL "
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. -->
# A Quick-trained Whisper-Small model for Nan-TW (้ฉๅ่ฉฑ/ๅฐ่ช) #JL
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 17.0 (nan-tw) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7699
- Cer: 138.9186
## Transcription Example
(Example Source: https://sutian.moe.edu.tw/zh-hant/su/27169/) <br>
**Original sentence**: _่ฌไบ่ตท้ ญ้ฃใ_ <br>
**Inference by _Whisper-Small_**: _เธเธฑเธเธเธน เธเธตเนเนเธเนเธฒเธซเธฅเธฑเธ_<br>
**Inference by this model**: _่ฌไบ่ตท้ ญ้ฃ_
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: 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
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.3511 | 2.9240 | 1000 | 0.7512 | 125.6361 |
| 0.0117 | 5.8480 | 2000 | 0.7479 | 141.2850 |
| 0.001 | 8.7719 | 3000 | 0.7629 | 136.0814 |
| 0.0006 | 11.6959 | 4000 | 0.7699 | 138.9186 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
AntoineBourgois/propp-fr_coreference-resolution_camembert-large_PER | AntoineBourgois | 2025-05-04T16:07:33Z | 0 | 1 | null | [
"coreference-resolution",
"anaphora-resolution",
"mentions-linking",
"literary-texts",
"camembert",
"nested-entities",
"BookNLP-fr",
"fr",
"base_model:almanach/camembert-large",
"base_model:finetune:almanach/camembert-large",
"license:apache-2.0",
"region:us"
] | null | 2024-12-14T11:46:42Z | ---
language: fr
tags:
- coreference-resolution
- anaphora-resolution
- mentions-linking
- literary-texts
- camembert
- literary-texts
- nested-entities
- BookNLP-fr
license: apache-2.0
metrics:
- MUC
- B3
- CEAF
- CoNLL-F1
base_model:
- almanach/camembert-large
---
## INTRODUCTION:
This model, developed as part of the [BookNLP-fr project](https://github.com/lattice-8094/fr-litbank), is a **coreference resolution model** built on top of [camembert-large](https://huggingface.co/almanach/camembert-large) embeddings. It is trained to link mentions of the same entity across a text, focusing on literary works in French.
This specific model has been trained to link entities of the following types: PER.
## MODEL PERFORMANCES (LOOCV):
Overall Coreference Resolution Performances for non-overlapping windows of different length:
| | Window width (tokens) | Document count | Sample count | MUC F1 | B3 F1 | CEAFe F1 | CONLL F1 |
|----|-------------------------|------------------|----------------|----------|---------|------------|------------|
| 0 | 500 | 29 | 677 | 92.18% | 83.86% | 76.86% | 84.30% |
| 1 | 1,000 | 29 | 332 | 92.65% | 79.79% | 71.77% | 81.40% |
| 2 | 2,000 | 28 | 162 | 93.29% | 75.85% | 67.34% | 78.83% |
| 3 | 5,000 | 19 | 56 | 93.76% | 69.60% | 61.16% | 74.84% |
| 4 | 10,000 | 18 | 27 | 94.28% | 65.73% | 58.59% | 72.86% |
| 5 | 25,000 | 2 | 3 | 94.76% | 62.48% | 53.33% | 70.19% |
| 6 | 50,000 | 1 | 1 | 97.39% | 56.43% | 47.40% | 67.07% |
Coreference Resolution Performances on the fully annotated sample for each document:
| | Token count | Mention count | MUC F1 | B3 F1 | CEAFe F1 | CONLL F1 |
|----|---------------|-----------------|----------|---------|------------|------------|
| 0 | 1,864 | 253 | 98.16% | 95.39% | 60.34% | 84.63% |
| 1 | 2,034 | 321 | 97.47% | 92.79% | 80.04% | 90.10% |
| 2 | 2,141 | 297 | 95.06% | 77.99% | 65.08% | 79.38% |
| 3 | 2,251 | 235 | 91.95% | 80.47% | 46.56% | 73.00% |
