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jaindeepali010/clinical_ner_miimansa_G1_model | jaindeepali010 | "2024-01-28T09:17:42Z" | 1 | 0 | transformers | [
"transformers",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-01-28T08:05:30Z" | This model is a clinical NER model finetuned using bert-base-uncased model, trained on G1 dataset. Training and validation was done using 80% of the total data (random state=42), while 20% used for testing.
The model was trained for 20 epoch with an early stopping patience of 3 epochs. |
albertus-sussex/veriscrape-simcse-auto-reference_2_to_verify_8-fold-7 | albertus-sussex | "2025-03-26T11:44:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2025-03-26T11:43:33Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
camidenecken/RM2-RoBERTa-rm-v3-SBERT-v4_12 | camidenecken | "2024-11-19T17:19:49Z" | 163 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-11-19T17:19:16Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
SatCat/rl_course_vizdoom_health_gathering_supreme | SatCat | "2023-02-24T07:31:08Z" | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-02-24T07:31:02Z" | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.42 +/- 6.12
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r SatCat/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
NexesMess/Llama_3.x_70b_Triads_V2 | NexesMess | "2025-03-12T20:30:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:NexesMess/Llama_3.3_70b_DoppelGanger_R1",
"base_model:merge:NexesMess/Llama_3.3_70b_DoppelGanger_R1",
"base_model:NexesMess/Llama_3.x_70b_Tessessence_0.10c",
"base_model:merge:NexesMess/Llama_3.x_70b_Tessessence_0.10c",
"base_model:Steelskull/L3.3-Electra-R1-70b",
"base_model:merge:Steelskull/L3.3-Electra-R1-70b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-12T19:59:02Z" | ---
base_model:
- NexesMess/Llama_3.3_70b_DoppelGanger_R1
- NexesMess/Llama_3.x_70b_Tessessence_0.10c
- Steelskull/L3.3-Electra-R1-70b
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 [NexesMess/Llama_3.x_70b_Tessessence_0.10c](https://huggingface.co/NexesMess/Llama_3.x_70b_Tessessence_0.10c) as a base.
### Models Merged
The following models were included in the merge:
* [NexesMess/Llama_3.3_70b_DoppelGanger_R1](https://huggingface.co/NexesMess/Llama_3.3_70b_DoppelGanger_R1)
* [Steelskull/L3.3-Electra-R1-70b](https://huggingface.co/Steelskull/L3.3-Electra-R1-70b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: model_stock
models:
- model: NexesMess/Llama_3.3_70b_DoppelGanger_R1
parameters:
weight: 1.0
- model: Steelskull/L3.3-Electra-R1-70b
parameters:
weight: 1.0
base_model: NexesMess/Llama_3.x_70b_Tessessence_0.10c
dtype: bfloat16
out_dtype: bfloat16
parameters:
int8_mask: true
normalize: true
rescale: false
chat_template: auto
tokenizer:
source: union
```
|
tiendoan/finetune-clip-vit-large-patch14 | tiendoan | "2024-11-05T04:30:58Z" | 192 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:openai/clip-vit-large-patch14",
"base_model:finetune:openai/clip-vit-large-patch14",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-11-05T04:04:17Z" | ---
library_name: transformers
base_model: openai/clip-vit-large-patch14
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: finetune-clip-vit-large-patch14
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. -->
# finetune-clip-vit-large-patch14
This model is a fine-tuned version of [openai/clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6545
- F1: 0.6242
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.7084 | 0.3690 | 100 | 0.7173 | 0.5405 |
| 0.6818 | 0.7380 | 200 | 0.7269 | 0.5683 |
| 0.7169 | 1.1070 | 300 | 0.6949 | 0.5683 |
| 0.6957 | 1.4760 | 400 | 0.6799 | 0.5650 |
| 0.6218 | 1.8450 | 500 | 0.7344 | 0.5766 |
| 0.6406 | 2.2140 | 600 | 0.6600 | 0.6118 |
| 0.6645 | 2.5830 | 700 | 0.6581 | 0.6113 |
| 0.6546 | 2.9520 | 800 | 0.6549 | 0.6192 |
| 0.6068 | 3.3210 | 900 | 0.6542 | 0.6224 |
| 0.6351 | 3.6900 | 1000 | 0.6545 | 0.6242 |
### Framework versions
- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0
|
3mei/phi_3_5_instruct_mini_4bit_reflection_405_v2_8k_gsm8k_3e_qkvogud_mlab_instr_resp | 3mei | "2024-09-24T02:21:44Z" | 60 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Phi-3.5-mini-instruct-bnb-4bit",
"base_model:quantized:unsloth/Phi-3.5-mini-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-09-24T02:20:04Z" | ---
base_model: unsloth/Phi-3.5-mini-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** 3mei
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3.5-mini-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)
|
vaibkumar/agentic_training_finetuned_v7-Q8_0-GGUF | vaibkumar | "2025-03-14T15:51:32Z" | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:vaibkumar/agentic_training_finetuned_v7",
"base_model:quantized:vaibkumar/agentic_training_finetuned_v7",
"endpoints_compatible",
"region:us"
] | null | "2025-03-14T15:50:31Z" | ---
base_model: vaibkumar/agentic_training_finetuned_v7
tags:
- llama-cpp
- gguf-my-repo
---
# vaibkumar/agentic_training_finetuned_v7-Q8_0-GGUF
This model was converted to GGUF format from [`vaibkumar/agentic_training_finetuned_v7`](https://huggingface.co/vaibkumar/agentic_training_finetuned_v7) 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/vaibkumar/agentic_training_finetuned_v7) 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 vaibkumar/agentic_training_finetuned_v7-Q8_0-GGUF --hf-file agentic_training_finetuned_v7-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo vaibkumar/agentic_training_finetuned_v7-Q8_0-GGUF --hf-file agentic_training_finetuned_v7-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo vaibkumar/agentic_training_finetuned_v7-Q8_0-GGUF --hf-file agentic_training_finetuned_v7-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo vaibkumar/agentic_training_finetuned_v7-Q8_0-GGUF --hf-file agentic_training_finetuned_v7-q8_0.gguf -c 2048
```
|
Nambata/dummy_vae_mapi | Nambata | "2025-01-08T02:08:08Z" | 25 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionControlNetPipeline",
"region:us"
] | text-to-image | "2025-01-08T02:05:23Z" | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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StepLaw/StepLaw-N_1.0B-D_1.0B-LR4.883e-04-BS32768 | StepLaw | "2025-04-02T01:03:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"step1",
"text-generation",
"StepLaw",
"causal-lm",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-02T00:59:24Z" | ---
license: apache-2.0
tags:
- StepLaw
- causal-lm
language:
- en
library_name: transformers
pipeline_tag: text-generation
model-index:
- name: step2v2_0618_h2048_ffnh8192_numh16_numl16_lr4.883e-04_bs16_ti61035_mlr1e-5
results: []
---
# Wandb Model Name: step2v2_0618_h2048_ffnh8192_numh16_numl16_lr4.883e-04_bs16_ti61035_mlr1e-5
This model is part of the [StepLaw-N_1.0B-D_1.0B](https://huggingface.co/collections/StepLaw/StepLaw-N_1.0B-D_1.0B) collection.
## Model Specifications
### Architecture
- **Hidden size (H)**: 2048
- **Feed-forward network size (FFN)**: 8192
- **Attention heads**: 16
- **Layers**: 16
- **Parameter count**: 1.1BM
### Training Parameters
- **Learning rate (lr)**: 4.883e-04
- **Batch size (bs)**: 16
- **Training iterations**: 61035
- **Training tokens (D)**: 2.0B
## Model Description
StepLaw models are trained with various hyperparameter settings to enable research on scaling laws and hyperparameter optimization. This specific model was trained with learning rate 4.883e-04 and batch size 16 for 61035 iterations, using a total of 2.0B training tokens.
## Usage Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "StepLaw/StepLaw-N_1.0B-D_1.0B-LR4.883e-04-BS32768"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
# Generate text
inputs = tokenizer("A long time ago in a galaxy far, far away", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```## Part of StepLaw Project
StepLaw is an initiative to provide thousands of models for optimal hyperparameter research.
Visit [StepLaw Project](https://step-law.github.io/) for more information.
|
automerger/Experiment26Shadow-7B | automerger | "2024-03-27T00:13:18Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:CorticalStack/shadow-clown-7B-slerp",
"base_model:finetune:CorticalStack/shadow-clown-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-08T18:56:36Z" | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- rwitz/experiment26-truthy-iter-0
- CorticalStack/shadow-clown-7B-slerp
---
# Experiment26Shadow-7B
Experiment26Shadow-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [rwitz/experiment26-truthy-iter-0](https://huggingface.co/rwitz/experiment26-truthy-iter-0)
* [CorticalStack/shadow-clown-7B-slerp](https://huggingface.co/CorticalStack/shadow-clown-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: rwitz/experiment26-truthy-iter-0
layer_range: [0, 32]
- model: CorticalStack/shadow-clown-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: rwitz/experiment26-truthy-iter-0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment26Shadow-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
scaledown/ScaleDown-7B-slerp-v0.1 | scaledown | "2024-03-26T01:20:49Z" | 1,538 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-01-01T08:26:00Z" | ---
license: apache-2.0
tags:
- merge
- mergekit
model-index:
- name: ScaleDown-7B-slerp-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 68.0
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=scaledown/ScaleDown-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.7
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=scaledown/ScaleDown-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.26
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=scaledown/ScaleDown-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 61.9
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=scaledown/ScaleDown-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=scaledown/ScaleDown-7B-slerp-v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.17
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=scaledown/ScaleDown-7B-slerp-v0.1
name: Open LLM Leaderboard
---
# ScaleDown-7B-slerp-v0.1
This model is a merge of the following models made with [mergekit](https://github.com/cg123/mergekit):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [jondurbin/bagel-dpo-7b-v0.1](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.1)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: jondurbin/bagel-dpo-7b-v0.1
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_scaledown__ScaleDown-7B-slerp-v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |71.57|
|AI2 Reasoning Challenge (25-Shot)|68.00|
|HellaSwag (10-Shot) |85.70|
|MMLU (5-Shot) |65.26|
|TruthfulQA (0-shot) |61.90|
|Winogrande (5-shot) |81.37|
|GSM8k (5-shot) |67.17|
|
BenjaminOcampo/task-implicit_task__model-usesvm__aug_method-ri | BenjaminOcampo | "2023-11-03T15:46:03Z" | 0 | 0 | null | [
"en",
"arxiv:1910.09700",
"region:us"
] | null | "2023-11-03T15:45:54Z" | ---
language: en
---
# Model Card for BenjaminOcampo/task-implicit_task__model-usesvm__aug_method-ri
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination-optional)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation-optional)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
**Classification results dev set**
```
precision recall f1-score support
0 0.88 0.86 0.87 2680
1 0.76 0.80 0.78 1501
2 0.35 0.31 0.33 186
accuracy 0.81 4367
macro avg 0.66 0.65 0.66 4367
weighted avg 0.81 0.81 0.81 4367
```
**Classification results test set**
```
precision recall f1-score support
0 0.89 0.87 0.88 2681
1 0.77 0.80 0.79 1501
2 0.37 0.37 0.37 186
accuracy 0.82 4368
macro avg 0.68 0.68 0.68 4368
weighted avg 0.82 0.82 0.82 4368
```
- **Developed by:** Nicolás Benjamín Ocampo
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** en
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
- **Resources for more information:** [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.
# Training Details
## Training Data
<!-- This should link to a Data 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 [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- 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 Data 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]
# 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]
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details> |
cleanrl/KungFuMaster-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2 | cleanrl | "2023-02-23T16:25:37Z" | 0 | 0 | cleanrl | [
"cleanrl",
"tensorboard",
"KungFuMaster-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-02-23T16:25:36Z" | ---
tags:
- KungFuMaster-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: KungFuMaster-v5
type: KungFuMaster-v5
metrics:
- type: mean_reward
value: 33200.00 +/- 9362.48
name: mean_reward
verified: false
---
# (CleanRL) **PPO** Agent Playing **KungFuMaster-v5**
This is a trained model of a PPO agent playing KungFuMaster-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[jax,envpool,atari]"
python -m cleanrl_utils.enjoy --exp-name cleanba_ppo_envpool_impala_atari_wrapper_naturecnn --env-id KungFuMaster-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/pyproject.toml
curl -OL https://huggingface.co/cleanrl/KungFuMaster-v5-cleanba_ppo_envpool_impala_atari_wrapper_naturecnn-seed2/raw/main/poetry.lock
poetry install --all-extras
python cleanba_ppo_envpool_impala_atari_wrapper_naturecnn.py --distributed --learner-device-ids 1 --track --wandb-project-name cleanba --save-model --upload-model --hf-entity cleanrl --env-id KungFuMaster-v5 --seed 2
```
# Hyperparameters
```python
{'actor_device_ids': [0],
'actor_devices': ['gpu:0'],
'anneal_lr': True,
'async_batch_size': 20,
'async_update': 3,
'batch_size': 15360,
'capture_video': False,
'clip_coef': 0.1,
'cuda': True,
'distributed': True,
'ent_coef': 0.01,
'env_id': 'KungFuMaster-v5',
'exp_name': 'cleanba_ppo_envpool_impala_atari_wrapper_naturecnn',
'gae_lambda': 0.95,
'gamma': 0.99,
'global_learner_decices': ['gpu:1', 'gpu:3'],
'hf_entity': 'cleanrl',
'learner_device_ids': [1],
'learner_devices': ['gpu:1'],
'learning_rate': 0.00025,
'local_batch_size': 7680,
'local_minibatch_size': 1920,
'local_num_envs': 60,
'local_rank': 0,
'max_grad_norm': 0.5,
'minibatch_size': 3840,
'norm_adv': True,
'num_envs': 120,
'num_minibatches': 4,
'num_steps': 128,
'num_updates': 3255,
'profile': False,
'save_model': True,
'seed': 2,
'target_kl': None,
'test_actor_learner_throughput': False,
'torch_deterministic': True,
'total_timesteps': 50000000,
'track': True,
'update_epochs': 4,
'upload_model': True,
'vf_coef': 0.5,
'wandb_entity': None,
'wandb_project_name': 'cleanba',
'world_size': 2}
```
|
brugmark/all-MiniLM-L6-v2-personal-project-finetuned-2024-06-03 | brugmark | "2024-06-03T14:08:24Z" | 125 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:sentence-transformers/all-MiniLM-L6-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L6-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-06-03T07:24:22Z" | ---
license: apache-2.0
base_model: sentence-transformers/all-MiniLM-L6-v2
tags:
- generated_from_trainer
model-index:
- name: all-MiniLM-L6-v2-personal-project-finetuned-2024-06-03
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. -->
# all-MiniLM-L6-v2-personal-project-finetuned-2024-06-03
This model is a fine-tuned version of [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 7.6355
## 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: 0.5
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
mradermacher/Qwen2.5-Coder-1.5B-GGUF | mradermacher | "2024-11-12T14:43:23Z" | 126 | 0 | transformers | [
"transformers",
"gguf",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"en",
"base_model:Qwen/Qwen2.5-Coder-1.5B",
"base_model:quantized:Qwen/Qwen2.5-Coder-1.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-11-12T05:39:04Z" | ---
base_model: Qwen/Qwen2.5-Coder-1.5B
language:
- en
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B/blob/main/LICENSE
quantized_by: mradermacher
tags:
- code
- qwen
- qwen-coder
- codeqwen
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-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/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q4_0_4_4.gguf) | Q4_0_4_4 | 1.0 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-1.5B-GGUF/resolve/main/Qwen2.5-Coder-1.5B.f16.gguf) | f16 | 3.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
KingLTD/pretrain_Law_model_vit5_version1 | KingLTD | "2023-09-04T04:14:25Z" | 105 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-base",
"base_model:finetune:VietAI/vit5-base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2023-09-04T03:28:51Z" | ---
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: pretrain_Law_model_vit5_version1
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. -->
# pretrain_Law_model_vit5_version1
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2779
- Rouge1: 0.4859
- Rouge2: 0.3617
- Rougel: 0.4218
- Rougelsum: 0.4417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 245 | 0.3566 | 0.4739 | 0.3369 | 0.4053 | 0.4273 |
| No log | 2.0 | 490 | 0.3240 | 0.4752 | 0.3453 | 0.4095 | 0.4300 |
| 0.7518 | 3.0 | 735 | 0.3059 | 0.4760 | 0.3510 | 0.4112 | 0.4311 |
| 0.7518 | 4.0 | 980 | 0.2951 | 0.4838 | 0.3584 | 0.4164 | 0.4387 |
| 0.2808 | 5.0 | 1225 | 0.2858 | 0.4799 | 0.3582 | 0.4166 | 0.4368 |
| 0.2808 | 6.0 | 1470 | 0.2831 | 0.4839 | 0.3611 | 0.4194 | 0.4403 |
| 0.2351 | 7.0 | 1715 | 0.2814 | 0.4858 | 0.3644 | 0.4218 | 0.4423 |
| 0.2351 | 8.0 | 1960 | 0.2779 | 0.4850 | 0.3612 | 0.4206 | 0.4416 |
| 0.2074 | 9.0 | 2205 | 0.2775 | 0.4836 | 0.3590 | 0.4199 | 0.4398 |
| 0.2074 | 10.0 | 2450 | 0.2779 | 0.4859 | 0.3617 | 0.4218 | 0.4417 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
|
igorsterner/AnE-NER | igorsterner | "2024-10-05T12:53:54Z" | 109 | 2 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"multilingual",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-12-18T09:05:15Z" | ---
license: mit
language:
- multilingual
base_model:
- FacebookAI/xlm-roberta-large
pipeline_tag: token-classification
---
# Multilingual Identification of English Code-Switching
AnE-NER (Any-English Code-Switching Named Entity Recognition) is a token-level model for detecting named entities in code-switching texts. It classifies words into two classes: `I` (inside a named entity) and `O` (outside a named entity). The model shows strong performance on both languages seen and unseen in the training data.
# Usage
You can use AnE-NER with Huggingface’s `pipeline` or `AutoModelForTokenClassification`.
Let's try the following example (taken from [this](https://aclanthology.org/W18-3213/) paper)
```python
input = "My Facebook, Ig & Twitter is hellaa dead yall Jk soy yo que has no life!"
```
## Pipeline
```python
from transformers import pipeline
classifier = pipeline("token-classification", model="igorsterner/AnE-NER", aggregation_strategy="simple")
result = classifier(input)
```
which returns
```
[{'entity_group': 'I',
'score': 0.95482016,
'word': 'Facebook',
'start': 3,
'end': 11},
{'entity_group': 'I',
'score': 0.9638739,
'word': 'Ig',
'start': 13,
'end': 15},
{'entity_group': 'I',
'score': 0.98207414,
'word': 'Twitter',
'start': 18,
'end': 25}]
```
## Advanced
If your input is already word-tokenized, and you want the corresponding word NER labels, you can try the following strategy
```python
import torch
from transformers import AutoModelForTokenClassification, AutoTokenizer
lid_model_name = "igorsterner/AnE-NER"
lid_tokenizer = AutoTokenizer.from_pretrained(lid_model_name)
lid_model = AutoModelForTokenClassification.from_pretrained(lid_model_name)
word_tokens = ['My', 'Facebook', ',', 'Ig', '&', 'Twitter', 'is', 'hellaa', 'dead', 'yall', 'Jk', 'soy', 'yo', 'que', 'has', 'no', 'life', '!']
subword_inputs = lid_tokenizer(
word_tokens, truncation=True, is_split_into_words=True, return_tensors="pt"
)
subword2word = subword_inputs.word_ids(batch_index=0)
logits = lid_model(**subword_inputs).logits
predictions = torch.argmax(logits, dim=2)
predicted_subword_labels = [lid_model.config.id2label[t.item()] for t in predictions[0]]
predicted_word_labels = [[] for _ in range(len(word_tokens))]
for idx, predicted_subword in enumerate(predicted_subword_labels):
if subword2word[idx] is not None:
predicted_word_labels[subword2word[idx]].append(predicted_subword)
def most_frequent(lst):
return max(set(lst), key=lst.count) if lst else "Other"
predicted_word_labels = [most_frequent(sublist) for sublist in predicted_word_labels]
for token, label in zip(word_tokens, predicted_word_labels):
print(f"{token}: {label}")
```
which returns
```
My: O
Facebook: I
,: O
Ig: I
&: O
Twitter: I
is: O
hellaa: O
dead: O
yall: O
Jk: O
soy: O
yo: O
que: O
has: O
no: O
life!: O
```
# Word-level language labels
If you also want the language of each word, you can additionaly run [AnE-LID](https://huggingface.co/igorsterner/ane-lid). Checkout my evaluation scripts for examples of using both at the same time, as we did in the paper: [https://github.com/igorsterner/AnE/tree/main/eval](https://github.com/igorsterner/AnE/tree/main/eval).