| 4 | 2,343 | 239 | 83.87% | 61.95% | 43.58% | 63.13% |
| 5 | 2,441 | 314 | 91.85% | 55.70% | 60.82% | 69.46% |
| 6 | 2,554 | 330 | 90.24% | 65.27% | 72.36% | 75.96% |
| 7 | 2,860 | 369 | 93.65% | 84.89% | 74.93% | 84.49% |
| 8 | 2,929 | 386 | 95.65% | 78.21% | 64.23% | 79.37% |
| 9 | 4,067 | 429 | 97.46% | 85.20% | 62.52% | 81.73% |
| 10 | 5,425 | 558 | 90.46% | 53.03% | 59.52% | 67.67% |
| 11 | 10,305 | 1,436 | 96.37% | 74.83% | 59.91% | 77.04% |
| 12 | 10,982 | 1,095 | 97.18% | 65.30% | 60.49% | 74.32% |
| 13 | 11,768 | 1,734 | 93.30% | 64.14% | 64.12% | 73.85% |
| 14 | 11,834 | 600 | 92.21% | 67.51% | 60.74% | 73.49% |
| 15 | 11,902 | 1,692 | 95.03% | 58.83% | 45.59% | 66.49% |
| 16 | 12,281 | 1,089 | 95.06% | 62.05% | 72.55% | 76.55% |
| 17 | 12,285 | 1,489 | 95.28% | 77.84% | 57.43% | 76.85% |
| 18 | 12,315 | 1,501 | 95.36% | 57.07% | 64.26% | 72.23% |
| 19 | 12,389 | 1,654 | 93.19% | 54.21% | 51.84% | 66.41% |
| 20 | 12,557 | 1,085 | 92.30% | 66.97% | 46.65% | 68.64% |
| 21 | 12,703 | 1,731 | 90.40% | 53.70% | 61.37% | 68.49% |
| 22 | 13,023 | 1,559 | 93.86% | 61.71% | 62.41% | 72.66% |
| 23 | 14,299 | 1,582 | 97.23% | 69.25% | 67.04% | 77.84% |
| 24 | 14,637 | 2,127 | 95.78% | 71.34% | 63.28% | 76.80% |
| 25 | 15,408 | 1,769 | 92.85% | 54.11% | 56.12% | 67.69% |
| 26 | 24,776 | 2,716 | 94.31% | 63.51% | 54.12% | 70.65% |
| 27 | 30,987 | 2,980 | 89.55% | 54.25% | 59.68% | 67.83% |
| 28 | 71,219 | 11,857 | 97.38% | 50.85% | 45.93% | 64.72% |
## TRAINING PARAMETERS:
- Entities types: PER
- Split strategy: Leave-one-out cross-validation (29 files)
- Train/Validation split: 0.85 / 0.15
- Batch size: 16,000
- Initial learning rate: 0.0004
- Focal loss gamma: 1
- Focal loss alpha: 0.25
- Pronoun lookup antecedents: 30
- Common and Proper nouns lookup antecedents: 300
## MODEL ARCHITECTURE:
Model Input: 2,165 dimensions vector
- Concatenated maximum context camembert-large embeddings (2 * 1,024 = 2,048 dimensions)
- Additional mentions features (106 dimensions):
- Length of mentions
- Position of the mention's start token within the sentence
- Grammatical category of the mentions (pronoun, common noun, proper noun)
- Dependency relation of the mention's head (one-hot encoded)
- Gender of the mentions (one-hot encoded)
- Number (singular/plural) of the mentions (one-hot encoded)
- Grammatical person of the mentions (one-hot encoded)
- Additional mention pairs features (11 dimensions):
- Distance between mention IDs
- Distance between start tokens of mentions
- Distance between end tokens of mentions
- Distance between sentences containing mentions
- Distance between paragraphs containing mentions
- Difference in nesting levels of mentions
- Ratio of shared tokens between mentions
- Exact text match between mentions (binary)
- Exact match of mention heads (binary)
- Match of syntactic heads between mentions (binary)
- Match of entity types between mentions (binary)
- Hidden Layers:
- Number of layers: 3
- Units per layer: 1,900 nodes
- Activation function: relu
- Dropout rate: 0.6
- Final Layer:
- Type: Linear
- Input: 1900 dimensions
- Output: 1 dimension (mention pair coreference score)
Model Output: Continuous prediction between 0 (not coreferent) and 1 (coreferent) indicating the degree of confidence.
## HOW TO USE:
*** IN CONSTRUCTION ***
## TRAINING CORPUS:
| | Document | Tokens Count | Is included in model eval |
|----|----------------------------------------------------------------|----------------|------------------------------------|
| 0 | 1836_Gautier-Theophile_La-morte-amoureuse | 14,299 tokens | **True** |
| 1 | 1840_Sand-George_Pauline | 12,315 tokens | **True** |
| 2 | 1842_Balzac-Honore-de_La-Maison-du-chat-qui-pelote | 24,776 tokens | **True** |
| 3 | 1844_Balzac-Honore-de_La-Maison-Nucingen | 30,987 tokens | **True** |
| 4 | 1844_Balzac-Honore-de_Sarrasine | 15,408 tokens | **True** |
| 5 | 1856_Cousin-Victor_Madame-de-Hautefort | 11,768 tokens | **True** |
| 6 | 1863_Gautier-Theophile_Le-capitaine-Fracasse | 11,834 tokens | **True** |
| 7 | 1873_Zola-Emile_Le-ventre-de-Paris | 12,557 tokens | **True** |
| 8 | 1881_Flaubert-Gustave_Bouvard-et-Pecuchet | 12,281 tokens | **True** |
| 9 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-1_1-MADEMOISELLE-FIFI | 5,425 tokens | **True** |
| 10 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-1_2-MADAME-BAPTISTE | 2,554 tokens | **True** |
| 11 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-1_3-LA-ROUILLE | 2,929 tokens | **True** |
| 12 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-2_1-MARROCA | 4,067 tokens | **True** |
| 13 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-2_2-LA-BUCHE | 2,251 tokens | **True** |
| 14 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-2_3-LA-RELIQUE | 2,034 tokens | **True** |
| 15 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_1-FOU | 1,864 tokens | **True** |
| 16 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_2-REVEIL | 2,141 tokens | **True** |
| 17 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_3-UNE-RUSE | 2,441 tokens | **True** |
| 18 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_4-A-CHEVAL | 2,860 tokens | **True** |
| 19 | 1882_Guy-de-Maupassant_Mademoiselle-Fifi-3_5-UN-REVEILLON | 2,343 tokens | **True** |
| 20 | 1901_Lucie-Achard_Rosalie-de-Constant-sa-famille-et-ses-amis | 12,703 tokens | **True** |
| 21 | 1903_Conan-Laure_Elisabeth_Seton | 13,023 tokens | **True** |
| 22 | 1904_Rolland-Romain_Jean-Christophe_Tome-I-L-aube | 10,982 tokens | **True** |
| 23 | 1904_Rolland-Romain_Jean-Christophe_Tome-II-Le-matin | 10,305 tokens | **True** |
| 24 | 1917_Adรจle-Bourgeois_Nรฉmoville | 12,389 tokens | **True** |
| 25 | 1923_Radiguet-Raymond_Le-diable-au-corps | 14,637 tokens | **True** |
| 26 | 1926_Audoux-Marguerite_De-la-ville-au-moulin | 11,902 tokens | **True** |
| 27 | 1937_Audoux-Marguerite_Douce-Lumiere | 12,285 tokens | **True** |
| 28 | Manon_Lescaut_PEDRO | 71,219 tokens | **True** |
| 29 | TOTAL | 346,579 tokens | 29 files used for cross-validation |
## CONTACT:
mail: antoine [dot] bourgois [at] protonmail [dot] com
|
dgiang02/GRPO_Qwen25_15B_16_005_2000kmap | dgiang02 | 2025-05-04T16:07:13Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T16:06:36Z | ---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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 ๐ค 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]
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[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
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Model Card Contact
[More Information Needed] |
mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF | mradermacher | 2025-05-04T16:06:10Z | 14 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TareksGraveyard/Progenitor-V1.2-LLaMa-70B",
"base_model:quantized:TareksGraveyard/Progenitor-V1.2-LLaMa-70B",
"license:llama3.3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-01-29T15:10:25Z | ---
base_model: TareksGraveyard/Progenitor-V1.2-LLaMa-70B
language:
- en
library_name: transformers
license: llama3.3
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/TareksGraveyard/Progenitor-V1.2-LLaMa-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-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/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Progenitor-V1.2-LLaMa-70B-GGUF/resolve/main/Progenitor-V1.2-LLaMa-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
carowagner/classify-questions-1B | carowagner | 2025-05-04T16:05:19Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T16:04:23Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.16687506437301636
f1_macro: 0.9044444444444445
f1_micro: 0.96
f1_weighted: 0.9579619047619048
precision_macro: 0.9844961240310077
precision_micro: 0.96
precision_weighted: 0.9618604651162791
recall_macro: 0.8452380952380952
recall_micro: 0.96
recall_weighted: 0.96
accuracy: 0.96
|