For the above example, you can get:
```
My: English
Facebook: NE.English
,: Other
Ig: NE.English
&: Other
Twitter: NE.English
is: English
hellaa: English
dead: English
yall: English
Jk: English
soy: notEnglish
yo: notEnglish
que: notEnglish
has: English
no: English
life: English
!: Other
```
# Citation
Please consider citing my work if it helped you
```
@inproceedings{sterner-2024-multilingual,
title = "Multilingual Identification of {E}nglish Code-Switching",
author = "Sterner, Igor",
editor = {Scherrer, Yves and
Jauhiainen, Tommi and
Ljube{\v{s}}i{\'c}, Nikola and
Zampieri, Marcos and
Nakov, Preslav and
Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Eleventh Workshop on NLP for Similar Languages, Varieties, and Dialects (VarDial 2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.vardial-1.14",
doi = "10.18653/v1/2024.vardial-1.14",
pages = "163--173",
abstract = "Code-switching research depends on fine-grained language identification. In this work, we study existing corpora used to train token-level language identification systems. We aggregate these corpora with a consistent labelling scheme and train a system to identify English code-switching in multilingual text. We show that the system identifies code-switching in unseen language pairs with absolute measure 2.3-4.6{\%} better than language-pair-specific SoTA. We also analyse the correlation between typological similarity of the languages and difficulty in recognizing code-switching.",
}
``` |
mlx-community/Falcon3-10B-Instruct-abliterated-4bit | mlx-community | "2025-02-19T20:43:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"falcon3",
"abliterated",
"uncensored",
"mlx",
"mlx-my-repo",
"conversational",
"en",
"fr",
"es",
"pt",
"base_model:huihui-ai/Falcon3-10B-Instruct-abliterated",
"base_model:quantized:huihui-ai/Falcon3-10B-Instruct-abliterated",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] | text-generation | "2025-02-19T10:43:20Z" | ---
language:
- en
- fr
- es
- pt
tags:
- falcon3
- abliterated
- uncensored
- mlx
- mlx-my-repo
base_model: huihui-ai/Falcon3-10B-Instruct-abliterated
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
library_name: transformers
---
# mlx-community/Falcon3-10B-Instruct-abliterated-4bit
The Model [mlx-community/Falcon3-10B-Instruct-abliterated-4bit](https://huggingface.co/mlx-community/Falcon3-10B-Instruct-abliterated-4bit) was converted to MLX format from [huihui-ai/Falcon3-10B-Instruct-abliterated](https://huggingface.co/huihui-ai/Falcon3-10B-Instruct-abliterated) using mlx-lm version **0.20.5**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Falcon3-10B-Instruct-abliterated-4bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
albertus-sussex/veriscrape-simcse-nbaplayer-reference_8_to_verify_2-fold-1 | albertus-sussex | "2025-03-28T11:29:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2025-03-28T11:29:36Z" | ---
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 🤗 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] |
damgomz/ft_2_16e6_base_x12 | damgomz | "2024-06-21T02:08:13Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-06-19T16:30:52Z" | ---
language: en
tags:
- text-classification
pipeline_tag: text-classification
widget:
- text: GEPS Techno is the pioneer of hybridization of renewable energies at sea.
We imagine, design and commercialize innovative off-grid systems that aim to generate
power at sea, stabilize and collect data. The success of our low power platforms
WAVEPEAL enabled us to scale-up the device up to WAVEGEM, the 150-kW capacity
platform.
---
## Environmental Impact (CODE CARBON DEFAULT)
| Metric | Value |
|--------------------------|---------------------------------|
| Duration (in seconds) | 125822.48677945136 |
| Emissions (Co2eq in kg) | 0.07613707246273 |
| CPU power (W) | 42.5 |
| GPU power (W) | [No GPU] |
| RAM power (W) | 3.75 |
| CPU energy (kWh) | 1.4854004569676238 |
| GPU energy (kWh) | [No GPU] |
| RAM energy (kWh) | 0.1310635769749674 |
| Consumed energy (kWh) | 1.6164640339425915 |
| Country name | Switzerland |
| Cloud provider | nan |
| Cloud region | nan |
| CPU count | 2 |
| CPU model | Intel(R) Xeon(R) Platinum 8360Y CPU @ 2.40GHz |
| GPU count | nan |
| GPU model | nan |
## Environmental Impact (for one core)
| Metric | Value |
|--------------------------|---------------------------------|
| CPU energy (kWh) | 0.24220828705044384 |
| Emissions (Co2eq in kg) | 0.04928047398861844 |
## Note
19 juin 2024
## My Config
| Config | Value |
|--------------------------|-----------------|
| checkpoint | albert-base-v2 |
| model_name | ft_2_16e6_base_x12 |
| sequence_length | 400 |
| num_epoch | 6 |
| learning_rate | 1.6e-05 |
| batch_size | 2 |
| weight_decay | 0.0 |
| warm_up_prop | 0.0 |
| drop_out_prob | 0.1 |
| packing_length | 100 |
| train_test_split | 0.2 |
| num_steps | 29328 |
## Training and Testing steps
Epoch | Train Loss | Test Loss | F-beta Score
---|---|---|---
| 0 | 0.000000 | 0.719599 | 0.552431 |
| 1 | 0.339442 | 0.266141 | 0.915358 |
| 2 | 0.243426 | 0.252600 | 0.927469 |
| 3 | 0.204434 | 0.255368 | 0.896083 |
| 4 | 0.207367 | 0.284679 | 0.901333 |
| 5 | 0.180203 | 0.273225 | 0.917218 |
| 6 | 0.146234 | 0.255182 | 0.910723 |
|
BAAI/Bunny-Llama-3-8B-V-gguf | BAAI | "2024-06-11T07:40:56Z" | 355 | 16 | null | [
"gguf",
"arxiv:2402.11530",
"license:apache-2.0",
"region:us"
] | null | "2024-05-04T17:15:00Z" | ---
inference: false
license: apache-2.0
---
# Model Card
<p align="center">
<img src="./icon.png" alt="Logo" width="350">
</p>
📖 [Technical report](https://arxiv.org/abs/2402.11530) | 🏠 [Code](https://github.com/BAAI-DCAI/Bunny) | 🐰 [Demo](http://bunny.baai.ac.cn)
This is **GGUF** format of [Bunny-Llama-3-8B-V](https://huggingface.co/BAAI/Bunny-Llama-3-8B-V).
Bunny is a family of lightweight but powerful multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Llama-3-8B, Phi-1.5, StableLM-2, Qwen1.5, MiniCPM and Phi-2. To compensate for the decrease in model size, we construct more informative training data by curated selection from a broader data source.
We provide Bunny-Llama-3-8B-V, which is built upon [SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) and [Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct). More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny).
# Quickstart
## Chat by [`llama.cpp`](https://github.com/ggerganov/llama.cpp)
```shell
# sample images can be found in images folder
# fp16
./llava-cli -m ggml-model-f16.gguf --mmproj mmproj-model-f16.gguf --image example_2.png -c 4096 -e \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhy is the image funny? ASSISTANT:" \
--temp 0.0
# int4
./llava-cli -m ggml-model-Q4_K_M.gguf --mmproj mmproj-model-f16.gguf --image example_2.png -c 4096 -e \
-p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\nWhy is the image funny? ASSISTANT:" \
--temp 0.0
```
## Chat by [ollama](https://ollama.com/)
```shell
# sample images can be found in images folder
# fp16
ollama create Bunny-Llama-3-8B-V-fp16 -f ./ollama-f16
ollama run Bunny-Llama-3-8B-V-fp16 'example_2.png
Why is the image funny?'
# int4
ollama create Bunny-Llama-3-8B-V-int4 -f ./ollama-Q4_K_M
ollama run Bunny-Llama-3-8B-V-int4 'example_2.png
Why is the image funny?'
```
|
yuvimor24/vakyansh-wav2vec2-indian-english-enm-700 | yuvimor24 | "2024-07-09T07:41:12Z" | 126 | 0 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"indian english ",
"indian english asr",
"audio",
"speech",
"indian english speech recognition",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-07-09T07:33:37Z" | ---
license: mit
language:
- en
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- 'indian english '
- indian english asr
- automatic-speech-recognition
- audio
- speech
- indian english speech recognition
--- |
ser-mei/borges-gpt-collab-finetuned | ser-mei | "2022-12-13T12:19:00Z" | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-11-29T17:47:50Z" | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: borges-gpt-collab-finetuned
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. -->
# borges-gpt-collab-finetuned
This model is a fine-tuned version of [DeepESP/gpt2-spanish](https://huggingface.co/DeepESP/gpt2-spanish) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2150
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42069
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.6177 | 4.96 | 35 | 4.3309 |
| 3.9729 | 9.96 | 70 | 4.2350 |
| 3.2225 | 14.96 | 105 | 4.3344 |
| 2.3158 | 19.96 | 140 | 4.5764 |
| 1.3761 | 24.96 | 175 | 4.9125 |
| 0.6779 | 29.96 | 210 | 5.3096 |
| 0.3399 | 34.96 | 245 | 5.6735 |
| 0.2147 | 39.96 | 280 | 5.9322 |
| 0.1675 | 44.96 | 315 | 6.1347 |
| 0.1418 | 49.96 | 350 | 6.2150 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0+rocm5.2
- Datasets 2.6.1
- Tokenizers 0.13.2
|
luyaoli/TestModel | luyaoli | "2025-04-10T13:39:51Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-10T13:39:50Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
hugging-quants/Meta-Llama-3.1-8B-BNB-NF4-BF16 | hugging-quants | "2024-07-28T19:08:05Z" | 113 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3.1",
"meta",
"bnb",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-07-28T18:29:59Z" | ---
license: llama3.1
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- llama-3.1
- meta
- bnb
---
> [!IMPORTANT]
> This repository is a community-driven quantized version of the original model [`meta-llama/Meta-Llama-3.1-8B`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) which is the BF16 half-precision official version released by Meta AI.
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
This repository contains [`meta-llama/Meta-Llama-3.1-8B`](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) quantized using [bitsandbytes](https://github.com/bitsandbytes-foundation/bitsandbytes) from BF16 down to NF4 with a block size of 64, **and storage type `torch.bfloat16`**.
|
Kort/i_3 | Kort | "2024-10-18T13:41:56Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-18T13:39:21Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Shinyaaa/RPC_ft_first_N | Shinyaaa | "2024-09-24T02:39:36Z" | 88 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-09-24T02:20:08Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
lesso12/8d2c1314-2a82-4b36-b153-95c50da41b56 | lesso12 | "2025-02-21T18:08:50Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:sethuiyer/Medichat-Llama3-8B",
"base_model:adapter:sethuiyer/Medichat-Llama3-8B",
"license:other",
"region:us"
] | null | "2025-02-21T17:48:43Z" | ---
library_name: peft
license: other
base_model: sethuiyer/Medichat-Llama3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8d2c1314-2a82-4b36-b153-95c50da41b56
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
auto_find_batch_size: true
base_model: sethuiyer/Medichat-Llama3-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c47ca4d9ff1f07ae_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c47ca4d9ff1f07ae_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso12/8d2c1314-2a82-4b36-b153-95c50da41b56
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000212
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/c47ca4d9ff1f07ae_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
seed: 120
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7a04d4c8-a979-4f82-b1e8-61b6807fd749
wandb_project: 12a
wandb_run: your_name
wandb_runid: 7a04d4c8-a979-4f82-b1e8-61b6807fd749
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8d2c1314-2a82-4b36-b153-95c50da41b56
This model is a fine-tuned version of [sethuiyer/Medichat-Llama3-8B](https://huggingface.co/sethuiyer/Medichat-Llama3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2420
## 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: 0.000212
- train_batch_size: 4
- eval_batch_size: 4
- seed: 120
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 1.7658 |
| 1.5277 | 0.0390 | 50 | 1.5597 |
| 1.4436 | 0.0781 | 100 | 1.5017 |
| 1.5801 | 0.1171 | 150 | 1.4366 |
| 1.4678 | 0.1562 | 200 | 1.3925 |
| 1.3112 | 0.1952 | 250 | 1.3409 |
| 1.3983 | 0.2343 | 300 | 1.3041 |
| 1.2145 | 0.2733 | 350 | 1.2721 |
| 1.2378 | 0.3124 | 400 | 1.2539 |
| 1.1975 | 0.3514 | 450 | 1.2475 |
| 1.3713 | 0.3905 | 500 | 1.2420 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
johnphilos/EnsinoFilo3 | johnphilos | "2025-03-23T04:14:02Z" | 1 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-11T15:00:03Z" | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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Paulescu/crypto-sentiment-extractor | Paulescu | "2025-03-13T12:12:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-13T12:03:30Z" | ---
base_model: unsloth/llama-3.2-1b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Paulescu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-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)
|
abdulmannan-01/DeepSeek-R1-Distill-Llama-8B-Lora-Finetuned-Openscholar-Dataset-Adapter | abdulmannan-01 | "2025-02-17T20:19:56Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"region:us"
] | null | "2025-02-17T20:14:49Z" | ---
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0 |
prxy5605/e736fac6-4d00-48e7-8c9c-4475957f1e5b | prxy5605 | "2025-01-18T00:24:38Z" | 9 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-3B",
"base_model:adapter:unsloth/Llama-3.2-3B",
"license:llama3.2",
"region:us"
] | null | "2025-01-18T00:01:57Z" | ---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-3B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e736fac6-4d00-48e7-8c9c-4475957f1e5b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Llama-3.2-3B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- train_c4393383-ef1d-4e9c-b95c-18b4f735570d.json
ds_type: json
format: custom
path: /workspace/input_data/train_c4393383-ef1d-4e9c-b95c-18b4f735570d.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
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: prxy5605/e736fac6-4d00-48e7-8c9c-4475957f1e5b
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/train_c4393383-ef1d-4e9c-b95c-18b4f735570d.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 26fdf33d-c321-41d9-b33c-17de9fdf24d1
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 26fdf33d-c321-41d9-b33c-17de9fdf24d1
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e736fac6-4d00-48e7-8c9c-4475957f1e5b
This model is a fine-tuned version of [unsloth/Llama-3.2-3B](https://huggingface.co/unsloth/Llama-3.2-3B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0639
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0661 | 0.0008 | 1 | 1.7443 |
| 1.497 | 0.0407 | 50 | 1.2598 |
| 1.1156 | 0.0813 | 100 | 1.1870 |
| 1.5363 | 0.1220 | 150 | 1.0768 |
| 0.9245 | 0.1627 | 200 | 1.0639 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
liam168/chat-DialoGPT-small-zh | liam168 | "2021-08-04T09:01:41Z" | 21 | 5 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2022-03-02T23:29:05Z" | ---
language: zh
widget:
- text: "你们宿舍都是这么厉害的人吗"
license: apache-2.0
---
# liam168/chat-DialoGPT-small-zh
## Model description
用中文聊天数据训练的模型；
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
mode_name = 'liam168/chat-DialoGPT-small-zh'
tokenizer = AutoTokenizer.from_pretrained(mode_name)
model = AutoModelForCausalLM.from_pretrained(mode_name)
# Let's chat for 5 lines
for step in range(5):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
# pretty print last ouput tokens from bot
print("Answer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
RayneAmes/vanya_v1 | RayneAmes | "2025-02-10T21:57:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-02-10T21:54:59Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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] |
Jonjew/SerenaTheFirstDescendant2 | Jonjew | "2025-03-31T12:45:08Z" | 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 | "2025-03-31T12:44:46Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
a futuristic, armored character named Serena floating in space. She is
flying up in space against a huge sun background. She is surrounded by
whirling fire and air. backlight. Serena is dressed in a sleek, form-fitting
white bodysuit with high cut legs, revealing black thigh-high stockings. The
bodysuit features intricate, metallic detailing and a high collar. She wears
matching gauntlets and boots. Serena's armor includes large, intricate
shoulder pieces with metallic, angular designs and glowing, metallic wings
on her lower back with sharp, metallic extensions. Her blonde hair is styled
in a short, wavy bob. The overall aesthetic is a blend of cyberpunk and
futuristic military, with a focus on sharp, angular lines and metallic
textures. Intense, colorful lighting and deep shadows <lora:TFD-Serena:0.8>
output:
url: images/00072-2302721930.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: TFD-Serena, Serena
license: unknown
---
# Serena - The First Descendant
<Gallery />
## Model description
FROM https://civitai.com/models/1374035/serena-the-first-descendant-flux-lora
Trigger TFD-Serena, Serena
Strength 0.8 to 1.0
A (FLUX) Character LoRA for Serena from The First Descendant -videogame.
Also check out my other TFD LoRAs below.
Triggerword: TFD-Serena or Serena
Suggested Weight: 0.8-1.0
My Preview Images Generated on:
-flux1-dev-Q8_0.gguf + t5xxl_fp16 (ForgeUI)
-Euler, Simple
-832x1280 + 1.5x Hires. Fix (4x-UltraSharp -upscaler)
-Distilled CFG Scale: 3.5 (2.0 hires. fix)
-Only This LoRA enabled
Add some of the following to your prompt to help you get the outfit:
a futuristic, armored character named Serena. Serena is dressed in a sleek, form-fitting white bodysuit with high cut legs, revealing black thigh-high stockings. The bodysuit features intricate, metallic detailing and a high collar. She wears matching gauntlets and boots. Serena's armor includes large, intricate shoulder pieces with metallic, angular designs and metallic wings on her lower back with sharp, metallic extensions. Her blonde hair is styled in a short, wavy bob. The overall aesthetic is a blend of cyberpunk and futuristic military, with a focus on sharp, angular lines and metallic textures.
NOTE: The wings are not consistent and will be a pain to get right or even good enough.
You can remove certain parts if they interfere with image composition.
Example: Removing the part about the boots can make it easier to get close-up shots
## Trigger words
You should use `TFD-Serena` to trigger the image generation.