TOMFORD79/Fly62 | TOMFORD79 | 2025-05-04T16:00:07Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-04T13:15:43Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
ma921/phi-2-sft-golden-hh | ma921 | 2025-05-04T15:58:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:54:35Z | ---
library_name: transformers
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: phi-2-sft-golden-hh
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. -->
# phi-2-sft-golden-hh
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_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: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
phospho-app/Gr00t_simple_pawn_move_v3_500-rlgkmw3sk5 | phospho-app | 2025-05-04T15:54:36Z | 0 | 0 | null | [
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-04T15:46:38Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/root/src/helper.py", line 224, in predict
raise RuntimeError(error_msg)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/modeling_flash_attention_utils.py", line 296, in _flash_attention_forward
attn_output = flash_attn_func(
^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/flash_attn/flash_attn_interface.py", line 1107, in flash_attn_func
def flash_attn_func(
KeyboardInterrupt
78%|โโโโโโโโ | 463/595 [06:01<01:42, 1.28it/s]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/root/src/helper.py", line 226, in predict
raise RuntimeError(e)
RuntimeError: Training process failed with exit code 1:
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/transformers/modeling_flash_attention_utils.py", line 296, in _flash_attention_forward
attn_output = flash_attn_func(
^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/flash_attn/flash_attn_interface.py", line 1107, in flash_attn_func
def flash_attn_func(
KeyboardInterrupt
78%|โโโโโโโโ | 463/595 [06:01<01:42, 1.28it/s]
```
## Training parameters:
- **Dataset**: [dopaul/simple_pawn_move_v3](https://huggingface.co/datasets/dopaul/simple_pawn_move_v3)
- **Wandb run URL**: None
- **Epochs**: 5
- **Batch size**: 64
- **Training steps**: 594
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
shubhamprshr/Qwen2.5-3B-Instruct_blocksworld6_sgrpo_balanced_0.5_0.5_True_1200 | shubhamprshr | 2025-05-04T15:53:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"dataset:blocksworld-dataset",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T01:12:30Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
datasets: blocksworld-dataset
library_name: transformers
model_name: Qwen2.5-3B-Instruct_blocksworld6_sgrpo_balanced_0.5_0.5_True_1200
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-3B-Instruct_blocksworld6_sgrpo_balanced_0.5_0.5_True_1200
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-dataset) 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="shubhamprshr/Qwen2.5-3B-Instruct_blocksworld6_sgrpo_balanced_0.5_0.5_True_1200", 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/shubhamprshr27-tamu/BW2/runs/hkzwevwu)
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: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
thuanan/Llama-3.2-1B-Instruct-Chat-sft | thuanan | 2025-05-04T15:53:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:48:52Z | ---
base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** thuanan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
infogeo/c7ed7350-031e-45e1-a8bf-2b5ecfa5a39e | infogeo | 2025-05-04T15:47:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060",
"base_model:adapter:The-matt/llama2_ko-7b_distinctive-snowflake-182_1060",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T15:43:26Z | ---
library_name: peft
base_model: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c7ed7350-031e-45e1-a8bf-2b5ecfa5a39e
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: The-matt/llama2_ko-7b_distinctive-snowflake-182_1060
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 5b937aaaaa1b4833_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/5b937aaaaa1b4833_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
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: 0.55
group_by_length: false
hub_model_id: infogeo/c7ed7350-031e-45e1-a8bf-2b5ecfa5a39e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
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: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/5b937aaaaa1b4833_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: 89c6d633-5ae5-4ed8-aed8-e6cc264c27ff
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 89c6d633-5ae5-4ed8-aed8-e6cc264c27ff
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# c7ed7350-031e-45e1-a8bf-2b5ecfa5a39e