You should use `Serena` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/SerenaTheFirstDescendant2/tree/main) them in the Files & versions tab.
|
Genie-hub/lcking | Genie-hub | "2025-03-13T07:27:46Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-03-13T07:17:23Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: LCKING
---
# Lcking
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `LCKING` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Genie-hub/lcking', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
baby-dev/8338f475-098b-4938-8874-c018a6eeed55 | baby-dev | "2025-02-23T14:27:12Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Meta-Llama-3-8B",
"base_model:adapter:NousResearch/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | "2025-02-23T11:23:58Z" | ---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8338f475-098b-4938-8874-c018a6eeed55
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. -->
# 8338f475-098b-4938-8874-c018a6eeed55
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7331
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
abaddon182/6623ad1b-2b30-4c14-aa9a-484c00aff9f0 | abaddon182 | "2025-03-30T06:45:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-30T06:39:14Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
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.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
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src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
mci29/sn29_q1m5_gnpy | mci29 | "2025-03-01T13:19:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-01T13:15:44Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
tareknaous/readabert-hi | tareknaous | "2024-07-17T16:56:47Z" | 123 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"hi",
"arxiv:2305.14463",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-02-17T00:55:33Z" | ---
library_name: transformers
license: apache-2.0
language:
- hi
---
Muril-base (muril-base-cased) model fine-tuned on the Hindi portion of the ReadMe++ corpus for sentence-level readability prediction on a scale of 6-level CEFR scale
Github (Dataset and Python Package): https://github.com/tareknaous/readme
Paper: https://arxiv.org/abs/2305.14463 |
sgr23/distilbert-base-uncased-finetuned-squad-d5716d28 | sgr23 | "2023-05-27T18:10:53Z" | 105 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | "2023-05-27T17:57:26Z" | ---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
aleegis11/e3d60bf4-b6f3-432f-a420-ee571baf5305 | aleegis11 | "2025-01-24T16:18:58Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2.5-7B",
"base_model:adapter:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] | null | "2025-01-24T15:50:40Z" | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e3d60bf4-b6f3-432f-a420-ee571baf5305
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-7B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- d3e177dcf9fd6bd3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d3e177dcf9fd6bd3_train_data.json
type:
field_input: phonemes
field_instruction: style
field_output: text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: aleegis11/e3d60bf4-b6f3-432f-a420-ee571baf5305
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/d3e177dcf9fd6bd3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: c9a889cd-07f0-4336-9474-12f0774aebd1
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: c9a889cd-07f0-4336-9474-12f0774aebd1
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e3d60bf4-b6f3-432f-a420-ee571baf5305
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0639
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1411 | 0.0029 | 1 | 2.6982 |
| 0.7284 | 0.1462 | 50 | 0.2369 |
| 0.815 | 0.2924 | 100 | 0.1339 |
| 0.2101 | 0.4386 | 150 | 0.0853 |
| 0.2648 | 0.5848 | 200 | 0.0639 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
hopkins/mbart-finetuned-eng-deu-35 | hopkins | "2023-07-03T00:59:44Z" | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2023-07-03T00:45:38Z" | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-finetuned-eng-deu-35
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. -->
# mbart-finetuned-eng-deu-35
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6532
- Bleu: 20.8829
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
radames/AltDiffusion-m9-img2img | radames | "2023-05-06T01:09:51Z" | 4 | 6 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"image-to-image",
"multilingual",
"English(En)",
"Chinese(Zh)",
"Spanish(Es)",
"French(Fr)",
"Russian(Ru)",
"Japanese(Ja)",
"Korean(Ko)",
"Arabic(Ar)",
"Italian(It)",
"zh",
"arxiv:2211.06679",
"license:creativeml-openrail-m",
"diffusers:AltDiffusionPipeline",
"region:us"
] | image-to-image | "2023-05-06T01:09:04Z" | ---
language: zh
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- image-to-image
- multilingual
- English(En)
- Chinese(Zh)
- Spanish(Es)
- French(Fr)
- Russian(Ru)
- Japanese(Ja)
- Korean(Ko)
- Arabic(Ar)
- Italian(It)
- diffusers
extra_gated_prompt: >-
This model is open access and available to all, with a CreativeML OpenRAIL-M
license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or
harmful outputs or content
2. The authors claim no rights on the outputs you generate, you are free to
use them and are accountable for their use which must not go against the
provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as
a service. If you do, please be aware you have to include the same use
restrictions as the ones in the license and share a copy of the CreativeML
OpenRAIL-M to all your users (please read the license entirely and carefully)
Please read the full license carefully here:
https://huggingface.co/spaces/CompVis/stable-diffusion-license
extra_gated_heading: Please read the LICENSE to access this model
duplicated_from: BAAI/AltDiffusion-m9
---
# AltDiffusion
| 名称 Name | 任务 Task | 语言 Language(s) | 模型 Model | Github |
|:----------:| :----: |:-------------------:| :----: |:------:|
| AltDiffusion-m9 | 多模态 Multimodal | Multilingual | Stable Diffusion | [FlagAI](https://github.com/FlagAI-Open/FlagAI) |
# Gradio
We support a [Gradio](https://github.com/gradio-app/gradio) Web UI to run AltDiffusion-m9:
[](https://huggingface.co/spaces/akhaliq/AltDiffusion-m9)
# 模型信息 Model Information
我们使用 [AltCLIP-m9](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md)，基于 [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion) 训练了双语Diffusion模型，训练数据来自 [WuDao数据集](https://data.baai.ac.cn/details/WuDaoCorporaText) 和 [LAION](https://huggingface.co/datasets/ChristophSchuhmann/improved_aesthetics_6plus) 。
我们的版本在多语言对齐方面表现非常出色，是目前市面上开源的最强多语言版本，保留了原版stable diffusion的大部分能力，并且在某些例子上比有着比原版模型更出色的能力。
AltDiffusion-m9 模型由名为 AltCLIP-m9 的多语 CLIP 模型支持，该模型也可在本项目中访问。您可以阅读 [此教程](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md) 了解更多信息。
We used [AltCLIP-m9](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md), and trained a bilingual Diffusion model based on [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion), with training data from [WuDao dataset](https://data.baai.ac.cn/details/WuDaoCorporaText) and [LAION](https://huggingface.co/datasets/laion/laion2B-en).
Our model performs well in aligning multilanguage and is the strongest open-source version on the market today, retaining most of the stable diffusion capabilities of the original, and in some cases even better than the original model.
AltDiffusion-m9 model is backed by a multilingual CLIP model named AltCLIP-m9, which is also accessible in FlagAI. You can read [this tutorial](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP/README.md) for more information.
## 引用
关于AltCLIP-m9，我们已经推出了相关报告，有更多细节可以查阅，如对您的工作有帮助，欢迎引用。
If you find this work helpful, please consider to cite
```
@article{https://doi.org/10.48550/arxiv.2211.06679,
doi = {10.48550/ARXIV.2211.06679},
url = {https://arxiv.org/abs/2211.06679},
author = {Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences},
title = {AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}
```
# 模型权重 Model Weights
第一次运行AltDiffusion-m9模型时会自动从huggingface下载如下权重,
The following weights are automatically downloaded from HF when the AltDiffusion-m9 model is run for the first time:
| 模型名称 Model name | 大小 Size | 描述 Description |
|------------------------------|---------|-------------------------------------------------------|
| StableDiffusionSafetyChecker | 1.13G | 图片的安全检查器；Safety checker for image |
| AltDiffusion-m9 | 8.0G | support English(En), Chinese(Zh), Spanish(Es), French(Fr), Russian(Ru), Japanese(Ja), Korean(Ko), Arabic(Ar) and Italian(It) |
| AltCLIP-m9 | 3.22G | support English(En), Chinese(Zh), Spanish(Es), French(Fr), Russian(Ru), Japanese(Ja), Korean(Ko), Arabic(Ar) and Italian(It) |
# 示例 Example
## 🧨Diffusers Example
**AltDiffusion-m9** 已被添加到 🧨Diffusers!
我们的[代码示例](https://colab.research.google.com/drive/1htPovT5YNutl2i31mIYrOzlIgGLm06IX#scrollTo=1TrIQp9N1Bnm)已放到colab上，欢迎使用。
您可以在 [此处](https://huggingface.co/docs/diffusers/main/en/api/pipelines/alt_diffusion) 查看文档页面。
以下示例将使用fast DPM 调度程序生成图像, 在V100 上耗时大约为 2 秒。
You can run our diffusers example through [here](https://colab.research.google.com/drive/1htPovT5YNutl2i31mIYrOzlIgGLm06IX#scrollTo=1TrIQp9N1Bnm) in colab.
You can see the documentation page [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/alt_diffusion).
The following example will use the fast DPM scheduler to generate an image in ca. 2 seconds on a V100.
First you should install diffusers main branch and some dependencies:
```
pip install git+https://github.com/huggingface/diffusers.git torch transformers accelerate sentencepiece
```
then you can run the following example:
```python
from diffusers import AltDiffusionPipeline, DPMSolverMultistepScheduler
import torch
pipe = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion-m9", torch_dtype=torch.float16, revision="fp16")
pipe = pipe.to("cuda")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
prompt = "黑暗精灵公主，非常详细，幻想，非常详细，数字绘画，概念艺术，敏锐的焦点，插图"
# or in English:
# prompt = "dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap"
image = pipe(prompt, num_inference_steps=25).images[0]
image.save("./alt.png")
```
## Transformers Example
```python
import os
import torch
import transformers
from transformers import BertPreTrainedModel
from transformers.models.clip.modeling_clip import CLIPPreTrainedModel
from transformers.models.xlm_roberta.tokenization_xlm_roberta import XLMRobertaTokenizer
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers import StableDiffusionPipeline
from transformers import BertPreTrainedModel,BertModel,BertConfig
import torch.nn as nn
import torch
from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
from transformers import XLMRobertaModel
from transformers.activations import ACT2FN
from typing import Optional
class RobertaSeriesConfig(XLMRobertaConfig):
def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=768,pooler_fn='cls',learn_encoder=False, **kwargs):
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.project_dim = project_dim
self.pooler_fn = pooler_fn
# self.learn_encoder = learn_encoder
class RobertaSeriesModelWithTransformation(BertPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
base_model_prefix = 'roberta'
config_class= XLMRobertaConfig
def __init__(self, config):
super().__init__(config)
self.roberta = XLMRobertaModel(config)
self.transformation = nn.Linear(config.hidden_size, config.project_dim)
self.post_init()
def get_text_embeds(self,bert_embeds,clip_embeds):
return self.merge_head(torch.cat((bert_embeds,clip_embeds)))
def set_tokenizer(self, tokenizer):
self.tokenizer = tokenizer
def forward(self, input_ids: Optional[torch.Tensor] = None) :
attention_mask = (input_ids != self.tokenizer.pad_token_id).to(torch.int64)
outputs = self.base_model(
input_ids=input_ids,
attention_mask=attention_mask,
)
projection_state = self.transformation(outputs.last_hidden_state)
return (projection_state,)
model_path_encoder = "BAAI/RobertaSeriesModelWithTransformation"
model_path_diffusion = "BAAI/AltDiffusion-m9"
device = "cuda"
seed = 12345
tokenizer = XLMRobertaTokenizer.from_pretrained(model_path_encoder, use_auth_token=True)
tokenizer.model_max_length = 77
text_encoder = RobertaSeriesModelWithTransformation.from_pretrained(model_path_encoder, use_auth_token=True)
text_encoder.set_tokenizer(tokenizer)
print("text encode loaded")
pipe = StableDiffusionPipeline.from_pretrained(model_path_diffusion,
tokenizer=tokenizer,
text_encoder=text_encoder,
use_auth_token=True,
)
print("diffusion pipeline loaded")
pipe = pipe.to(device)
prompt = "Thirty years old lee evans as a sad 19th century postman. detailed, soft focus, candle light, interesting lights, realistic, oil canvas, character concept art by munkácsy mihály, csók istván, john everett millais, henry meynell rheam, and da vinci"
with torch.no_grad():
image = pipe(prompt, guidance_scale=7.5).images[0]
image.save("3.png")
```
您可以在`predict_generate_images`函数里通过改变参数来调整设置，具体信息如下:
More parameters of predict_generate_images for you to adjust for `predict_generate_images` are listed below:
| 参数名 Parameter | 类型 Type | 描述 Description |
|--------------------------------|------------|-------------------------------------------------------|
| prompt | str | 提示文本; The prompt text |
| out_path | str | 输出路径; The output path to save images |
| n_samples | int | 输出图片数量; Number of images to be generate |
| skip_grid | bool | 如果为True, 会将所有图片拼接在一起，输出一张新的图片; If set to true, image gridding step will be skipped |
| ddim_step | int | DDIM模型的步数; Number of steps in ddim model |
| plms | bool | 如果为True, 则会使用plms模型; If set to true, PLMS Sampler instead of DDIM Sampler will be applied |
| scale | float | 这个值决定了文本在多大程度上影响生成的图片，值越大影响力越强; This value determines how important the prompt incluences generate images |
| H | int | 图片的高度; Height of image |
| W | int | 图片的宽度; Width of image |
| C | int | 图片的channel数; Numeber of channels of generated images |
| seed | int | 随机种子; Random seed number |
注意：模型推理要求一张至少10G以上的GPU。
Note that the model inference requires a GPU of at least 10G above.
# 更多生成结果 More Results
## multilanguage examples
同一句prompts不同语言生成的人脸不一样！
One prompts in different languages generates different faces!
## 中英文对齐能力 Chinese and English alignment ability
### prompt:dark elf princess, highly detailed, d & d, fantasy, highly detailed, digital painting, trending on artstation, concept art, sharp focus, illustration, art by artgerm and greg rutkowski and fuji choko and viktoria gavrilenko and hoang lap
### 英文生成结果/Generated results from English prompts
### prompt:黑暗精灵公主，非常详细，幻想，非常详细，数字绘画，概念艺术，敏锐的焦点，插图
### 中文生成结果/Generated results from Chinese prompts
## 中文表现能力/The performance for Chinese prompts
## prompt:带墨镜的男孩肖像，充满细节，8K高清
## prompt:带墨镜的中国男孩肖像，充满细节，8K高清
## 长图生成能力/The ability to generate long images
### prompt: 一只带着帽子的小狗
### 原版 stable diffusion：
### Ours:
注: 此处长图生成技术由右脑科技(RightBrain AI)提供。
Note: The long image generation technology here is provided by Right Brain Technology.
# 许可/License
该模型通过 [CreativeML Open RAIL-M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) 获得许可。作者对您生成的输出不主张任何权利，您可以自由使用它们并对它们的使用负责，不得违反本许可中的规定。该许可证禁止您分享任何违反任何法律、对他人造成伤害、传播任何可能造成伤害的个人信息、传播错误信息和针对弱势群体的任何内容。您可以出于商业目的修改和使用模型，但必须包含相同使用限制的副本。有关限制的完整列表，请[阅读许可证](https://huggingface.co/spaces/CompVis/stable-diffusion-license) 。
The model is licensed with a [CreativeML Open RAIL-M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license). The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in this license. The license forbids you from sharing any content that violates any laws, produce any harm to a person, disseminate any personal information that would be meant for harm, spread misinformation and target vulnerable groups. You can modify and use the model for commercial purposes, but a copy of the same use restrictions must be included. For the full list of restrictions please [read the license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) . |
JCX-kcuf/Llama-2-7b-hf-gpt-4-80k | JCX-kcuf | "2024-03-11T15:44:32Z" | 48 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-03-10T16:34:35Z" | ---
license: apache-2.0
---
## Description
This model is finetuned on the distillation data from GPT-4.
The base model is meta-llama/Llama-2-7b-hf
## Usage
The model has a query format as in llama-2.
```
<s> [INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
<</SYS>>
{query} [/INST]
``` |
tensorblock/PlatYi-34B-Q-GGUF | tensorblock | "2025-01-09T09:32:42Z" | 180 | 0 | transformers | [
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"en",
"dataset:garage-bAInd/Open-Platypus",
"base_model:kyujinpy/PlatYi-34B-Q",
"base_model:quantized:kyujinpy/PlatYi-34B-Q",
"license:cc-by-nc-sa-4.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-09T07:07:06Z" | ---
language:
- en
license: cc-by-nc-sa-4.0
library_name: transformers
datasets:
- garage-bAInd/Open-Platypus
pipeline_tag: text-generation
tags:
- TensorBlock
- GGUF
base_model: kyujinpy/PlatYi-34B-Q
model-index:
- name: PlatYi-34B-Q
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 66.89
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/PlatYi-34B-Q
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 85.14
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/PlatYi-34B-Q
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.66
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/PlatYi-34B-Q
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 53.03
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/PlatYi-34B-Q
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.48
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/PlatYi-34B-Q
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 53.98
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/PlatYi-34B-Q
name: Open LLM Leaderboard
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;">
Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a>
</p>
</div>
</div>
## kyujinpy/PlatYi-34B-Q - GGUF
This repo contains GGUF format model files for [kyujinpy/PlatYi-34B-Q](https://huggingface.co/kyujinpy/PlatYi-34B-Q).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).
<div style="text-align: left; margin: 20px 0;">
<a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Run them on the TensorBlock client using your local machine ↗
</a>
</div>
## Prompt template
```
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [PlatYi-34B-Q-Q2_K.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q2_K.gguf) | Q2_K | 12.825 GB | smallest, significant quality loss - not recommended for most purposes |
| [PlatYi-34B-Q-Q3_K_S.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q3_K_S.gguf) | Q3_K_S | 14.960 GB | very small, high quality loss |
| [PlatYi-34B-Q-Q3_K_M.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q3_K_M.gguf) | Q3_K_M | 16.655 GB | very small, high quality loss |
| [PlatYi-34B-Q-Q3_K_L.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q3_K_L.gguf) | Q3_K_L | 18.139 GB | small, substantial quality loss |
| [PlatYi-34B-Q-Q4_0.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q4_0.gguf) | Q4_0 | 19.467 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [PlatYi-34B-Q-Q4_K_S.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q4_K_S.gguf) | Q4_K_S | 19.599 GB | small, greater quality loss |
| [PlatYi-34B-Q-Q4_K_M.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q4_K_M.gguf) | Q4_K_M | 20.659 GB | medium, balanced quality - recommended |
| [PlatYi-34B-Q-Q5_0.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q5_0.gguf) | Q5_0 | 23.708 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [PlatYi-34B-Q-Q5_K_S.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q5_K_S.gguf) | Q5_K_S | 23.708 GB | large, low quality loss - recommended |
| [PlatYi-34B-Q-Q5_K_M.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q5_K_M.gguf) | Q5_K_M | 24.322 GB | large, very low quality loss - recommended |
| [PlatYi-34B-Q-Q6_K.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q6_K.gguf) | Q6_K | 28.214 GB | very large, extremely low quality loss |
| [PlatYi-34B-Q-Q8_0.gguf](https://huggingface.co/tensorblock/PlatYi-34B-Q-GGUF/blob/main/PlatYi-34B-Q-Q8_0.gguf) | Q8_0 | 36.542 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/PlatYi-34B-Q-GGUF --include "PlatYi-34B-Q-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/PlatYi-34B-Q-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
facebook/hiera-base-plus-224-mae-hf | facebook | "2024-06-20T10:47:16Z" | 30 | 0 | transformers | [
"transformers",
"safetensors",
"hiera",
"pretraining",
"en",
"dataset:imagenet-1k",
"arxiv:2306.00989",
"arxiv:2010.11929",
"arxiv:1512.03385",
"arxiv:2103.14030",
"arxiv:2104.11227",
"arxiv:2111.06377",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2024-05-12T10:51:25Z" | ---
datasets:
- imagenet-1k
language:
- en
library_name: transformers
license: cc-by-nc-4.0
---
# Hiera Model (Tiny, fine-tuned on IN1K)
**Hiera** is a _hierarchical_ vision transformer that is fast, powerful, and, above all, _simple_. It was introduced in the paper [Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles](https://arxiv.org/abs/2306.00989/) and outperforms the state-of-the-art across a wide array of image and video tasks _while being much faster_.
<p align="center">
<img src="https://github.com/facebookresearch/hiera/raw/main/examples/img/inference_speed.png" width="75%">
</p>
## How does it work?
Vision transformers like [ViT](https://arxiv.org/abs/2010.11929) use the same spatial resolution and number of features throughout the whole network. But this is inefficient: the early layers don't need that many features, and the later layers don't need that much spatial resolution. Prior hierarchical models like [ResNet](https://arxiv.org/abs/1512.03385) accounted for this by using fewer features at the start and less spatial resolution at the end.
Several domain specific vision transformers have been introduced that employ this hierarchical design, such as [Swin](https://arxiv.org/abs/2103.14030) or [MViT](https://arxiv.org/abs/2104.11227). But in the pursuit of state-of-the-art results using fully supervised training on ImageNet-1K, these models have become more and more complicated as they add specialized modules to make up for spatial biases that ViTs lack. While these changes produce effective models with attractive FLOP counts, under the hood the added complexity makes these models _slower_ overall.
We show that a lot of this bulk is actually _unnecessary_. Instead of manually adding spatial bases through architectural changes, we opt to _teach_ the model these biases instead. By training with [MAE](https://arxiv.org/abs/2111.06377), we can simplify or remove _all_ of these bulky modules in existing transformers and _increase accuracy_ in the process. The result is Hiera, an extremely efficient and simple architecture that outperforms the state-of-the-art in several image and video recognition tasks.
## Intended uses & limitations
Hiera can be used for image classification, feature extraction or masked image modeling. This checkpoint in specific is intended for **Masked Image Modeling**.
### How to use
```python
from transformers import AutoImageProcessor, HieraForPreTraining
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_processor = AutoImageProcessor.from_pretrained("facebook/hiera-base-plus-224-mae-hf")
model = HieraForPreTraining.from_pretrained("facebook/hiera-base-plus-224-mae-hf")
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
loss = outputs.loss
```
### BibTeX entry and citation info
If you use Hiera or this code in your work, please cite:
```
@article{ryali2023hiera,
title={Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles},
author={Ryali, Chaitanya and Hu, Yuan-Ting and Bolya, Daniel and Wei, Chen and Fan, Haoqi and Huang, Po-Yao and Aggarwal, Vaibhav and Chowdhury, Arkabandhu and Poursaeed, Omid and Hoffman, Judy and Malik, Jitendra and Li, Yanghao and Feichtenhofer, Christoph},
journal={ICML},
year={2023}
} |
davidschulte/ESM_silicone_dyda_e | davidschulte | "2025-03-28T13:32:35Z" | 21 | 0 | null | [
"safetensors",
"embedding_space_map",
"BaseLM:bert-base-multilingual-uncased",
"dataset:eusip/silicone",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-12-09T22:13:57Z" | ---
base_model: bert-base-multilingual-uncased
datasets:
- eusip/silicone
license: apache-2.0
tags:
- embedding_space_map
- BaseLM:bert-base-multilingual-uncased
---
# ESM eusip/silicone
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
ESM
- **Developed by:** David Schulte
- **Model type:** ESM
- **Base Model:** bert-base-multilingual-uncased
- **Intermediate Task:** eusip/silicone
- **ESM architecture:** linear
- **ESM embedding dimension:** 768
- **Language(s) (NLP):** [More Information Needed]
- **License:** Apache-2.0 license
- **ESM version:** 0.1.0
## Training Details
### Intermediate Task
- **Task ID:** eusip/silicone
- **Subset [optional]:** dyda_e
- **Text Column:** Utterance
- **Label Column:** Label
- **Dataset Split:** train
- **Sample size [optional]:** 10000
- **Sample seed [optional]:** 42
### Training Procedure [optional]
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Language Model Training Hyperparameters [optional]
- **Epochs:** 3
- **Batch size:** 32
- **Learning rate:** 2e-05
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### ESM Training Hyperparameters [optional]
- **Epochs:** 10
- **Batch size:** 32
- **Learning rate:** 0.001
- **Weight Decay:** 0.01
- **Optimizer**: AdamW
### Additional trainiung details [optional]
## Model evaluation
### Evaluation of fine-tuned language model [optional]
### Evaluation of ESM [optional]
MSE:
### Additional evaluation details [optional]
## What are Embedding Space Maps used for?
Embedding Space Maps are a part of ESM-LogME, a efficient method for finding intermediate datasets for transfer learning. There are two reasons to use ESM-LogME:
### You don't have enough training data for your problem
If you don't have a enough training data for your problem, just use ESM-LogME to find more.
You can supplement model training by including publicly available datasets in the training process.
1. Fine-tune a language model on suitable intermediate dataset.
2. Fine-tune the resulting model on your target dataset.
This workflow is called intermediate task transfer learning and it can significantly improve the target performance.
But what is a suitable dataset for your problem? ESM-LogME enable you to quickly rank thousands of datasets on the Hugging Face Hub by how well they are exptected to transfer to your target task.
### You want to find similar datasets to your target dataset
Using ESM-LogME can be used like search engine on the Hugging Face Hub. You can find similar tasks to your target task without having to rely on heuristics. ESM-LogME estimates how language models fine-tuned on each intermediate task would benefinit your target task. This quantitative approach combines the effects of domain similarity and task similarity.
## How can I use ESM-LogME / ESMs?
[](https://pypi.org/project/hf-dataset-selector)
We release **hf-dataset-selector**, a Python package for intermediate task selection using Embedding Space Maps.
**hf-dataset-selector** fetches ESMs for a given language model and uses it to find the best dataset for applying intermediate training to the target task. ESMs are found by their tags on the Huggingface Hub.
```python
from hfselect import Dataset, compute_task_ranking
# Load target dataset from the Hugging Face Hub
dataset = Dataset.from_hugging_face(
name="stanfordnlp/imdb",
split="train",
text_col="text",
label_col="label",
is_regression=False,
num_examples=1000,
seed=42
)
# Fetch ESMs and rank tasks
task_ranking = compute_task_ranking(
dataset=dataset,
model_name="bert-base-multilingual-uncased"
)
# Display top 5 recommendations
print(task_ranking[:5])
```
```python
1. davanstrien/test_imdb_embedd2 Score: -0.618529
2. davanstrien/test_imdb_embedd Score: -0.618644
3. davanstrien/test1 Score: -0.619334
4. stanfordnlp/imdb Score: -0.619454
5. stanfordnlp/sst Score: -0.62995
```
| Rank | Task ID | Task Subset | Text Column | Label Column | Task Split | Num Examples | ESM Architecture | Score |
|-------:|:------------------------------|:----------------|:--------------|:---------------|:-------------|---------------:|:-------------------|----------:|
| 1 | davanstrien/test_imdb_embedd2 | default | text | label | train | 10000 | linear | -0.618529 |
| 2 | davanstrien/test_imdb_embedd | default | text | label | train | 10000 | linear | -0.618644 |
| 3 | davanstrien/test1 | default | text | label | train | 10000 | linear | -0.619334 |
| 4 | stanfordnlp/imdb | plain_text | text | label | train | 10000 | linear | -0.619454 |
| 5 | stanfordnlp/sst | dictionary | phrase | label | dictionary | 10000 | linear | -0.62995 |
| 6 | stanfordnlp/sst | default | sentence | label | train | 8544 | linear | -0.63312 |
| 7 | kuroneko5943/snap21 | CDs_and_Vinyl_5 | sentence | label | train | 6974 | linear | -0.634365 |
| 8 | kuroneko5943/snap21 | Video_Games_5 | sentence | label | train | 6997 | linear | -0.638787 |
| 9 | kuroneko5943/snap21 | Movies_and_TV_5 | sentence | label | train | 6989 | linear | -0.639068 |
| 10 | fancyzhx/amazon_polarity | amazon_polarity | content | label | train | 10000 | linear | -0.639718 |
For more information on how to use ESMs please have a look at the [official Github repository](https://github.com/davidschulte/hf-dataset-selector). We provide documentation further documentation and tutorials for finding intermediate datasets and training your own ESMs.
## How do Embedding Space Maps work?
<!-- This section describes the evaluation protocols and provides the results. -->
Embedding Space Maps (ESMs) are neural networks that approximate the effect of fine-tuning a language model on a task. They can be used to quickly transform embeddings from a base model to approximate how a fine-tuned model would embed the the input text.
ESMs can be used for intermediate task selection with the ESM-LogME workflow.
## How can I use Embedding Space Maps for Intermediate Task Selection?
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
If you are using this Embedding Space Maps, please cite our [paper](https://aclanthology.org/2024.emnlp-main.529/).
**BibTeX:**
```
@inproceedings{schulte-etal-2024-less,
title = "Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning",
author = "Schulte, David and
Hamborg, Felix and
Akbik, Alan",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.529/",
doi = "10.18653/v1/2024.emnlp-main.529",
pages = "9431--9442",
abstract = "Intermediate task transfer learning can greatly improve model performance. If, for example, one has little training data for emotion detection, first fine-tuning a language model on a sentiment classification dataset may improve performance strongly. But which task to choose for transfer learning? Prior methods producing useful task rankings are infeasible for large source pools, as they require forward passes through all source language models. We overcome this by introducing Embedding Space Maps (ESMs), light-weight neural networks that approximate the effect of fine-tuning a language model. We conduct the largest study on NLP task transferability and task selection with 12k source-target pairs. We find that applying ESMs on a prior method reduces execution time and disk space usage by factors of 10 and 278, respectively, while retaining high selection performance (avg. regret@5 score of 2.95)."
}
```
**APA:**
```
Schulte, D., Hamborg, F., & Akbik, A. (2024, November). Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer Learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (pp. 9431-9442).
```
## Additional Information
|
MelanieKoe/w2v2-base-10k-voxpopuli-ft-de_lr1e-4_at0.8_da1 | MelanieKoe | "2024-03-27T10:29:37Z" | 78 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base-10k-voxpopuli-ft-de",
"base_model:finetune:facebook/wav2vec2-base-10k-voxpopuli-ft-de",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-03-20T15:05:20Z" | ---
license: cc-by-nc-4.0
base_model: facebook/wav2vec2-base-10k-voxpopuli-ft-de
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v2-base-10k-voxpopuli-ft-de_lr1e-4_at0.8_da1
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. -->
# w2v2-base-10k-voxpopuli-ft-de_lr1e-4_at0.8_da1
This model is a fine-tuned version of [facebook/wav2vec2-base-10k-voxpopuli-ft-de](https://huggingface.co/facebook/wav2vec2-base-10k-voxpopuli-ft-de) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8259
- Wer: 0.1632
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8086 | 5.43 | 250 | 0.8872 | 0.2456 |
| 0.1358 | 10.87 | 500 | 1.1126 | 0.1858 |
| 0.0916 | 16.3 | 750 | 1.5579 | 0.1991 |
| 0.0716 | 21.74 | 1000 | 1.2674 | 0.1833 |
| 0.0584 | 27.17 | 1250 | 1.6728 | 0.1768 |
| 0.0464 | 32.61 | 1500 | 1.8259 | 0.1632 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Jjateen/a2c-PandaReachDense-v3 | Jjateen | "2023-12-23T21:47:33Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-12-23T21:43:17Z" | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.25 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
omrudra998/KishanSevakHindi | omrudra998 | "2024-11-18T12:02:49Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-18T11:56:41Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Serendien/topic_learning_llama | Serendien | "2024-11-15T06:31:30Z" | 180 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-15T06:31:15Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sdyy/bb | sdyy | "2024-12-20T03:12:53Z" | 9 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"aqlm",
"region:us"
] | null | "2024-12-20T02:16:00Z" | ---
license: apache-2.0
---
ISTA-DASLab/Meta-Llama-3-70B-AQLM-PV-1Bit-1x16
and
tokenizer.model
from
https://huggingface.co/meta-llama/Meta-Llama-3-70B/blob/main/original/tokenizer.model
|
valhalla/SwinIR-real-sr-M-x2-GAN | valhalla | "2022-10-23T17:44:40Z" | 2 | 0 | transformers | [
"transformers",
"jax",
"swin-ir",
"region:us"
] | null | "2022-10-23T15:44:22Z" | ---
tags:
- swin-ir
inference: false
--- |
alekskusz/distilbert-base-uncased-distilled-clinc | alekskusz | "2024-10-18T10:49:22Z" | 106 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-10-15T09:28:20Z" | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1677
- Accuracy: 0.9510
## 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: 0.00015488796175955455
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 19
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8652 | 1.0 | 318 | 0.2578 | 0.9277 |
| 0.1803 | 2.0 | 636 | 0.2039 | 0.9397 |
| 0.1226 | 3.0 | 954 | 0.1887 | 0.9442 |
| 0.1064 | 4.0 | 1272 | 0.1808 | 0.9481 |
| 0.0963 | 5.0 | 1590 | 0.1770 | 0.9468 |
| 0.0928 | 6.0 | 1908 | 0.1826 | 0.9477 |
| 0.092 | 7.0 | 2226 | 0.1777 | 0.95 |
| 0.0876 | 8.0 | 2544 | 0.1719 | 0.9516 |
| 0.0861 | 9.0 | 2862 | 0.1813 | 0.9455 |
| 0.0868 | 10.0 | 3180 | 0.1804 | 0.9471 |
| 0.0841 | 11.0 | 3498 | 0.1749 | 0.9484 |
| 0.0834 | 12.0 | 3816 | 0.1764 | 0.9487 |
| 0.0817 | 13.0 | 4134 | 0.1714 | 0.9513 |
| 0.081 | 14.0 | 4452 | 0.1727 | 0.9503 |
| 0.0802 | 15.0 | 4770 | 0.1707 | 0.95 |
| 0.0794 | 16.0 | 5088 | 0.1697 | 0.9506 |
| 0.0787 | 17.0 | 5406 | 0.1683 | 0.9523 |
| 0.0784 | 18.0 | 5724 | 0.1684 | 0.9510 |
| 0.078 | 19.0 | 6042 | 0.1677 | 0.9510 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Tokenizers 0.20.1
|
Nadav-Deepchecks/safe_input_classifier_1203 | Nadav-Deepchecks | "2024-12-03T13:44:44Z" | 104 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-03T13:44:07Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
nhung01/45492c56-1f50-40b9-8675-b1f16f4da4cd | nhung01 | "2025-01-14T23:40:49Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codegemma-2b",
"base_model:adapter:unsloth/codegemma-2b",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-14T23:23:21Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codegemma-2b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 45492c56-1f50-40b9-8675-b1f16f4da4cd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/codegemma-2b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 772ddb1b0bc59bd0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/772ddb1b0bc59bd0_train_data.json
type:
field_instruction: prompt
field_output: model_1_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: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nhung01/45492c56-1f50-40b9-8675-b1f16f4da4cd
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/772ddb1b0bc59bd0_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: 74637dd8-3572-49dc-be4a-cbf8f9298e12
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 74637dd8-3572-49dc-be4a-cbf8f9298e12
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 45492c56-1f50-40b9-8675-b1f16f4da4cd
This model is a fine-tuned version of [unsloth/codegemma-2b](https://huggingface.co/unsloth/codegemma-2b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7558
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.9163 | 0.0530 | 200 | 1.7558 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
llmixer/BigLiz-120b | llmixer | "2024-03-12T07:23:33Z" | 19 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-01-16T19:45:22Z" | ---
license: llama2
pipeline_tag: text-generation
---
# BigLiz 120B
<img src="https://cdn-uploads.huggingface.co/production/uploads/65a6db055c58475cf9e6def1/trbJntEb6rKJacv7XJYDK.png" width=600>
A Goliath-120b style frankenmerge of lzlv-70b and WinterGoddess-1.4x-70b.
# Prompting Format
Vicuna and Alpaca.
# Merge process
The models used in the merge are [lzlv-70b](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf) and [WinterGoddess-1.4x-70b](https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2).
```yaml
slices:
- sources:
- model: lizpreciatior_lzlv_70b_fp16_hf
layer_range: [0, 16]
- sources:
- model: Sao10K_WinterGoddess-1.4x-70B-L2
layer_range: [8, 24]
- sources:
- model: lizpreciatior_lzlv_70b_fp16_hf
layer_range: [17, 32]
- sources:
- model: Sao10K_WinterGoddess-1.4x-70B-L2
layer_range: [25, 40]
- sources:
- model: lizpreciatior_lzlv_70b_fp16_hf
layer_range: [33, 48]
- sources:
- model: Sao10K_WinterGoddess-1.4x-70B-L2
layer_range: [41, 56]
- sources:
- model: lizpreciatior_lzlv_70b_fp16_hf
layer_range: [49, 64]
- sources:
- model: Sao10K_WinterGoddess-1.4x-70B-L2
layer_range: [57, 72]
- sources:
- model: lizpreciatior_lzlv_70b_fp16_hf
layer_range: [65, 80]
merge_method: passthrough
dtype: float16
```
# Acknowledgements
[@lizpreciatior](https://huggingface.co/lizpreciatior) For creating lzlv
[@Sao10K](https://huggingface.co/Sao10K) For creating WinterGoddess
[@alpindale](https://huggingface.co/alpindale) For creating the original Goliath
[@chargoddard](https://huggingface.co/chargoddard) For developing [mergekit](https://github.com/cg123/mergekit). |
jiinking/5_bitwise_MQA_llama_model | jiinking | "2025-03-11T09:41:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-11T09:12:27Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k | cognitivecomputations | "2024-06-23T16:16:28Z" | 2,809 | 18 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-23T15:29:59Z" | ---
license: apache-2.0
base_model: 01-ai/Yi-1.5-34B-32k
tags:
- generated_from_trainer
- axolotl
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
---
# Dolphin 2.9.3 Yi 1.5 34b 32k 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
[](https://discord.gg/cognitivecomputations)
Discord: https://discord.gg/cognitivecomputations
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
Our appreciation for the sponsors of Dolphin 2.9.3:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 8xH100 node
- [OnDemand](https://on-demand.io/) - provided inference sponsorship
This model is based on Yi-1.5-34b-32k, and is governed by the apache 2.0 license.
The base model has 32k context, and our finetuning took place with 8192 sequence length.
Dolphin 2.9.3 uses ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9.3 has a variety of instruction following, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. We have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to apache 2.0 license. We grant permission for any use, including commercial. Dolphin was trained on data generated from GPT4, among other models.