This model is a fine-tuned version of [The-matt/llama2_ko-7b_distinctive-snowflake-182_1060](https://huggingface.co/The-matt/llama2_ko-7b_distinctive-snowflake-182_1060) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5276
## 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 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5014 | 0.1403 | 150 | 1.5276 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
RazzzHF/razzzModels | RazzzHF | 2025-05-04T15:46:42Z | 0 | 4 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2023-08-12T02:28:15Z | ---
license: cc-by-nc-sa-4.0
---
|
ivangrapher/52299cbd-52c3-4da8-ba0a-02751db70178 | ivangrapher | 2025-05-04T15:46:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/llama-2-7b",
"base_model:adapter:unsloth/llama-2-7b",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T15:27:17Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/llama-2-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 52299cbd-52c3-4da8-ba0a-02751db70178
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: unsloth/llama-2-7b
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 92a4ab705f6ca41d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/92a4ab705f6ca41d_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: 0.55
group_by_length: false
hub_model_id: ivangrapher/52299cbd-52c3-4da8-ba0a-02751db70178
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
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: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/92a4ab705f6ca41d_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: 2048
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: 233f72d1-e3b6-4877-90aa-2582c4f49bbb
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 233f72d1-e3b6-4877-90aa-2582c4f49bbb
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 52299cbd-52c3-4da8-ba0a-02751db70178
This model is a fine-tuned version of [unsloth/llama-2-7b](https://huggingface.co/unsloth/llama-2-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7977
## 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 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: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.817 | 0.1403 | 150 | 0.7977 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
apriasmoro/45d38f63-6247-4d5b-8a83-b96a586e89ea | apriasmoro | 2025-05-04T15:44:26Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:41:30Z | ---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
library_name: transformers
model_name: 45d38f63-6247-4d5b-8a83-b96a586e89ea
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 45d38f63-6247-4d5b-8a83-b96a586e89ea
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B).
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="apriasmoro/45d38f63-6247-4d5b-8a83-b96a586e89ea", 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/apriasmoro-abcstudio/llama3_dpo/runs/ssujh1xu)
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Nickybcybc/Qwen3-lora_model | Nickybcybc | 2025-05-04T15:44:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T15:43:18Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Nickybcybc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 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)
|
zayanhugsAI/twitter_roberta_finetuned | zayanhugsAI | 2025-05-04T15:43:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T22:09:36Z | ---
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 ๐ค 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] |
MrRobotoAI/A14 | MrRobotoAI | 2025-05-04T15:42:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"base_model:Blackroot/Llama-3-LongStory-LORA",
"base_model:merge:Blackroot/Llama-3-LongStory-LORA",
"base_model:MrRobotoAI/A5",
"base_model:merge:MrRobotoAI/A5",
"base_model:MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K",
"base_model:merge:MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K",
"base_model:MrRobotoAI/Nord-8b-Uncensored-BASE-128k",
"base_model:merge:MrRobotoAI/Nord-8b-Uncensored-BASE-128k",
"base_model:MrRobotoAI/Odin-v2-8b-NOVELIST-128K",
"base_model:merge:MrRobotoAI/Odin-v2-8b-NOVELIST-128K",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T01:22:17Z | ---
base_model:
- MrRobotoAI/Nord-8b-Uncensored-BASE-128k
- Blackroot/Llama-3-LongStory-LORA
- MrRobotoAI/Odin-v2-8b-NOVELIST-128K
- MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K
- MrRobotoAI/Odin-v2-8b-NOVELIST-128K
- MrRobotoAI/A5
library_name: transformers
tags:
- mergekit
- merge
---
# merge 13,801 R
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 [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [MrRobotoAI/Odin-v2-8b-NOVELIST-128K](https://huggingface.co/MrRobotoAI/Odin-v2-8b-NOVELIST-128K) as a base.