## Evals
## Training
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: 01-ai/Yi-1.5-34B-32k
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true
# load_in_8bit: false
load_in_4bit: true
# strict: false
adapter: qlora
lora_modules_to_save: [embed_tokens, lm_head]
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: false
lora_fan_in_fan_out:
datasets:
- path: /workspace/datasets/dolphin-2.9.3/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/SystemChat_filtered_sharegpt.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/SystemChat_multilingual_sharegpt.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9.3/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: dolphin-2.9.3-yi34b-prepared
val_set_size: 0.01
output_dir: ./dolphin-2.9.3-out
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project: dolphin-2.9.3-yi-1.5-34b
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
# evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 4
save_total_limit: 2
save_steps:
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
bos_token: "<|startoftext|>"
eos_token: "<|im_end|>"
pad_token: "<unk>"
unk_token: "<unk>"
tokens:
- "<|im_start|>"
#unfrozen_parameters:
lora_target_modules:
# input_layernorm layers
# - model.layers.0.input_layernorm
# - model.layers.1.input_layernorm
# - model.layers.2.input_layernorm
# - model.layers.3.input_layernorm
# - model.layers.4.input_layernorm
# - model.layers.5.input_layernorm
# - model.layers.6.input_layernorm
# - model.layers.7.input_layernorm
# - model.layers.8.input_layernorm
# - model.layers.9.input_layernorm
# - model.layers.10.input_layernorm
# - model.layers.11.input_layernorm
# - model.layers.12.input_layernorm
# - model.layers.13.input_layernorm
# - model.layers.14.input_layernorm
# - model.layers.15.input_layernorm
# - model.layers.16.input_layernorm
# - model.layers.17.input_layernorm
# - model.layers.18.input_layernorm
# - model.layers.19.input_layernorm
# - model.layers.20.input_layernorm
# - model.layers.21.input_layernorm
# - model.layers.22.input_layernorm
# - model.layers.23.input_layernorm
# - model.layers.24.input_layernorm
# - model.layers.25.input_layernorm
# - model.layers.26.input_layernorm
# - model.layers.27.input_layernorm
# - model.layers.28.input_layernorm
# - model.layers.29.input_layernorm
- lm_head
# mlp.down_proj layers
- model.layers.44.mlp.down_proj
- model.layers.45.mlp.down_proj
- model.layers.46.mlp.down_proj
- model.layers.47.mlp.down_proj
- model.layers.43.mlp.down_proj
- model.layers.48.mlp.down_proj
- model.layers.49.mlp.down_proj
- model.layers.42.mlp.down_proj
- model.layers.50.mlp.down_proj
- model.layers.41.mlp.down_proj
- model.layers.51.mlp.down_proj
- model.layers.52.mlp.down_proj
- model.layers.39.mlp.down_proj
- model.layers.40.mlp.down_proj
- model.layers.53.mlp.down_proj
- model.layers.54.mlp.down_proj
- model.layers.38.mlp.down_proj
- model.layers.56.mlp.down_proj
- model.layers.55.mlp.down_proj
- model.layers.37.mlp.down_proj
- model.layers.36.mlp.down_proj
- model.layers.57.mlp.down_proj
- model.layers.35.mlp.down_proj
- model.layers.12.mlp.down_proj
- model.layers.13.mlp.down_proj
- model.layers.16.mlp.down_proj
- model.layers.14.mlp.down_proj
- model.layers.11.mlp.down_proj
- model.layers.34.mlp.down_proj
- model.layers.17.mlp.down_proj
# mlp.gate_proj layers
- model.layers.57.mlp.gate_proj
- model.layers.58.mlp.gate_proj
- model.layers.56.mlp.gate_proj
- model.layers.55.mlp.gate_proj
- model.layers.54.mlp.gate_proj
- model.layers.35.mlp.gate_proj
- model.layers.34.mlp.gate_proj
- model.layers.53.mlp.gate_proj
- model.layers.26.mlp.gate_proj
- model.layers.52.mlp.gate_proj
- model.layers.25.mlp.gate_proj
- model.layers.33.mlp.gate_proj
- model.layers.51.mlp.gate_proj
- model.layers.18.mlp.gate_proj
- model.layers.32.mlp.gate_proj
- model.layers.36.mlp.gate_proj
- model.layers.24.mlp.gate_proj
- model.layers.17.mlp.gate_proj
- model.layers.23.mlp.gate_proj
- model.layers.31.mlp.gate_proj
- model.layers.50.mlp.gate_proj
- model.layers.19.mlp.gate_proj
- model.layers.15.mlp.gate_proj
- model.layers.27.mlp.gate_proj
- model.layers.37.mlp.gate_proj
- model.layers.14.mlp.gate_proj
- model.layers.39.mlp.gate_proj
- model.layers.11.mlp.gate_proj
- model.layers.29.mlp.gate_proj
- model.layers.28.mlp.gate_proj
# mlp.up_proj layers
- model.layers.21.mlp.up_proj
- model.layers.48.mlp.up_proj
- model.layers.49.mlp.up_proj
- model.layers.24.mlp.up_proj
- model.layers.47.mlp.up_proj
- model.layers.25.mlp.up_proj
- model.layers.23.mlp.up_proj
- model.layers.50.mlp.up_proj
- model.layers.14.mlp.up_proj
- model.layers.46.mlp.up_proj
- model.layers.26.mlp.up_proj
- model.layers.27.mlp.up_proj
- model.layers.20.mlp.up_proj
- model.layers.13.mlp.up_proj
- model.layers.51.mlp.up_proj
- model.layers.28.mlp.up_proj
- model.layers.45.mlp.up_proj
- model.layers.22.mlp.up_proj
- model.layers.52.mlp.up_proj
- model.layers.12.mlp.up_proj
- model.layers.29.mlp.up_proj
- model.layers.44.mlp.up_proj
- model.layers.53.mlp.up_proj
- model.layers.11.mlp.up_proj
- model.layers.42.mlp.up_proj
- model.layers.30.mlp.up_proj
- model.layers.43.mlp.up_proj
- model.layers.19.mlp.up_proj
- model.layers.54.mlp.up_proj
- model.layers.40.mlp.up_proj
- model.embed_tokens
# model.norm layers
# post_attention_layernorm layers
# - model.layers.0.post_attention_layernorm
# - model.layers.1.post_attention_layernorm
# - model.layers.2.post_attention_layernorm
# - model.layers.3.post_attention_layernorm
# - model.layers.4.post_attention_layernorm
# - model.layers.5.post_attention_layernorm
# - model.layers.6.post_attention_layernorm
# - model.layers.7.post_attention_layernorm
# - model.layers.8.post_attention_layernorm
# - model.layers.9.post_attention_layernorm
# - model.layers.10.post_attention_layernorm
# - model.layers.11.post_attention_layernorm
# - model.layers.12.post_attention_layernorm
# - model.layers.13.post_attention_layernorm
# - model.layers.14.post_attention_layernorm
# - model.layers.15.post_attention_layernorm
# - model.layers.16.post_attention_layernorm
# - model.layers.17.post_attention_layernorm
# - model.layers.18.post_attention_layernorm
# - model.layers.19.post_attention_layernorm
# - model.layers.20.post_attention_layernorm
# - model.layers.21.post_attention_layernorm
# - model.layers.22.post_attention_layernorm
# - model.layers.23.post_attention_layernorm
# - model.layers.24.post_attention_layernorm
# - model.layers.25.post_attention_layernorm
# - model.layers.26.post_attention_layernorm
# - model.layers.27.post_attention_layernorm
# - model.layers.28.post_attention_layernorm
# - model.layers.29.post_attention_layernorm
# self_attn.k_proj layers
- model.layers.55.self_attn.k_proj
- model.layers.51.self_attn.k_proj
- model.layers.53.self_attn.k_proj
- model.layers.56.self_attn.k_proj
- model.layers.54.self_attn.k_proj
- model.layers.57.self_attn.k_proj
- model.layers.52.self_attn.k_proj
- model.layers.59.self_attn.k_proj
- model.layers.49.self_attn.k_proj
- model.layers.48.self_attn.k_proj
- model.layers.47.self_attn.k_proj
- model.layers.41.self_attn.k_proj
- model.layers.58.self_attn.k_proj
- model.layers.40.self_attn.k_proj
- model.layers.46.self_attn.k_proj
- model.layers.44.self_attn.k_proj
- model.layers.50.self_attn.k_proj
- model.layers.43.self_attn.k_proj
- model.layers.39.self_attn.k_proj
- model.layers.42.self_attn.k_proj
- model.layers.45.self_attn.k_proj
- model.layers.33.self_attn.k_proj
- model.layers.37.self_attn.k_proj
- model.layers.17.self_attn.k_proj
- model.layers.24.self_attn.k_proj
- model.layers.21.self_attn.k_proj
- model.layers.25.self_attn.k_proj
- model.layers.23.self_attn.k_proj
- model.layers.35.self_attn.k_proj
- model.layers.20.self_attn.k_proj
# self_attn.o_proj layers
- model.layers.53.self_attn.o_proj
- model.layers.55.self_attn.o_proj
- model.layers.54.self_attn.o_proj
- model.layers.42.self_attn.o_proj
- model.layers.52.self_attn.o_proj
- model.layers.51.self_attn.o_proj
- model.layers.50.self_attn.o_proj
- model.layers.1.self_attn.o_proj
- model.layers.40.self_attn.o_proj
- model.layers.37.self_attn.o_proj
- model.layers.34.self_attn.o_proj
- model.layers.36.self_attn.o_proj
- model.layers.41.self_attn.o_proj
- model.layers.35.self_attn.o_proj
- model.layers.46.self_attn.o_proj
- model.layers.27.self_attn.o_proj
- model.layers.33.self_attn.o_proj
- model.layers.30.self_attn.o_proj
- model.layers.43.self_attn.o_proj
- model.layers.39.self_attn.o_proj
- model.layers.17.self_attn.o_proj
- model.layers.28.self_attn.o_proj
- model.layers.48.self_attn.o_proj
- model.layers.31.self_attn.o_proj
- model.layers.29.self_attn.o_proj
- model.layers.38.self_attn.o_proj
- model.layers.47.self_attn.o_proj
- model.layers.56.self_attn.o_proj
- model.layers.32.self_attn.o_proj
- model.layers.4.self_attn.o_proj
# self_attn.q_proj layers
- model.layers.1.self_attn.q_proj
- model.layers.3.self_attn.q_proj
- model.layers.4.self_attn.q_proj
- model.layers.5.self_attn.q_proj
- model.layers.2.self_attn.q_proj
- model.layers.0.self_attn.q_proj
- model.layers.6.self_attn.q_proj
- model.layers.8.self_attn.q_proj
- model.layers.7.self_attn.q_proj
- model.layers.10.self_attn.q_proj
- model.layers.36.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.9.self_attn.q_proj
- model.layers.35.self_attn.q_proj
- model.layers.28.self_attn.q_proj
- model.layers.34.self_attn.q_proj
- model.layers.27.self_attn.q_proj
- model.layers.14.self_attn.q_proj
- model.layers.29.self_attn.q_proj
- model.layers.12.self_attn.q_proj
- model.layers.33.self_attn.q_proj
- model.layers.30.self_attn.q_proj
- model.layers.24.self_attn.q_proj
- model.layers.32.self_attn.q_proj
- model.layers.37.self_attn.q_proj
- model.layers.20.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.16.self_attn.q_proj
- model.layers.26.self_attn.q_proj
- model.layers.31.self_attn.q_proj
# self_attn.v_proj layers
- model.layers.7.self_attn.v_proj
- model.layers.8.self_attn.v_proj
- model.layers.9.self_attn.v_proj
- model.layers.10.self_attn.v_proj
- model.layers.12.self_attn.v_proj
- model.layers.13.self_attn.v_proj
- model.layers.14.self_attn.v_proj
- model.layers.15.self_attn.v_proj
- model.layers.16.self_attn.v_proj
- model.layers.17.self_attn.v_proj
- model.layers.21.self_attn.v_proj
- model.layers.23.self_attn.v_proj
- model.layers.39.self_attn.v_proj
- model.layers.46.self_attn.v_proj
- model.layers.48.self_attn.v_proj
- model.layers.49.self_attn.v_proj
- model.layers.51.self_attn.v_proj
- model.layers.52.self_attn.v_proj
- model.layers.53.self_attn.v_proj
- model.layers.54.self_attn.v_proj
- model.layers.55.self_attn.v_proj
- model.layers.56.self_attn.v_proj
- model.layers.22.self_attn.v_proj
- model.layers.18.self_attn.v_proj
- model.layers.50.self_attn.v_proj
- model.layers.47.self_attn.v_proj
- model.layers.44.self_attn.v_proj
- model.layers.45.self_attn.v_proj
- model.layers.57.self_attn.v_proj
- model.layers.41.self_attn.v_proj
```
</details><br>
# out-yi
This model is a fine-tuned version of [01-ai/Yi-1.5-34B](https://huggingface.co/01-ai/Yi-1.5-34B-32k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4425
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6265 | 0.0 | 1 | 0.6035 |
| 0.4674 | 0.25 | 327 | 0.4344 |
| 0.4337 | 0.5 | 654 | 0.4250 |
| 0.4346 | 0.75 | 981 | 0.4179 |
| 0.3985 | 1.0 | 1308 | 0.4118 |
| 0.3128 | 1.23 | 1635 | 0.4201 |
| 0.3261 | 1.48 | 1962 | 0.4157 |
| 0.3259 | 1.73 | 2289 | 0.4122 |
| 0.3126 | 1.98 | 2616 | 0.4079 |
| 0.2265 | 2.21 | 2943 | 0.4441 |
| 0.2297 | 2.46 | 3270 | 0.4427 |
| 0.2424 | 2.71 | 3597 | 0.4425 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0 |
SUMEDH91/my_awesome_wikitext-model | SUMEDH91 | "2024-01-05T06:11:35Z" | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-01-04T17:54:25Z" | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_awesome_wikitext-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_wikitext-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2915
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4882 | 1.0 | 5441 | 1.3856 |
| 1.3941 | 2.0 | 10882 | 1.3115 |
| 1.3617 | 3.0 | 16323 | 1.2915 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
shaoyuyoung/QTC4SO | shaoyuyoung | "2023-03-11T04:06:57Z" | 103 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-12-09T03:22:14Z" | ---
license: mit
---
# Introduction
QTC4SO(Qusetion Title Completion for Stack Overflow) is a pre-trained model based on T5. We fine-tuned it on our downstream task. It is used for question title completion on StackOverflow
# More details
You can find our code and dataset on our [GitHub project](https://github.com/shaoyuyoung/QTC4SO)<br>
For more details, please refer to [our paper](https://smartse.github.io/paper/icpc2023.pdf) |
ray1916/ray__epoch_2 | ray1916 | "2024-11-22T02:07:38Z" | 30 | 0 | transformers | [
"transformers",
"safetensors",
"florence2",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-11-22T01:44:56Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PrunaAI/beomi-Llama-3-Open-Ko-8B-bnb-4bit-smashed | PrunaAI | "2024-08-02T15:58:01Z" | 11 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:beomi/Llama-3-Open-Ko-8B",
"base_model:quantized:beomi/Llama-3-Open-Ko-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-05-01T23:52:59Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: beomi/Llama-3-Open-Ko-8B
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo beomi/Llama-3-Open-Ko-8B installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/beomi-Llama-3-Open-Ko-8B-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("beomi/Llama-3-Open-Ko-8B")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model beomi/Llama-3-Open-Ko-8B before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
FabioTiroli/LaminiFT4 | FabioTiroli | "2024-09-13T11:13:26Z" | 181 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"base_model:lamini/lamini_docs_finetuned",
"base_model:finetune:lamini/lamini_docs_finetuned",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-13T01:24:35Z" | ---
library_name: transformers
license: apache-2.0
base_model: lamini/lamini_docs_finetuned
tags:
- generated_from_trainer
model-index:
- name: LaminiFT4
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. -->
# LaminiFT4
This model is a fine-tuned version of [lamini/lamini_docs_finetuned](https://huggingface.co/lamini/lamini_docs_finetuned) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 3
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cpu
- Datasets 2.21.0
- Tokenizers 0.19.1
|
CyberHarem/momoshina_fumika_alicegearaegisexpansion | CyberHarem | "2023-12-11T09:34:00Z" | 0 | 0 | null | [
"art",
"text-to-image",
"dataset:CyberHarem/momoshina_fumika_alicegearaegisexpansion",
"license:mit",
"region:us"
] | text-to-image | "2023-12-11T09:19:32Z" | ---
license: mit
datasets:
- CyberHarem/momoshina_fumika_alicegearaegisexpansion
pipeline_tag: text-to-image
tags:
- art
---
# Lora of momoshina_fumika_alicegearaegisexpansion
This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). And the auto-training framework is maintained by [DeepGHS Team](https://huggingface.co/deepghs).
The base model used during training is [NAI](https://huggingface.co/deepghs/animefull-latest), and the base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11).
After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.
For example, if you want to use the model from step 5720, you need to download `5720/momoshina_fumika_alicegearaegisexpansion.pt` as the embedding and `5720/momoshina_fumika_alicegearaegisexpansion.safetensors` for loading Lora. By using both files together, you can generate images for the desired characters.
**The best step we recommend is 5720**, with the score of 0.967. The trigger words are:
1. `momoshina_fumika_alicegearaegisexpansion`
2. `glasses, long_hair, purple_eyes, blue_hair, blush, black_hair`
For the following groups, it is not recommended to use this model and we express regret:
1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.
2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.
3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.
4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.
5. Individuals who finds the generated image content offensive to their values.
These are available steps:
| Steps | Score | Download | pattern_1 | pattern_2 | pattern_3 | pattern_4 | pattern_5 | pattern_6 | pattern_7 | pattern_8 | bikini | bondage | free | maid | miko | nude | nude2 | suit | yukata |
|:---------|:----------|:------------------------------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-----------------------------------------------|:-------------------------------------------------|:--------------------------------------------------|:-------------------------------------|:-------------------------------------|:-------------------------------------|:-----------------------------------------------|:------------------------------------------------|:-------------------------------------|:-----------------------------------------|
| 6600 | 0.900 | [Download](6600/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6600/previews/bikini.png) | [<NSFW, click to see>](6600/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6600/previews/nude.png) | [<NSFW, click to see>](6600/previews/nude2.png) |  |  |
| 6160 | 0.797 | [Download](6160/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](6160/previews/bikini.png) | [<NSFW, click to see>](6160/previews/bondage.png) |  |  |  | [<NSFW, click to see>](6160/previews/nude.png) | [<NSFW, click to see>](6160/previews/nude2.png) |  |  |
| **5720** | **0.967** | [**Download**](5720/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5720/previews/bikini.png) | [<NSFW, click to see>](5720/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5720/previews/nude.png) | [<NSFW, click to see>](5720/previews/nude2.png) |  |  |
| 5280 | 0.927 | [Download](5280/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](5280/previews/bikini.png) | [<NSFW, click to see>](5280/previews/bondage.png) |  |  |  | [<NSFW, click to see>](5280/previews/nude.png) | [<NSFW, click to see>](5280/previews/nude2.png) |  |  |
| 4840 | 0.887 | [Download](4840/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4840/previews/bikini.png) | [<NSFW, click to see>](4840/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4840/previews/nude.png) | [<NSFW, click to see>](4840/previews/nude2.png) |  |  |
| 4400 | 0.882 | [Download](4400/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](4400/previews/bikini.png) | [<NSFW, click to see>](4400/previews/bondage.png) |  |  |  | [<NSFW, click to see>](4400/previews/nude.png) | [<NSFW, click to see>](4400/previews/nude2.png) |  |  |
| 3960 | 0.881 | [Download](3960/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3960/previews/bikini.png) | [<NSFW, click to see>](3960/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3960/previews/nude.png) | [<NSFW, click to see>](3960/previews/nude2.png) |  |  |
| 3520 | 0.954 | [Download](3520/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3520/previews/bikini.png) | [<NSFW, click to see>](3520/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3520/previews/nude.png) | [<NSFW, click to see>](3520/previews/nude2.png) |  |  |
| 3080 | 0.931 | [Download](3080/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](3080/previews/bikini.png) | [<NSFW, click to see>](3080/previews/bondage.png) |  |  |  | [<NSFW, click to see>](3080/previews/nude.png) | [<NSFW, click to see>](3080/previews/nude2.png) |  |  |
| 2640 | 0.948 | [Download](2640/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2640/previews/bikini.png) | [<NSFW, click to see>](2640/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2640/previews/nude.png) | [<NSFW, click to see>](2640/previews/nude2.png) |  |  |
| 2200 | 0.815 | [Download](2200/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](2200/previews/bikini.png) | [<NSFW, click to see>](2200/previews/bondage.png) |  |  |  | [<NSFW, click to see>](2200/previews/nude.png) | [<NSFW, click to see>](2200/previews/nude2.png) |  |  |
| 1760 | 0.923 | [Download](1760/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1760/previews/bikini.png) | [<NSFW, click to see>](1760/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1760/previews/nude.png) | [<NSFW, click to see>](1760/previews/nude2.png) |  |  |
| 1320 | 0.892 | [Download](1320/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](1320/previews/bikini.png) | [<NSFW, click to see>](1320/previews/bondage.png) |  |  |  | [<NSFW, click to see>](1320/previews/nude.png) | [<NSFW, click to see>](1320/previews/nude2.png) |  |  |
| 880 | 0.776 | [Download](880/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](880/previews/bikini.png) | [<NSFW, click to see>](880/previews/bondage.png) |  |  |  | [<NSFW, click to see>](880/previews/nude.png) | [<NSFW, click to see>](880/previews/nude2.png) |  |  |
| 440 | 0.542 | [Download](440/momoshina_fumika_alicegearaegisexpansion.zip) |  |  |  |  |  |  |  |  | [<NSFW, click to see>](440/previews/bikini.png) | [<NSFW, click to see>](440/previews/bondage.png) |  |  |  | [<NSFW, click to see>](440/previews/nude.png) | [<NSFW, click to see>](440/previews/nude2.png) |  |  |
|
motmono/q-Taxi-v3 | motmono | "2022-10-30T19:40:19Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2022-10-30T19:40:11Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="motmono/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
varun-v-rao/bert-large-cased-lora-1.58M-snli-model1 | varun-v-rao | "2024-02-06T06:57:05Z" | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-large-cased",
"base_model:finetune:google-bert/bert-large-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-02-06T03:12:15Z" | ---
license: apache-2.0
base_model: bert-large-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-large-cased-lora-1.58M-snli-model1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-lora-1.58M-snli-model1
This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8126
- Accuracy: 0.695
## 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: 256
- eval_batch_size: 256
- seed: 57
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5065 | 1.0 | 2146 | 0.4147 | 0.8480 |
| 0.4613 | 2.0 | 4292 | 0.3828 | 0.8588 |
| 0.4464 | 3.0 | 6438 | 0.3717 | 0.8629 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Jonjew/JulianneMoore | Jonjew | "2025-03-07T04:10:12Z" | 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 | "2025-03-07T04:09:36Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
Breathtaking over the shoulder shot photography of ohwx looking at viewer,
imperfections, necklace, looking at viewer, eyelashes, fine hair detail,
entire hairstyle visible, perfect eyes with iris pattern, sensual lips,
nose, (perfectly sharp:1.3), realistic textures, (deep focus:1.5), 8k uhd,
dslr, ultra high quality image, film grain, Fujifilm XT3
parameters:
negative_prompt: JulianneMoore_flux_lora_v1_Weight-1.00
output:
url: >-
images/JulianneMoore_flux_lora_v1_Weight-1.00_2025-02-21_2025-02-21-202736_0.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: ohwx
license: unknown
---
# Julianne Moore (Flux)
<Gallery />
## Model description
FROM https://civitai.com/models/1281677/julianne-moore-flux
Trigger ohwx
Strength 1
Guidance: 2.2-3
Steps (dev): 30-40
👍 *** If you love it, like it! ***👍
workflow: https://civitai.com/models/1088678
👑 Julianne Moore 🎬
About my celebrities loras
90% of the dataset used to build my loras only use head images. That really help the blend with other lora or model as there is no hands, feet, that may or will interfere in the final image render. When you get distorted hands with a person lora, it's because there is info on hands in the dataset used to train the lora, but that will not happen with my loras.