### Models Merged
The following models were included in the merge:
* [MrRobotoAI/Nord-8b-Uncensored-BASE-128k](https://huggingface.co/MrRobotoAI/Nord-8b-Uncensored-BASE-128k) + [Blackroot/Llama-3-LongStory-LORA](https://huggingface.co/Blackroot/Llama-3-LongStory-LORA)
* [MrRobotoAI/Odin-v2-8b-NOVELIST-128K](https://huggingface.co/MrRobotoAI/Odin-v2-8b-NOVELIST-128K) + [MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K](https://huggingface.co/MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K)
* [MrRobotoAI/A5](https://huggingface.co/MrRobotoAI/A5)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: task_arithmetic
models:
- model: MrRobotoAI/A5
parameters:
weight:
- filter: v_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: o_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: up_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: gate_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- filter: down_proj
value: [0.8, 0.8, 0.5, 0.6, 0.7, 0.8, 0.7, 0.6, 0.5, 0.8, 0.8]
- value: 2
- model: MrRobotoAI/Nord-8b-Uncensored-BASE-128k+Blackroot/Llama-3-LongStory-LORA
parameters:
weight:
- filter: v_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: o_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: up_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: gate_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: down_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- value: 1
- model: MrRobotoAI/Odin-v2-8b-NOVELIST-128K+MrRobotoAI/Llama-3.1-8B-Instruct-Adapter-512K
parameters:
weight:
- filter: v_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: o_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: up_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: gate_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- filter: down_proj
value: [0.1, 0.1, 0.25, 0.2, 0.15, 0.1, 0.15, 0.2, 0.25, 0.1, 0.1]
- value: 0
base_model: MrRobotoAI/Odin-v2-8b-NOVELIST-128K
dtype: bfloat16
```
|
fffanx/Llama-3.2-1B-Instruct-GRPO-agent16_E1 | fffanx | 2025-05-04T15:42:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T01:10:32Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent16_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent16_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) 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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent16_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Alphatao/dc6c07f3-e309-4d05-aac1-fc85d2156cf3 | Alphatao | 2025-05-04T15:42:25Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:EleutherAI/pythia-1b",
"base_model:finetune:EleutherAI/pythia-1b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:21:56Z | ---
base_model: EleutherAI/pythia-1b
library_name: transformers
model_name: dc6c07f3-e309-4d05-aac1-fc85d2156cf3
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for dc6c07f3-e309-4d05-aac1-fc85d2156cf3
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b).
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="Alphatao/dc6c07f3-e309-4d05-aac1-fc85d2156cf3", 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/alphatao-alphatao/Gradients-On-Demand/runs/mcdqonvy)
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.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent15_E1 | fffanx | 2025-05-04T15:42:01Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T01:09:43Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent15_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent15_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) 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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent15_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AdoCleanCode/Youtube8M_real_model_v4_0.8 | AdoCleanCode | 2025-05-04T15:40:25Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T00:24:50Z | ---
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: Youtube8M_real_model_v4_0.8
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. -->
# Youtube8M_real_model_v4_0.8
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5185
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.5816 | 1.0 | 20534 | 0.5626 |
| 0.5621 | 2.0 | 41068 | 0.5390 |
| 0.5404 | 3.0 | 61602 | 0.5275 |
| 0.5242 | 4.0 | 82136 | 0.5215 |
| 0.51 | 5.0 | 102670 | 0.5185 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.1.2+cu121
- Datasets 2.19.1
- Tokenizers 0.20.3
|
ustc-community/dfine_s_coco | ustc-community | 2025-05-04T15:39:50Z | 532 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-02-11T14:30:13Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_s_coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_s_coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 โ 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
venkatramaraju/polyglot | venkatramaraju | 2025-05-04T15:39:11Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T15:34:31Z | # ๐ Polyglot
Polyglot is a high-performance multilingual tokenizer, built entirely from scratch in Go, that efficiently compresses text from 10 diverse languages using the Byte-Pair Encoding (BPE) algorithm. The system supports English, Hebrew, Bengali, Vietnamese, Korean, Arabic, Russian, Thai, Chinese, and Japanese.
## ๐ Metrics
- **Compression Ratio:** 3.0
- **Vocabulary Size:** 40,146
- **Total Training Corpus:** 432,584,912 characters (10M sentences)
## ๐ Benchmarking
Polyglot is evaluated against five SOTA tokenizers: Tiktoken, Transformers, SentencePiece, mBERT, and XLM. A total of 100,000 unseen sentencesโ10,000 per language across 10 languagesโwere sampled from the [statmt/cc10](https://huggingface.co/datasets/statmt/cc100?p=1) dataset. For each tokenizer and language, the mean compression ratio and token fertility were computed over the corresponding 10,000 sentences.