I've trained on Flux.1 Dev so other merged or trained checkpoint may not work well with my loras.
The drawback side of that is that the body may not be reflecting the reality. It may not be a drawback tho.
This is a lora for Flux.1 Dev. Work with other model but you must drop some simple bloc (good start 19-32).
Trained with ai-toolkit, so merging it is not easy.
To get the best result
Guidance: 2.2-3
Steps (dev): 30-40
daemon detailer (lying sigma sampler): factor: -0.02, start 0.06, end 0.75
Resolution: Upscale the latent by 1.25 or 1.5 you'll get awsome result. (take longer time but worth it)
Trigger word is (may work better in certain context): ohwx
Enjoy!
## Trigger words
You should use `ohwx` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/JulianneMoore/tree/main) them in the Files & versions tab.
|
rogramss/whisper-tiny_to_british_accent_ae_further_analysis | rogramss | "2025-03-19T18:01:45Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:british_english_AE_fa",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2025-03-19T17:05:25Z" | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- british_english_AE_fa
model-index:
- name: Whisper tiny Chinese
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper tiny British
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the British English 'AE' Phonemes dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.3636
- eval_wer: 13.2631
- eval_runtime: 140.0191
- eval_samples_per_second: 3.392
- eval_steps_per_second: 3.392
- epoch: 2.5
- step: 1500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- 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: 2000
### Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
memevis/tryy41 | memevis | "2025-01-27T17:35:51Z" | 14 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-27T17:30:36Z" | ---
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 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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MayBashendy/ASAP_FineTuningBERT_AugV4_k25_task1_organization_fold2 | MayBashendy | "2024-11-25T05:12:32Z" | 161 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-25T04:09:52Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ASAP_FineTuningBERT_AugV4_k25_task1_organization_fold2
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. -->
# ASAP_FineTuningBERT_AugV4_k25_task1_organization_fold2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8864
- Qwk: 0.3550
- Mse: 0.8864
- Rmse: 0.9415
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0008 | 2 | 9.5211 | 0.0085 | 9.5211 | 3.0856 |
| No log | 0.0016 | 4 | 7.7638 | 0.0 | 7.7638 | 2.7864 |
| No log | 0.0024 | 6 | 7.1927 | 0.0 | 7.1927 | 2.6819 |
| No log | 0.0032 | 8 | 6.4727 | -0.0012 | 6.4727 | 2.5442 |
| No log | 0.0041 | 10 | 5.3579 | 0.0007 | 5.3579 | 2.3147 |
| No log | 0.0049 | 12 | 4.3309 | 0.0 | 4.3309 | 2.0811 |
| No log | 0.0057 | 14 | 3.4456 | 0.0078 | 3.4456 | 1.8562 |
| No log | 0.0065 | 16 | 2.8072 | 0.0144 | 2.8072 | 1.6755 |
| No log | 0.0073 | 18 | 1.7915 | 0.0449 | 1.7915 | 1.3385 |
| No log | 0.0081 | 20 | 1.3687 | 0.0213 | 1.3687 | 1.1699 |
| No log | 0.0089 | 22 | 1.0646 | 0.0213 | 1.0646 | 1.0318 |
| No log | 0.0097 | 24 | 0.8607 | 0.3235 | 0.8607 | 0.9278 |
| No log | 0.0105 | 26 | 0.7966 | 0.0910 | 0.7966 | 0.8925 |
| No log | 0.0113 | 28 | 0.8009 | 0.0648 | 0.8009 | 0.8949 |
| No log | 0.0122 | 30 | 0.8471 | 0.0648 | 0.8471 | 0.9204 |
| No log | 0.0130 | 32 | 0.8928 | 0.0325 | 0.8928 | 0.9449 |
| No log | 0.0138 | 34 | 0.9072 | 0.0164 | 0.9072 | 0.9525 |
| No log | 0.0146 | 36 | 0.9057 | 0.0 | 0.9057 | 0.9517 |
| No log | 0.0154 | 38 | 0.9030 | 0.0 | 0.9030 | 0.9503 |
| No log | 0.0162 | 40 | 0.9555 | 0.0164 | 0.9555 | 0.9775 |
| No log | 0.0170 | 42 | 1.0194 | 0.0164 | 1.0194 | 1.0096 |
| No log | 0.0178 | 44 | 1.1108 | 0.0164 | 1.1108 | 1.0539 |
| No log | 0.0186 | 46 | 1.0026 | 0.0164 | 1.0026 | 1.0013 |
| No log | 0.0195 | 48 | 0.8869 | 0.0164 | 0.8869 | 0.9417 |
| No log | 0.0203 | 50 | 0.8344 | 0.0 | 0.8344 | 0.9135 |
| No log | 0.0211 | 52 | 0.8327 | 0.0 | 0.8327 | 0.9125 |
| No log | 0.0219 | 54 | 0.8921 | 0.0 | 0.8921 | 0.9445 |
| No log | 0.0227 | 56 | 1.2001 | 0.0250 | 1.2001 | 1.0955 |
| No log | 0.0235 | 58 | 1.2927 | 0.0559 | 1.2927 | 1.1370 |
| No log | 0.0243 | 60 | 0.9422 | 0.0 | 0.9422 | 0.9707 |
| No log | 0.0251 | 62 | 0.9049 | 0.0 | 0.9049 | 0.9513 |
| No log | 0.0259 | 64 | 0.9336 | 0.0 | 0.9336 | 0.9662 |
| No log | 0.0268 | 66 | 1.0105 | 0.0 | 1.0105 | 1.0052 |
| No log | 0.0276 | 68 | 1.1159 | 0.0001 | 1.1159 | 1.0564 |
| No log | 0.0284 | 70 | 0.9173 | 0.0 | 0.9173 | 0.9578 |
| No log | 0.0292 | 72 | 0.8965 | 0.0 | 0.8965 | 0.9468 |
| No log | 0.0300 | 74 | 0.9306 | 0.0 | 0.9306 | 0.9647 |
| No log | 0.0308 | 76 | 0.9083 | 0.0 | 0.9083 | 0.9530 |
| No log | 0.0316 | 78 | 0.9239 | 0.0075 | 0.9239 | 0.9612 |
| No log | 0.0324 | 80 | 1.0984 | 0.1563 | 1.0984 | 1.0481 |
| No log | 0.0332 | 82 | 1.0258 | 0.1584 | 1.0258 | 1.0128 |
| No log | 0.0340 | 84 | 0.8683 | 0.1654 | 0.8683 | 0.9319 |
| No log | 0.0349 | 86 | 0.8879 | 0.1509 | 0.8879 | 0.9423 |
| No log | 0.0357 | 88 | 1.0538 | 0.1724 | 1.0538 | 1.0265 |
| No log | 0.0365 | 90 | 1.2321 | 0.1690 | 1.2321 | 1.1100 |
| No log | 0.0373 | 92 | 0.9262 | 0.1549 | 0.9262 | 0.9624 |
| No log | 0.0381 | 94 | 0.8781 | 0.1837 | 0.8781 | 0.9371 |
| No log | 0.0389 | 96 | 1.0370 | 0.1922 | 1.0370 | 1.0183 |
| No log | 0.0397 | 98 | 1.3008 | 0.1475 | 1.3008 | 1.1405 |
| No log | 0.0405 | 100 | 1.4885 | 0.1036 | 1.4885 | 1.2200 |
| No log | 0.0413 | 102 | 1.1735 | 0.2053 | 1.1735 | 1.0833 |
| No log | 0.0422 | 104 | 0.8042 | 0.0802 | 0.8042 | 0.8968 |
| No log | 0.0430 | 106 | 0.7891 | 0.0852 | 0.7891 | 0.8883 |
| No log | 0.0438 | 108 | 0.7999 | 0.0296 | 0.7999 | 0.8944 |
| No log | 0.0446 | 110 | 0.9821 | 0.0671 | 0.9821 | 0.9910 |
| No log | 0.0454 | 112 | 1.3540 | 0.1995 | 1.3540 | 1.1636 |
| No log | 0.0462 | 114 | 1.4034 | 0.1837 | 1.4034 | 1.1847 |
| No log | 0.0470 | 116 | 1.1613 | 0.1814 | 1.1613 | 1.0776 |
| No log | 0.0478 | 118 | 0.8645 | 0.0406 | 0.8645 | 0.9298 |
| No log | 0.0486 | 120 | 0.8526 | 0.0684 | 0.8526 | 0.9234 |
| No log | 0.0495 | 122 | 1.1374 | 0.1793 | 1.1374 | 1.0665 |
| No log | 0.0503 | 124 | 1.4875 | 0.1797 | 1.4875 | 1.2196 |
| No log | 0.0511 | 126 | 1.5333 | 0.1617 | 1.5333 | 1.2383 |
| No log | 0.0519 | 128 | 1.4762 | 0.1496 | 1.4762 | 1.2150 |
| No log | 0.0527 | 130 | 1.3425 | 0.1505 | 1.3425 | 1.1586 |
| No log | 0.0535 | 132 | 1.4790 | 0.1362 | 1.4790 | 1.2162 |
| No log | 0.0543 | 134 | 1.6983 | 0.0960 | 1.6983 | 1.3032 |
| No log | 0.0551 | 136 | 1.6694 | 0.1184 | 1.6694 | 1.2920 |
| No log | 0.0559 | 138 | 1.3125 | 0.1790 | 1.3125 | 1.1456 |
| No log | 0.0567 | 140 | 0.9324 | 0.2274 | 0.9324 | 0.9656 |
| No log | 0.0576 | 142 | 0.9334 | 0.2387 | 0.9334 | 0.9661 |
| No log | 0.0584 | 144 | 1.2946 | 0.1722 | 1.2946 | 1.1378 |
| No log | 0.0592 | 146 | 1.5237 | 0.1302 | 1.5237 | 1.2344 |
| No log | 0.0600 | 148 | 1.2725 | 0.2041 | 1.2725 | 1.1281 |
| No log | 0.0608 | 150 | 1.0451 | 0.2604 | 1.0451 | 1.0223 |
| No log | 0.0616 | 152 | 0.9733 | 0.2814 | 0.9733 | 0.9865 |
| No log | 0.0624 | 154 | 0.8388 | 0.3151 | 0.8388 | 0.9159 |
| No log | 0.0632 | 156 | 0.9445 | 0.2922 | 0.9445 | 0.9718 |
| No log | 0.0640 | 158 | 1.2082 | 0.2450 | 1.2082 | 1.0992 |
| No log | 0.0649 | 160 | 1.2216 | 0.2540 | 1.2216 | 1.1053 |
| No log | 0.0657 | 162 | 0.8965 | 0.2518 | 0.8965 | 0.9468 |
| No log | 0.0665 | 164 | 0.7406 | 0.1494 | 0.7406 | 0.8606 |
| No log | 0.0673 | 166 | 0.7469 | 0.1497 | 0.7469 | 0.8643 |
| No log | 0.0681 | 168 | 0.7615 | 0.2034 | 0.7615 | 0.8726 |
| No log | 0.0689 | 170 | 0.9080 | 0.2664 | 0.9080 | 0.9529 |
| No log | 0.0697 | 172 | 1.3884 | 0.2180 | 1.3884 | 1.1783 |
| No log | 0.0705 | 174 | 1.2009 | 0.2437 | 1.2009 | 1.0958 |
| No log | 0.0713 | 176 | 0.9239 | 0.2802 | 0.9239 | 0.9612 |
| No log | 0.0722 | 178 | 1.0926 | 0.2625 | 1.0926 | 1.0453 |
| No log | 0.0730 | 180 | 1.4207 | 0.2061 | 1.4207 | 1.1919 |
| No log | 0.0738 | 182 | 1.1721 | 0.2385 | 1.1721 | 1.0827 |
| No log | 0.0746 | 184 | 1.1990 | 0.2205 | 1.1990 | 1.0950 |
| No log | 0.0754 | 186 | 1.0254 | 0.2137 | 1.0254 | 1.0126 |
| No log | 0.0762 | 188 | 0.8561 | 0.1982 | 0.8561 | 0.9253 |
| No log | 0.0770 | 190 | 0.9205 | 0.2474 | 0.9205 | 0.9594 |
| No log | 0.0778 | 192 | 1.1717 | 0.1869 | 1.1717 | 1.0824 |
| No log | 0.0786 | 194 | 1.4316 | 0.1669 | 1.4316 | 1.1965 |
| No log | 0.0794 | 196 | 1.1419 | 0.1964 | 1.1419 | 1.0686 |
| No log | 0.0803 | 198 | 0.8386 | 0.2808 | 0.8386 | 0.9157 |
| No log | 0.0811 | 200 | 0.8245 | 0.2511 | 0.8245 | 0.9080 |
| No log | 0.0819 | 202 | 0.9247 | 0.2598 | 0.9247 | 0.9616 |
| No log | 0.0827 | 204 | 0.8609 | 0.3103 | 0.8609 | 0.9279 |
| No log | 0.0835 | 206 | 0.9117 | 0.3048 | 0.9117 | 0.9548 |
| No log | 0.0843 | 208 | 1.1003 | 0.2614 | 1.1003 | 1.0490 |
| No log | 0.0851 | 210 | 0.8235 | 0.3402 | 0.8235 | 0.9075 |
| No log | 0.0859 | 212 | 0.7206 | 0.2885 | 0.7206 | 0.8489 |
| No log | 0.0867 | 214 | 0.7273 | 0.2624 | 0.7273 | 0.8528 |
| No log | 0.0876 | 216 | 0.7721 | 0.2794 | 0.7721 | 0.8787 |
| No log | 0.0884 | 218 | 0.7771 | 0.2494 | 0.7771 | 0.8815 |
| No log | 0.0892 | 220 | 0.7609 | 0.2048 | 0.7609 | 0.8723 |
| No log | 0.0900 | 222 | 0.7760 | 0.1930 | 0.7760 | 0.8809 |
| No log | 0.0908 | 224 | 0.8475 | 0.1722 | 0.8475 | 0.9206 |
| No log | 0.0916 | 226 | 0.9025 | 0.1739 | 0.9025 | 0.9500 |
| No log | 0.0924 | 228 | 0.8373 | 0.2369 | 0.8373 | 0.9150 |
| No log | 0.0932 | 230 | 0.8519 | 0.2628 | 0.8519 | 0.9230 |
| No log | 0.0940 | 232 | 0.9719 | 0.2743 | 0.9719 | 0.9858 |
| No log | 0.0949 | 234 | 1.0164 | 0.2726 | 1.0164 | 1.0082 |
| No log | 0.0957 | 236 | 0.8096 | 0.2940 | 0.8096 | 0.8998 |
| No log | 0.0965 | 238 | 0.8164 | 0.2987 | 0.8164 | 0.9036 |
| No log | 0.0973 | 240 | 1.1212 | 0.2574 | 1.1212 | 1.0589 |
| No log | 0.0981 | 242 | 1.2868 | 0.2254 | 1.2868 | 1.1344 |
| No log | 0.0989 | 244 | 0.9503 | 0.2740 | 0.9503 | 0.9749 |
| No log | 0.0997 | 246 | 0.8232 | 0.1960 | 0.8232 | 0.9073 |
| No log | 0.1005 | 248 | 0.8321 | 0.1973 | 0.8321 | 0.9122 |
| No log | 0.1013 | 250 | 0.9043 | 0.2290 | 0.9043 | 0.9509 |
| No log | 0.1021 | 252 | 1.2968 | 0.1984 | 1.2968 | 1.1388 |
| No log | 0.1030 | 254 | 1.2826 | 0.1930 | 1.2826 | 1.1325 |
| No log | 0.1038 | 256 | 0.9921 | 0.2212 | 0.9921 | 0.9961 |
| No log | 0.1046 | 258 | 0.8900 | 0.1663 | 0.8900 | 0.9434 |
| No log | 0.1054 | 260 | 0.9361 | 0.1714 | 0.9361 | 0.9675 |
| No log | 0.1062 | 262 | 1.1213 | 0.2056 | 1.1213 | 1.0589 |
| No log | 0.1070 | 264 | 1.2654 | 0.1960 | 1.2654 | 1.1249 |
| No log | 0.1078 | 266 | 1.1232 | 0.2289 | 1.1232 | 1.0598 |
| No log | 0.1086 | 268 | 0.9849 | 0.2136 | 0.9849 | 0.9924 |
| No log | 0.1094 | 270 | 1.1004 | 0.2157 | 1.1004 | 1.0490 |
| No log | 0.1103 | 272 | 1.3683 | 0.1988 | 1.3683 | 1.1697 |
| No log | 0.1111 | 274 | 1.2697 | 0.1941 | 1.2697 | 1.1268 |
| No log | 0.1119 | 276 | 1.1292 | 0.2055 | 1.1292 | 1.0626 |
| No log | 0.1127 | 278 | 1.0772 | 0.1996 | 1.0772 | 1.0379 |
| No log | 0.1135 | 280 | 1.1942 | 0.1848 | 1.1942 | 1.0928 |
| No log | 0.1143 | 282 | 1.3719 | 0.1531 | 1.3719 | 1.1713 |
| No log | 0.1151 | 284 | 1.0921 | 0.1868 | 1.0921 | 1.0450 |
| No log | 0.1159 | 286 | 1.1128 | 0.1879 | 1.1128 | 1.0549 |
| No log | 0.1167 | 288 | 1.3590 | 0.1511 | 1.3590 | 1.1657 |
| No log | 0.1176 | 290 | 1.4723 | 0.1514 | 1.4723 | 1.2134 |
| No log | 0.1184 | 292 | 1.4840 | 0.1467 | 1.4840 | 1.2182 |
| No log | 0.1192 | 294 | 1.2196 | 0.1978 | 1.2196 | 1.1043 |