### ๐ Compression Ratio
| Language | polyglot | mbert | sentencepiece | tiktoken | transformers | xlm |
|----------|----------|--------|---------------|----------|--------------|------|
| ar | 2.61 | 2.43 | 2.76 | 3.03 | 1.00 | 3.03 |
| bn | 2.80 | 2.07 | 2.84 | 2.56 | 0.52 | 2.83 |
| en | 3.75 | 3.77 | 3.90 | 4.43 | 4.21 | 3.77 |
| he | 2.32 | 2.29 | 2.51 | 2.54 | 0.88 | 2.80 |
| ja | 1.51 | 1.25 | 11.80 | 1.35 | 0.72 | 1.78 |
| ko | 1.51 | 1.48 | 1.93 | 1.62 | 0.50 | 1.78 |
| ru | 3.36 | 3.17 | 1.37 | 3.72 | 0.94 | 3.85 |
| th | 2.49 | 1.45 | 6.49 | 2.30 | 0.55 | 3.22 |
| vi | 2.86 | 3.13 | 1.26 | 3.20 | 1.14 | 3.42 |
| zh | 1.36 | 1.04 | 5.40 | 1.32 | 0.50 | 1.51 |
#### Compression Ratio Rankings
| Rank | Tokenizer | Average Compression Ratio |
|:----:|:--------------:|:---------------------------:|
| 1 | sentencepiece | **4.03** |
| 2 | xlm | **2.80** |
| 3 | tiktoken | **2.61** |
| 4 | polyglot | **2.46** |
| 5 | mbert | **2.21** |
| 6 | transformers | **1.10** |
### ๐งฉ Token Fertility
| Language | polyglot | mbert | sentencepiece | tiktoken | transformers | xlm |
|----------|----------|--------|--------------|----------|--------------|------|
| ar | 1.96 | 2.10 | 1.85 | 1.69 | 5.10 | 1.68 |
| bn | 1.84 | 2.50 | 1.82 | 2.02 | 10.01 | 1.82 |
| en | 1.19 | 1.19 | 1.15 | 1.01 | 1.06 | 1.19 |
| he | 2.08 | 2.10 | 1.92 | 1.90 | 5.46 | 1.72 |
| ja | 1.12 | 1.35 | 0.14 | 1.25 | 2.35 | 0.95 |
| ko | 1.91 | 1.95 | 1.50 | 1.79 | 5.73 | 1.63 |
| ru | 1.62 | 1.72 | 3.97 | 1.46 | 5.82 | 1.42 |
| th | 1.76 | 3.02 | 0.67 | 1.90 | 7.96 | 1.36 |
| vi | 1.65 | 1.50 | 3.74 | 1.47 | 4.13 | 1.37 |
| zh | 1.21 | 1.58 | 0.30 | 1.25 | 3.31 | 1.09 |
#### Token Fertility Rankings
| Rank | Tokenizer | Average Token Fertility |
|:----:|:--------------:|:--------------------------:|
| 1 | transformers | **5.09** |
| 2 | mbert | **1.90** |
| 3 | sentencepiece | **1.71** |
| 4 | polyglot | **1.63** |
| 5 | tiktoken | **1.57** |
| 6 | xlm | **1.42** |
### ๐ ๐ Cross-Lingual Consistency
A primary goal of Polyglot is to achieve uniform tokenization quality across diverse languages. The following table compares how consistently each tokenizer performs across all 10 evaluated languages.
| Tokenizer | Compression Ratio ฯ | Token Fertility ฯ | Total ฯ |
|---------------|------------------------|----------------------|-----------------------------|
| xlm | 0.80 | 0.27 | 1.07 |
| polyglot | 0.76 | 0.33 | 1.09 |
| tiktoken | 0.97 | 0.32 | 1.29 |
| mbert | 0.88 | 0.53 | 1.41 |
| transformers | 1.06 | 2.48 | 3.54 |
## ๐๏ธ Training
- **Dataset:** The tokenizer was trained on 10M sentences from the [opus-100 dataset](https://huggingface.co/datasets/Helsinki-NLP/opus-100), with 1M sentences per language. The language set was carefully selected to incorporate a sufficiently diverse range of scripts in our training dataset.
- **Training Process:** The current version has a compression ratio of 3.0. Training runs are in progress to push this to 5.0.