| No log | 0.1200 | 296 | 0.9451 | 0.1739 | 0.9451 | 0.9721 |
| No log | 0.1208 | 298 | 0.9789 | 0.1786 | 0.9789 | 0.9894 |
| No log | 0.1216 | 300 | 1.0237 | 0.2253 | 1.0237 | 1.0118 |
| No log | 0.1224 | 302 | 1.2818 | 0.2214 | 1.2818 | 1.1322 |
| No log | 0.1232 | 304 | 1.4200 | 0.2055 | 1.4200 | 1.1917 |
| No log | 0.1240 | 306 | 1.0439 | 0.2383 | 1.0439 | 1.0217 |
| No log | 0.1248 | 308 | 0.8017 | 0.2183 | 0.8017 | 0.8954 |
| No log | 0.1257 | 310 | 0.8045 | 0.2286 | 0.8045 | 0.8969 |
| No log | 0.1265 | 312 | 1.0384 | 0.2339 | 1.0384 | 1.0190 |
| No log | 0.1273 | 314 | 1.3067 | 0.2233 | 1.3067 | 1.1431 |
| No log | 0.1281 | 316 | 1.0476 | 0.2413 | 1.0476 | 1.0235 |
| No log | 0.1289 | 318 | 0.8234 | 0.2037 | 0.8234 | 0.9074 |
| No log | 0.1297 | 320 | 0.7986 | 0.2109 | 0.7986 | 0.8937 |
| No log | 0.1305 | 322 | 0.9317 | 0.2364 | 0.9317 | 0.9653 |
| No log | 0.1313 | 324 | 1.1979 | 0.2197 | 1.1979 | 1.0945 |
| No log | 0.1321 | 326 | 1.0247 | 0.2571 | 1.0247 | 1.0123 |
| No log | 0.1330 | 328 | 0.7914 | 0.2415 | 0.7914 | 0.8896 |
| No log | 0.1338 | 330 | 0.7712 | 0.1931 | 0.7712 | 0.8782 |
| No log | 0.1346 | 332 | 0.8331 | 0.2360 | 0.8331 | 0.9128 |
| No log | 0.1354 | 334 | 0.9862 | 0.2632 | 0.9862 | 0.9931 |
| No log | 0.1362 | 336 | 1.1504 | 0.2135 | 1.1504 | 1.0726 |
| No log | 0.1370 | 338 | 0.9062 | 0.2811 | 0.9062 | 0.9520 |
| No log | 0.1378 | 340 | 0.8195 | 0.2506 | 0.8195 | 0.9052 |
| No log | 0.1386 | 342 | 0.8229 | 0.2625 | 0.8229 | 0.9071 |
| No log | 0.1394 | 344 | 0.9846 | 0.3087 | 0.9846 | 0.9923 |
| No log | 0.1403 | 346 | 0.9594 | 0.2868 | 0.9594 | 0.9795 |
| No log | 0.1411 | 348 | 0.7996 | 0.2404 | 0.7996 | 0.8942 |
| No log | 0.1419 | 350 | 0.7957 | 0.2499 | 0.7957 | 0.8920 |
| No log | 0.1427 | 352 | 0.8933 | 0.2527 | 0.8933 | 0.9452 |
| No log | 0.1435 | 354 | 0.8482 | 0.2634 | 0.8482 | 0.9210 |
| No log | 0.1443 | 356 | 0.7788 | 0.2659 | 0.7788 | 0.8825 |
| No log | 0.1451 | 358 | 0.7845 | 0.2428 | 0.7845 | 0.8857 |
| No log | 0.1459 | 360 | 0.7883 | 0.2731 | 0.7883 | 0.8879 |
| No log | 0.1467 | 362 | 0.8310 | 0.2785 | 0.8310 | 0.9116 |
| No log | 0.1475 | 364 | 0.8319 | 0.2857 | 0.8319 | 0.9121 |
| No log | 0.1484 | 366 | 0.7908 | 0.2593 | 0.7908 | 0.8893 |
| No log | 0.1492 | 368 | 0.8194 | 0.2318 | 0.8194 | 0.9052 |
| No log | 0.1500 | 370 | 0.8003 | 0.2160 | 0.8003 | 0.8946 |
| No log | 0.1508 | 372 | 0.8383 | 0.2854 | 0.8383 | 0.9156 |
| No log | 0.1516 | 374 | 0.8361 | 0.2787 | 0.8361 | 0.9144 |
| No log | 0.1524 | 376 | 0.7749 | 0.2542 | 0.7749 | 0.8803 |
| No log | 0.1532 | 378 | 0.8254 | 0.2040 | 0.8254 | 0.9085 |
| No log | 0.1540 | 380 | 0.8412 | 0.2267 | 0.8412 | 0.9172 |
| No log | 0.1548 | 382 | 0.7438 | 0.2968 | 0.7438 | 0.8624 |
| No log | 0.1557 | 384 | 0.7877 | 0.3415 | 0.7877 | 0.8875 |
| No log | 0.1565 | 386 | 0.8279 | 0.3333 | 0.8279 | 0.9099 |
| No log | 0.1573 | 388 | 0.7186 | 0.3515 | 0.7186 | 0.8477 |
| No log | 0.1581 | 390 | 0.7607 | 0.2778 | 0.7607 | 0.8722 |
| No log | 0.1589 | 392 | 0.7769 | 0.2683 | 0.7769 | 0.8814 |
| No log | 0.1597 | 394 | 0.7069 | 0.3299 | 0.7069 | 0.8408 |
| No log | 0.1605 | 396 | 0.7091 | 0.3601 | 0.7091 | 0.8421 |
| No log | 0.1613 | 398 | 0.7039 | 0.3409 | 0.7039 | 0.8390 |
| No log | 0.1621 | 400 | 0.7179 | 0.3585 | 0.7179 | 0.8473 |
| No log | 0.1630 | 402 | 0.7321 | 0.3374 | 0.7321 | 0.8557 |
| No log | 0.1638 | 404 | 0.7686 | 0.3583 | 0.7686 | 0.8767 |
| No log | 0.1646 | 406 | 0.7637 | 0.3413 | 0.7637 | 0.8739 |
| No log | 0.1654 | 408 | 0.7411 | 0.3383 | 0.7411 | 0.8608 |
| No log | 0.1662 | 410 | 0.7352 | 0.3274 | 0.7352 | 0.8574 |
| No log | 0.1670 | 412 | 0.7548 | 0.3264 | 0.7548 | 0.8688 |
| No log | 0.1678 | 414 | 0.8822 | 0.3071 | 0.8822 | 0.9392 |
| No log | 0.1686 | 416 | 0.9175 | 0.3171 | 0.9175 | 0.9579 |
| No log | 0.1694 | 418 | 0.7634 | 0.3313 | 0.7634 | 0.8738 |
| No log | 0.1702 | 420 | 0.7216 | 0.3252 | 0.7216 | 0.8494 |
| No log | 0.1711 | 422 | 0.7332 | 0.3080 | 0.7332 | 0.8562 |
| No log | 0.1719 | 424 | 0.7549 | 0.3732 | 0.7549 | 0.8688 |
| No log | 0.1727 | 426 | 0.9698 | 0.3516 | 0.9698 | 0.9848 |
| No log | 0.1735 | 428 | 0.8549 | 0.3605 | 0.8549 | 0.9246 |
| No log | 0.1743 | 430 | 0.6854 | 0.3681 | 0.6854 | 0.8279 |
| No log | 0.1751 | 432 | 0.7212 | 0.3046 | 0.7212 | 0.8492 |
| No log | 0.1759 | 434 | 0.7133 | 0.3028 | 0.7133 | 0.8446 |
| No log | 0.1767 | 436 | 0.6673 | 0.3231 | 0.6673 | 0.8169 |
| No log | 0.1775 | 438 | 0.7067 | 0.3245 | 0.7067 | 0.8406 |
| No log | 0.1784 | 440 | 0.6928 | 0.3203 | 0.6928 | 0.8324 |
| No log | 0.1792 | 442 | 0.7009 | 0.3174 | 0.7009 | 0.8372 |
| No log | 0.1800 | 444 | 0.7195 | 0.3302 | 0.7195 | 0.8482 |
| No log | 0.1808 | 446 | 0.8087 | 0.3160 | 0.8087 | 0.8993 |
| No log | 0.1816 | 448 | 0.8538 | 0.3002 | 0.8538 | 0.9240 |
| No log | 0.1824 | 450 | 0.7595 | 0.2963 | 0.7595 | 0.8715 |
| No log | 0.1832 | 452 | 0.7829 | 0.2588 | 0.7829 | 0.8848 |
| No log | 0.1840 | 454 | 0.7573 | 0.2883 | 0.7573 | 0.8702 |
| No log | 0.1848 | 456 | 0.8051 | 0.2721 | 0.8051 | 0.8973 |
| No log | 0.1857 | 458 | 0.8523 | 0.2872 | 0.8523 | 0.9232 |
| No log | 0.1865 | 460 | 0.7410 | 0.3264 | 0.7410 | 0.8608 |
| No log | 0.1873 | 462 | 0.7136 | 0.3443 | 0.7136 | 0.8447 |
| No log | 0.1881 | 464 | 0.7115 | 0.3424 | 0.7115 | 0.8435 |
| No log | 0.1889 | 466 | 0.7652 | 0.3633 | 0.7652 | 0.8748 |
| No log | 0.1897 | 468 | 0.7898 | 0.3585 | 0.7898 | 0.8887 |
| No log | 0.1905 | 470 | 0.7537 | 0.3257 | 0.7537 | 0.8682 |
| No log | 0.1913 | 472 | 0.7553 | 0.3522 | 0.7553 | 0.8691 |
| No log | 0.1921 | 474 | 0.8120 | 0.3425 | 0.8120 | 0.9011 |
| No log | 0.1929 | 476 | 0.7625 | 0.3299 | 0.7625 | 0.8732 |
| No log | 0.1938 | 478 | 0.6969 | 0.3343 | 0.6969 | 0.8348 |
| No log | 0.1946 | 480 | 0.7400 | 0.3047 | 0.7400 | 0.8602 |
| No log | 0.1954 | 482 | 0.7084 | 0.3224 | 0.7084 | 0.8416 |
| No log | 0.1962 | 484 | 0.6966 | 0.3188 | 0.6966 | 0.8346 |
| No log | 0.1970 | 486 | 0.7850 | 0.3462 | 0.7850 | 0.8860 |
| No log | 0.1978 | 488 | 0.7492 | 0.3324 | 0.7492 | 0.8656 |
| No log | 0.1986 | 490 | 0.7379 | 0.3049 | 0.7379 | 0.8590 |
| No log | 0.1994 | 492 | 0.7495 | 0.2910 | 0.7495 | 0.8657 |
| No log | 0.2002 | 494 | 0.7539 | 0.3325 | 0.7539 | 0.8683 |
| No log | 0.2011 | 496 | 0.7623 | 0.3659 | 0.7623 | 0.8731 |
| No log | 0.2019 | 498 | 0.6939 | 0.3530 | 0.6939 | 0.8330 |
| 0.934 | 0.2027 | 500 | 0.7732 | 0.3011 | 0.7732 | 0.8793 |
| 0.934 | 0.2035 | 502 | 0.7525 | 0.2977 | 0.7525 | 0.8674 |
| 0.934 | 0.2043 | 504 | 0.6796 | 0.3690 | 0.6796 | 0.8244 |
| 0.934 | 0.2051 | 506 | 0.7122 | 0.3229 | 0.7122 | 0.8439 |
| 0.934 | 0.2059 | 508 | 0.7012 | 0.3381 | 0.7012 | 0.8374 |
| 0.934 | 0.2067 | 510 | 0.7835 | 0.2812 | 0.7835 | 0.8851 |
| 0.934 | 0.2075 | 512 | 0.8539 | 0.2447 | 0.8539 | 0.9241 |
| 0.934 | 0.2084 | 514 | 0.7376 | 0.3324 | 0.7376 | 0.8588 |
| 0.934 | 0.2092 | 516 | 0.7424 | 0.3530 | 0.7424 | 0.8616 |
| 0.934 | 0.2100 | 518 | 0.7216 | 0.3606 | 0.7216 | 0.8495 |
| 0.934 | 0.2108 | 520 | 0.6917 | 0.3578 | 0.6917 | 0.8317 |
| 0.934 | 0.2116 | 522 | 0.6888 | 0.3626 | 0.6888 | 0.8300 |
| 0.934 | 0.2124 | 524 | 0.6776 | 0.3776 | 0.6776 | 0.8232 |
| 0.934 | 0.2132 | 526 | 0.6715 | 0.3753 | 0.6715 | 0.8194 |
| 0.934 | 0.2140 | 528 | 0.6793 | 0.3954 | 0.6793 | 0.8242 |
| 0.934 | 0.2148 | 530 | 0.7225 | 0.4331 | 0.7225 | 0.8500 |
| 0.934 | 0.2156 | 532 | 0.6937 | 0.4113 | 0.6937 | 0.8329 |
| 0.934 | 0.2165 | 534 | 0.6909 | 0.4100 | 0.6909 | 0.8312 |
| 0.934 | 0.2173 | 536 | 0.6858 | 0.4127 | 0.6858 | 0.8281 |
| 0.934 | 0.2181 | 538 | 0.6928 | 0.3865 | 0.6928 | 0.8323 |
| 0.934 | 0.2189 | 540 | 0.6935 | 0.3767 | 0.6935 | 0.8328 |
| 0.934 | 0.2197 | 542 | 0.7385 | 0.3379 | 0.7385 | 0.8593 |
| 0.934 | 0.2205 | 544 | 0.7458 | 0.3394 | 0.7458 | 0.8636 |
| 0.934 | 0.2213 | 546 | 0.7457 | 0.3384 | 0.7457 | 0.8635 |
| 0.934 | 0.2221 | 548 | 0.7649 | 0.3267 | 0.7649 | 0.8746 |
| 0.934 | 0.2229 | 550 | 0.7700 | 0.3611 | 0.7700 | 0.8775 |
| 0.934 | 0.2238 | 552 | 0.7702 | 0.3446 | 0.7702 | 0.8776 |
| 0.934 | 0.2246 | 554 | 0.7567 | 0.3477 | 0.7567 | 0.8699 |
| 0.934 | 0.2254 | 556 | 0.7433 | 0.3535 | 0.7433 | 0.8621 |
| 0.934 | 0.2262 | 558 | 0.8157 | 0.3443 | 0.8157 | 0.9032 |
| 0.934 | 0.2270 | 560 | 0.8270 | 0.3320 | 0.8270 | 0.9094 |
| 0.934 | 0.2278 | 562 | 0.7428 | 0.3275 | 0.7428 | 0.8618 |
| 0.934 | 0.2286 | 564 | 0.8466 | 0.2571 | 0.8466 | 0.9201 |
| 0.934 | 0.2294 | 566 | 0.8050 | 0.2791 | 0.8050 | 0.8972 |
| 0.934 | 0.2302 | 568 | 0.7260 | 0.3424 | 0.7260 | 0.8521 |
| 0.934 | 0.2310 | 570 | 0.7500 | 0.3789 | 0.7500 | 0.8660 |
| 0.934 | 0.2319 | 572 | 0.7383 | 0.4091 | 0.7383 | 0.8593 |
| 0.934 | 0.2327 | 574 | 0.6799 | 0.3774 | 0.6799 | 0.8246 |
| 0.934 | 0.2335 | 576 | 0.7321 | 0.3665 | 0.7321 | 0.8556 |
| 0.934 | 0.2343 | 578 | 0.6742 | 0.3778 | 0.6742 | 0.8211 |
| 0.934 | 0.2351 | 580 | 0.7255 | 0.4219 | 0.7255 | 0.8518 |
| 0.934 | 0.2359 | 582 | 0.8879 | 0.4102 | 0.8879 | 0.9423 |
| 0.934 | 0.2367 | 584 | 0.7819 | 0.4286 | 0.7819 | 0.8842 |
| 0.934 | 0.2375 | 586 | 0.6981 | 0.3954 | 0.6981 | 0.8355 |
| 0.934 | 0.2383 | 588 | 0.6966 | 0.3757 | 0.6966 | 0.8346 |
| 0.934 | 0.2392 | 590 | 0.7253 | 0.3962 | 0.7253 | 0.8516 |
| 0.934 | 0.2400 | 592 | 0.8609 | 0.4052 | 0.8609 | 0.9278 |
| 0.934 | 0.2408 | 594 | 0.7854 | 0.4049 | 0.7854 | 0.8862 |
| 0.934 | 0.2416 | 596 | 0.7298 | 0.3740 | 0.7298 | 0.8543 |
| 0.934 | 0.2424 | 598 | 0.7518 | 0.3440 | 0.7518 | 0.8671 |
| 0.934 | 0.2432 | 600 | 0.7397 | 0.3631 | 0.7397 | 0.8601 |
| 0.934 | 0.2440 | 602 | 0.7354 | 0.3522 | 0.7354 | 0.8576 |
| 0.934 | 0.2448 | 604 | 0.7321 | 0.3499 | 0.7321 | 0.8556 |
| 0.934 | 0.2456 | 606 | 0.7283 | 0.3426 | 0.7283 | 0.8534 |
| 0.934 | 0.2465 | 608 | 0.7193 | 0.3408 | 0.7193 | 0.8481 |
| 0.934 | 0.2473 | 610 | 0.7143 | 0.3347 | 0.7143 | 0.8451 |
| 0.934 | 0.2481 | 612 | 0.7169 | 0.3374 | 0.7169 | 0.8467 |
| 0.934 | 0.2489 | 614 | 0.7232 | 0.3311 | 0.7232 | 0.8504 |
| 0.934 | 0.2497 | 616 | 0.7712 | 0.3539 | 0.7712 | 0.8782 |
| 0.934 | 0.2505 | 618 | 0.8304 | 0.4071 | 0.8304 | 0.9113 |
| 0.934 | 0.2513 | 620 | 0.7238 | 0.3798 | 0.7238 | 0.8508 |
| 0.934 | 0.2521 | 622 | 0.6872 | 0.3748 | 0.6872 | 0.8290 |
| 0.934 | 0.2529 | 624 | 0.7574 | 0.4173 | 0.7574 | 0.8703 |
| 0.934 | 0.2537 | 626 | 0.7265 | 0.3957 | 0.7265 | 0.8523 |
| 0.934 | 0.2546 | 628 | 0.6639 | 0.3974 | 0.6639 | 0.8148 |
| 0.934 | 0.2554 | 630 | 0.6860 | 0.3997 | 0.6860 | 0.8283 |
| 0.934 | 0.2562 | 632 | 0.7070 | 0.4128 | 0.7070 | 0.8408 |
| 0.934 | 0.2570 | 634 | 0.6750 | 0.4125 | 0.6750 | 0.8216 |
| 0.934 | 0.2578 | 636 | 0.6760 | 0.4161 | 0.6760 | 0.8222 |
| 0.934 | 0.2586 | 638 | 0.7038 | 0.3801 | 0.7038 | 0.8389 |
| 0.934 | 0.2594 | 640 | 0.6844 | 0.3880 | 0.6844 | 0.8273 |
| 0.934 | 0.2602 | 642 | 0.7153 | 0.4000 | 0.7153 | 0.8457 |
| 0.934 | 0.2610 | 644 | 0.7135 | 0.3602 | 0.7135 | 0.8447 |
| 0.934 | 0.2619 | 646 | 0.7275 | 0.3557 | 0.7275 | 0.8529 |
| 0.934 | 0.2627 | 648 | 0.7135 | 0.3208 | 0.7135 | 0.8447 |
| 0.934 | 0.2635 | 650 | 0.7139 | 0.3283 | 0.7139 | 0.8449 |
| 0.934 | 0.2643 | 652 | 0.7229 | 0.3050 | 0.7229 | 0.8503 |
| 0.934 | 0.2651 | 654 | 0.7387 | 0.3294 | 0.7387 | 0.8595 |
| 0.934 | 0.2659 | 656 | 0.7159 | 0.3029 | 0.7159 | 0.8461 |
| 0.934 | 0.2667 | 658 | 0.7207 | 0.3032 | 0.7207 | 0.8489 |
| 0.934 | 0.2675 | 660 | 0.7676 | 0.3484 | 0.7676 | 0.8761 |
| 0.934 | 0.2683 | 662 | 0.9858 | 0.3364 | 0.9858 | 0.9929 |
| 0.934 | 0.2692 | 664 | 0.9152 | 0.3425 | 0.9152 | 0.9566 |
| 0.934 | 0.2700 | 666 | 0.7322 | 0.3056 | 0.7322 | 0.8557 |
| 0.934 | 0.2708 | 668 | 0.7542 | 0.3232 | 0.7542 | 0.8684 |