- **Implementation:** Data aggregation and formatting were implemented in Python. The core BPE algorithm and server were written in Go. Training data was chunked and streamed from S3 for efficient processing on machines of various sizes.
## ๐ Deployment
Deploy Polyglot locally using Docker with the following commands:
```bash
# Build the Docker image
docker build -t polyglot-app .
# Run the container
docker run -p 8080:8080 -p 3000:3000 polyglot-app
```
Navigate to [localhost:3000](http://localhost:3000/) to interface with the tool.
## ๐ Website
Visit [Polyglot's website](https://polyglot-k6h6.onrender.com/). Please note that the host instance automatically spins down during periods of inactivity which may result in delays due to cold starts. It may take upto a minute to startup.
Computation speed may vary between the hosted version and local deployment, depending on your local hardware specifications and the resources allocated by Render's infrastructure.
**Website**
**Local**
## ๐ฅ๏ธ Frontend
The `ui` directory contains an intuitive user interface that provides the following capabilities:
- Text input for tokenization
- Visualization of tokenized segments and their corresponding integer representations
- Decoding functionality to reconstruct the original text
- Real-time metrics displaying compression ratio, token-to-character counts for performance analysis, and computation times.
## โ๏ธ Backend
The backend exposes two RESTful endpoints:
- **`/encode`:** Processes input text and returns the corresponding token sequence with text representations
- **`/decode`:** Accepts a token sequence and reconstructs the original text
## ๐ License
This project is licensed under the MIT License.
|
ustc-community/dfine_m_obj2coco | ustc-community | 2025-05-04T15:38:14Z | 62 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T11:39:09Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_m_obj2coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_m_obj2coco")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 โ 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
ustc-community/dfine_l_obj365 | ustc-community | 2025-05-04T15:37:30Z | 96 | 0 | transformers | [
"transformers",
"safetensors",
"d_fine",
"object-detection",
"vision",
"en",
"dataset:coco",
"dataset:objects365",
"arxiv:2410.13842",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2025-03-28T13:00:47Z | ---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: object-detection
tags:
- object-detection
- vision
datasets:
- coco
- objects365
---
## D-FINE
### **Overview**
The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf)
This is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
### **Performance**
D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
### **How to use**
```python
import torch
import requests
from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor
url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_l_obj365")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_l_obj365")
inputs = image_processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
for result in results:
for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
score, label = score.item(), label_id.item()
box = [round(i, 2) for i in box.tolist()]
print(f"{model.config.id2label[label]}: {score:.2f} {box}")
```
### **Training**
D-FINE is trained on COCO and Objects365 (Lin et al. [2014]) train2017 and validated on COCO + Objects365 val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 โ 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios.
### **Applications**
D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent8_E1 | fffanx | 2025-05-04T15:36:55Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T00:40:32Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent8_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent8_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) 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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent8_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
fffanx/Llama-3.2-1B-Instruct-GRPO-agent7_E1 | fffanx | 2025-05-04T15:36:12Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:grouped_dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T00:40:02Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets: grouped_dataset
library_name: transformers
model_name: Llama-3.2-1B-Instruct-GRPO-agent7_E1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-1B-Instruct-GRPO-agent7_E1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on the [grouped_dataset](https://huggingface.co/datasets/grouped_dataset) 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="fffanx/Llama-3.2-1B-Instruct-GRPO-agent7_E1", 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: 0.17.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
apriasmoro/da55b775-f3e0-4a9d-aadd-72666dabfccd | apriasmoro | 2025-05-04T15:35:17Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T15:31:14Z | ---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
library_name: transformers
model_name: da55b775-f3e0-4a9d-aadd-72666dabfccd
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for da55b775-f3e0-4a9d-aadd-72666dabfccd
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B).
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="apriasmoro/da55b775-f3e0-4a9d-aadd-72666dabfccd", 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/apriasmoro-abcstudio/llama3_dpo/runs/glb116cq)
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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Jeremmmyyyyy/gemma-3-1b-Math | Jeremmmyyyyy | 2025-05-04T15:34:57Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:09:51Z | ---
base_model: google/gemma-3-1b-it
library_name: transformers
model_name: gemma-3-1b-it
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for gemma-3-1b-it
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it).
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="Jeremmmyyyyy/gemma-3-1b-it", 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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
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