| 0.934 | 0.2716 | 670 | 0.7494 | 0.3128 | 0.7494 | 0.8657 |
| 0.934 | 0.2724 | 672 | 0.7823 | 0.3356 | 0.7823 | 0.8845 |
| 0.934 | 0.2732 | 674 | 0.9038 | 0.3323 | 0.9038 | 0.9507 |
| 0.934 | 0.2740 | 676 | 0.8235 | 0.3517 | 0.8235 | 0.9075 |
| 0.934 | 0.2748 | 678 | 0.7770 | 0.3484 | 0.7770 | 0.8815 |
| 0.934 | 0.2756 | 680 | 0.8314 | 0.3779 | 0.8314 | 0.9118 |
| 0.934 | 0.2764 | 682 | 0.7419 | 0.3595 | 0.7419 | 0.8614 |
| 0.934 | 0.2773 | 684 | 0.7566 | 0.3847 | 0.7566 | 0.8698 |
| 0.934 | 0.2781 | 686 | 0.7483 | 0.3807 | 0.7483 | 0.8651 |
| 0.934 | 0.2789 | 688 | 0.7094 | 0.3987 | 0.7094 | 0.8422 |
| 0.934 | 0.2797 | 690 | 0.7608 | 0.3915 | 0.7608 | 0.8722 |
| 0.934 | 0.2805 | 692 | 0.7558 | 0.3871 | 0.7558 | 0.8694 |
| 0.934 | 0.2813 | 694 | 0.6837 | 0.3781 | 0.6837 | 0.8269 |
| 0.934 | 0.2821 | 696 | 0.6994 | 0.3924 | 0.6994 | 0.8363 |
| 0.934 | 0.2829 | 698 | 0.7135 | 0.3682 | 0.7135 | 0.8447 |
| 0.934 | 0.2837 | 700 | 0.7303 | 0.3646 | 0.7303 | 0.8546 |
| 0.934 | 0.2846 | 702 | 0.9187 | 0.3878 | 0.9187 | 0.9585 |
| 0.934 | 0.2854 | 704 | 1.0355 | 0.3343 | 1.0355 | 1.0176 |
| 0.934 | 0.2862 | 706 | 0.8372 | 0.3466 | 0.8372 | 0.9150 |
| 0.934 | 0.2870 | 708 | 0.7284 | 0.3352 | 0.7284 | 0.8534 |
| 0.934 | 0.2878 | 710 | 0.7340 | 0.3405 | 0.7340 | 0.8567 |
| 0.934 | 0.2886 | 712 | 0.7525 | 0.3564 | 0.7525 | 0.8675 |
| 0.934 | 0.2894 | 714 | 0.8538 | 0.3778 | 0.8538 | 0.9240 |
| 0.934 | 0.2902 | 716 | 0.8531 | 0.3515 | 0.8531 | 0.9236 |
| 0.934 | 0.2910 | 718 | 0.7635 | 0.3579 | 0.7635 | 0.8738 |
| 0.934 | 0.2919 | 720 | 0.6918 | 0.3829 | 0.6918 | 0.8317 |
| 0.934 | 0.2927 | 722 | 0.6950 | 0.3832 | 0.6950 | 0.8337 |
| 0.934 | 0.2935 | 724 | 0.7626 | 0.3854 | 0.7626 | 0.8733 |
| 0.934 | 0.2943 | 726 | 0.7315 | 0.3791 | 0.7315 | 0.8553 |
| 0.934 | 0.2951 | 728 | 0.7178 | 0.3937 | 0.7178 | 0.8472 |
| 0.934 | 0.2959 | 730 | 0.6627 | 0.3991 | 0.6627 | 0.8141 |
| 0.934 | 0.2967 | 732 | 0.6847 | 0.4048 | 0.6847 | 0.8275 |
| 0.934 | 0.2975 | 734 | 0.6678 | 0.4100 | 0.6678 | 0.8172 |
| 0.934 | 0.2983 | 736 | 0.6926 | 0.4379 | 0.6926 | 0.8322 |
| 0.934 | 0.2991 | 738 | 0.6459 | 0.4401 | 0.6459 | 0.8037 |
| 0.934 | 0.3000 | 740 | 0.6589 | 0.4072 | 0.6589 | 0.8117 |
| 0.934 | 0.3008 | 742 | 0.6587 | 0.3983 | 0.6587 | 0.8116 |
| 0.934 | 0.3016 | 744 | 0.6703 | 0.4146 | 0.6703 | 0.8187 |
| 0.934 | 0.3024 | 746 | 0.6995 | 0.4391 | 0.6995 | 0.8364 |
| 0.934 | 0.3032 | 748 | 0.6893 | 0.4523 | 0.6893 | 0.8302 |
| 0.934 | 0.3040 | 750 | 0.6745 | 0.4088 | 0.6745 | 0.8213 |
| 0.934 | 0.3048 | 752 | 0.6554 | 0.4272 | 0.6554 | 0.8096 |
| 0.934 | 0.3056 | 754 | 0.6356 | 0.4144 | 0.6356 | 0.7973 |
| 0.934 | 0.3064 | 756 | 0.6508 | 0.4217 | 0.6508 | 0.8067 |
| 0.934 | 0.3073 | 758 | 0.6540 | 0.3948 | 0.6540 | 0.8087 |
| 0.934 | 0.3081 | 760 | 0.6684 | 0.3842 | 0.6684 | 0.8176 |
| 0.934 | 0.3089 | 762 | 0.6597 | 0.4044 | 0.6597 | 0.8122 |
| 0.934 | 0.3097 | 764 | 0.6868 | 0.4166 | 0.6868 | 0.8287 |
| 0.934 | 0.3105 | 766 | 0.7299 | 0.4065 | 0.7299 | 0.8543 |
| 0.934 | 0.3113 | 768 | 0.7622 | 0.3902 | 0.7622 | 0.8731 |
| 0.934 | 0.3121 | 770 | 0.7281 | 0.3694 | 0.7281 | 0.8533 |
| 0.934 | 0.3129 | 772 | 0.7006 | 0.3761 | 0.7006 | 0.8370 |
| 0.934 | 0.3137 | 774 | 0.7397 | 0.3228 | 0.7397 | 0.8600 |
| 0.934 | 0.3146 | 776 | 0.7176 | 0.3338 | 0.7176 | 0.8471 |
| 0.934 | 0.3154 | 778 | 0.7316 | 0.3787 | 0.7316 | 0.8553 |
| 0.934 | 0.3162 | 780 | 0.9242 | 0.3504 | 0.9242 | 0.9613 |
| 0.934 | 0.3170 | 782 | 0.8678 | 0.3709 | 0.8678 | 0.9315 |
| 0.934 | 0.3178 | 784 | 0.7681 | 0.3886 | 0.7681 | 0.8764 |
| 0.934 | 0.3186 | 786 | 0.7731 | 0.3373 | 0.7731 | 0.8793 |
| 0.934 | 0.3194 | 788 | 0.8053 | 0.3653 | 0.8053 | 0.8974 |
| 0.934 | 0.3202 | 790 | 0.8751 | 0.3961 | 0.8751 | 0.9355 |
| 0.934 | 0.3210 | 792 | 0.8788 | 0.4033 | 0.8788 | 0.9374 |
| 0.934 | 0.3218 | 794 | 0.8115 | 0.4170 | 0.8115 | 0.9008 |
| 0.934 | 0.3227 | 796 | 0.8006 | 0.4214 | 0.8006 | 0.8947 |
| 0.934 | 0.3235 | 798 | 0.7455 | 0.4489 | 0.7455 | 0.8634 |
| 0.934 | 0.3243 | 800 | 0.7228 | 0.4605 | 0.7228 | 0.8502 |
| 0.934 | 0.3251 | 802 | 0.7383 | 0.4564 | 0.7383 | 0.8592 |
| 0.934 | 0.3259 | 804 | 0.7061 | 0.4571 | 0.7061 | 0.8403 |
| 0.934 | 0.3267 | 806 | 0.7464 | 0.3885 | 0.7464 | 0.8639 |
| 0.934 | 0.3275 | 808 | 0.7697 | 0.3594 | 0.7697 | 0.8773 |
| 0.934 | 0.3283 | 810 | 0.7060 | 0.4426 | 0.7060 | 0.8403 |
| 0.934 | 0.3291 | 812 | 0.7866 | 0.4255 | 0.7866 | 0.8869 |
| 0.934 | 0.3300 | 814 | 0.7162 | 0.4573 | 0.7162 | 0.8463 |
| 0.934 | 0.3308 | 816 | 0.6738 | 0.3894 | 0.6738 | 0.8209 |
| 0.934 | 0.3316 | 818 | 0.6648 | 0.3906 | 0.6648 | 0.8153 |
| 0.934 | 0.3324 | 820 | 0.6586 | 0.4513 | 0.6586 | 0.8116 |
| 0.934 | 0.3332 | 822 | 0.6727 | 0.4380 | 0.6727 | 0.8202 |
| 0.934 | 0.3340 | 824 | 0.7372 | 0.4109 | 0.7372 | 0.8586 |
| 0.934 | 0.3348 | 826 | 0.7019 | 0.4102 | 0.7019 | 0.8378 |
| 0.934 | 0.3356 | 828 | 0.7158 | 0.4192 | 0.7158 | 0.8460 |
| 0.934 | 0.3364 | 830 | 0.7349 | 0.3643 | 0.7349 | 0.8573 |
| 0.934 | 0.3373 | 832 | 0.7396 | 0.4205 | 0.7396 | 0.8600 |
| 0.934 | 0.3381 | 834 | 0.7927 | 0.4165 | 0.7927 | 0.8903 |
| 0.934 | 0.3389 | 836 | 0.7650 | 0.4165 | 0.7650 | 0.8747 |
| 0.934 | 0.3397 | 838 | 0.7539 | 0.3790 | 0.7539 | 0.8682 |
| 0.934 | 0.3405 | 840 | 0.7319 | 0.3929 | 0.7319 | 0.8555 |
| 0.934 | 0.3413 | 842 | 0.7179 | 0.4108 | 0.7179 | 0.8473 |
| 0.934 | 0.3421 | 844 | 0.7300 | 0.3815 | 0.7300 | 0.8544 |
| 0.934 | 0.3429 | 846 | 0.7551 | 0.3745 | 0.7551 | 0.8690 |
| 0.934 | 0.3437 | 848 | 0.6887 | 0.4012 | 0.6887 | 0.8299 |
| 0.934 | 0.3445 | 850 | 0.6794 | 0.4001 | 0.6794 | 0.8243 |
| 0.934 | 0.3454 | 852 | 0.7130 | 0.3759 | 0.7130 | 0.8444 |
| 0.934 | 0.3462 | 854 | 0.7182 | 0.3759 | 0.7182 | 0.8475 |
| 0.934 | 0.3470 | 856 | 0.6991 | 0.3711 | 0.6991 | 0.8361 |
| 0.934 | 0.3478 | 858 | 0.6846 | 0.3777 | 0.6846 | 0.8274 |
| 0.934 | 0.3486 | 860 | 0.6650 | 0.4095 | 0.6650 | 0.8155 |
| 0.934 | 0.3494 | 862 | 0.7067 | 0.3623 | 0.7067 | 0.8407 |
| 0.934 | 0.3502 | 864 | 0.6873 | 0.3854 | 0.6873 | 0.8291 |
| 0.934 | 0.3510 | 866 | 0.7201 | 0.4202 | 0.7201 | 0.8486 |
| 0.934 | 0.3518 | 868 | 0.7567 | 0.3864 | 0.7567 | 0.8699 |
| 0.934 | 0.3527 | 870 | 0.6998 | 0.3632 | 0.6998 | 0.8365 |
| 0.934 | 0.3535 | 872 | 0.7986 | 0.3108 | 0.7986 | 0.8936 |
| 0.934 | 0.3543 | 874 | 0.7564 | 0.3029 | 0.7564 | 0.8697 |
| 0.934 | 0.3551 | 876 | 0.6892 | 0.3554 | 0.6892 | 0.8302 |
| 0.934 | 0.3559 | 878 | 0.7357 | 0.3721 | 0.7357 | 0.8577 |
| 0.934 | 0.3567 | 880 | 0.7102 | 0.3634 | 0.7102 | 0.8428 |
| 0.934 | 0.3575 | 882 | 0.7307 | 0.3191 | 0.7307 | 0.8548 |
| 0.934 | 0.3583 | 884 | 0.7217 | 0.3315 | 0.7217 | 0.8495 |
| 0.934 | 0.3591 | 886 | 0.7698 | 0.3531 | 0.7698 | 0.8774 |
| 0.934 | 0.3600 | 888 | 0.7576 | 0.3670 | 0.7576 | 0.8704 |
| 0.934 | 0.3608 | 890 | 0.7406 | 0.3426 | 0.7406 | 0.8606 |
| 0.934 | 0.3616 | 892 | 0.7359 | 0.3247 | 0.7359 | 0.8578 |
| 0.934 | 0.3624 | 894 | 0.6944 | 0.3896 | 0.6944 | 0.8333 |
| 0.934 | 0.3632 | 896 | 0.7006 | 0.3803 | 0.7006 | 0.8370 |
| 0.934 | 0.3640 | 898 | 0.6862 | 0.3855 | 0.6862 | 0.8284 |
| 0.934 | 0.3648 | 900 | 0.7162 | 0.4059 | 0.7162 | 0.8463 |
| 0.934 | 0.3656 | 902 | 0.7137 | 0.4027 | 0.7137 | 0.8448 |
| 0.934 | 0.3664 | 904 | 0.7440 | 0.3940 | 0.7440 | 0.8626 |
| 0.934 | 0.3672 | 906 | 0.7479 | 0.3840 | 0.7479 | 0.8648 |
| 0.934 | 0.3681 | 908 | 0.7836 | 0.3394 | 0.7836 | 0.8852 |
| 0.934 | 0.3689 | 910 | 0.7746 | 0.3592 | 0.7746 | 0.8801 |
| 0.934 | 0.3697 | 912 | 0.7737 | 0.3481 | 0.7737 | 0.8796 |
| 0.934 | 0.3705 | 914 | 0.7739 | 0.3487 | 0.7739 | 0.8797 |
| 0.934 | 0.3713 | 916 | 0.7981 | 0.3712 | 0.7981 | 0.8934 |
| 0.934 | 0.3721 | 918 | 0.7247 | 0.3931 | 0.7247 | 0.8513 |
| 0.934 | 0.3729 | 920 | 0.7135 | 0.3925 | 0.7135 | 0.8447 |
| 0.934 | 0.3737 | 922 | 0.8336 | 0.3658 | 0.8336 | 0.9130 |
| 0.934 | 0.3745 | 924 | 0.7210 | 0.3872 | 0.7210 | 0.8491 |
| 0.934 | 0.3754 | 926 | 0.6977 | 0.4294 | 0.6977 | 0.8353 |
| 0.934 | 0.3762 | 928 | 0.7136 | 0.4352 | 0.7136 | 0.8448 |
| 0.934 | 0.3770 | 930 | 0.6788 | 0.4170 | 0.6788 | 0.8239 |
| 0.934 | 0.3778 | 932 | 0.6737 | 0.4328 | 0.6737 | 0.8208 |
| 0.934 | 0.3786 | 934 | 0.7061 | 0.3858 | 0.7061 | 0.8403 |
| 0.934 | 0.3794 | 936 | 0.7671 | 0.3646 | 0.7671 | 0.8759 |
| 0.934 | 0.3802 | 938 | 0.7219 | 0.3971 | 0.7219 | 0.8496 |
| 0.934 | 0.3810 | 940 | 0.7239 | 0.3835 | 0.7239 | 0.8508 |
| 0.934 | 0.3818 | 942 | 0.7367 | 0.3564 | 0.7367 | 0.8583 |
| 0.934 | 0.3827 | 944 | 0.7590 | 0.3293 | 0.7590 | 0.8712 |
| 0.934 | 0.3835 | 946 | 0.7166 | 0.3716 | 0.7166 | 0.8465 |
| 0.934 | 0.3843 | 948 | 0.7190 | 0.3648 | 0.7190 | 0.8479 |
| 0.934 | 0.3851 | 950 | 0.7217 | 0.3541 | 0.7217 | 0.8495 |
| 0.934 | 0.3859 | 952 | 0.7924 | 0.3143 | 0.7924 | 0.8901 |
| 0.934 | 0.3867 | 954 | 0.7955 | 0.2995 | 0.7955 | 0.8919 |
| 0.934 | 0.3875 | 956 | 0.7022 | 0.3545 | 0.7022 | 0.8380 |
| 0.934 | 0.3883 | 958 | 0.7773 | 0.3942 | 0.7773 | 0.8817 |
| 0.934 | 0.3891 | 960 | 0.7980 | 0.3874 | 0.7980 | 0.8933 |
| 0.934 | 0.3899 | 962 | 0.7043 | 0.3479 | 0.7043 | 0.8392 |
| 0.934 | 0.3908 | 964 | 0.7732 | 0.2805 | 0.7732 | 0.8793 |
| 0.934 | 0.3916 | 966 | 0.7964 | 0.2759 | 0.7964 | 0.8924 |
| 0.934 | 0.3924 | 968 | 0.7550 | 0.3503 | 0.7550 | 0.8689 |
| 0.934 | 0.3932 | 970 | 0.8387 | 0.3502 | 0.8387 | 0.9158 |
| 0.934 | 0.3940 | 972 | 0.8067 | 0.3315 | 0.8067 | 0.8981 |
| 0.934 | 0.3948 | 974 | 0.7669 | 0.3488 | 0.7669 | 0.8757 |
| 0.934 | 0.3956 | 976 | 0.7505 | 0.3727 | 0.7505 | 0.8663 |
| 0.934 | 0.3964 | 978 | 0.7314 | 0.3617 | 0.7314 | 0.8552 |
| 0.934 | 0.3972 | 980 | 0.7958 | 0.3636 | 0.7958 | 0.8921 |
| 0.934 | 0.3981 | 982 | 0.8442 | 0.3637 | 0.8442 | 0.9188 |
| 0.934 | 0.3989 | 984 | 0.7538 | 0.3734 | 0.7538 | 0.8682 |
| 0.934 | 0.3997 | 986 | 0.7322 | 0.4015 | 0.7322 | 0.8557 |
| 0.934 | 0.4005 | 988 | 0.7365 | 0.4021 | 0.7365 | 0.8582 |
| 0.934 | 0.4013 | 990 | 0.7283 | 0.3794 | 0.7283 | 0.8534 |
| 0.934 | 0.4021 | 992 | 0.7212 | 0.3842 | 0.7212 | 0.8492 |
| 0.934 | 0.4029 | 994 | 0.7096 | 0.3888 | 0.7096 | 0.8424 |
| 0.934 | 0.4037 | 996 | 0.7261 | 0.3938 | 0.7261 | 0.8521 |
| 0.934 | 0.4045 | 998 | 0.7677 | 0.3873 | 0.7677 | 0.8762 |
| 0.3836 | 0.4054 | 1000 | 0.8145 | 0.3742 | 0.8145 | 0.9025 |
| 0.3836 | 0.4062 | 1002 | 0.8123 | 0.3717 | 0.8123 | 0.9013 |
| 0.3836 | 0.4070 | 1004 | 0.7417 | 0.3490 | 0.7417 | 0.8612 |
| 0.3836 | 0.4078 | 1006 | 0.7529 | 0.3642 | 0.7529 | 0.8677 |
| 0.3836 | 0.4086 | 1008 | 0.7902 | 0.3288 | 0.7902 | 0.8889 |
| 0.3836 | 0.4094 | 1010 | 0.8864 | 0.3550 | 0.8864 | 0.9415 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
ThuyNT03/CS505-Dev-CSI-xlm-align-base | ThuyNT03 | "2024-07-03T21:49:21Z" | 8 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/xlm-align-base",
"base_model:finetune:microsoft/xlm-align-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-07-03T21:27:18Z" | ---
base_model: microsoft/xlm-align-base
tags:
- generated_from_trainer
model-index:
- name: CS505-Dev-CSI-xlm-align-base
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. -->
# CS505-Dev-CSI-xlm-align-base
This model is a fine-tuned version of [microsoft/xlm-align-base](https://huggingface.co/microsoft/xlm-align-base) 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: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF | mradermacher | "2025-01-08T11:33:28Z" | 31 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:zelk12/MT2-MMMAGMU-gemma-2-9B",
"base_model:quantized:zelk12/MT2-MMMAGMU-gemma-2-9B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-01-08T10:57:26Z" | ---
base_model: zelk12/MT2-MMMAGMU-gemma-2-9B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/zelk12/MT2-MMMAGMU-gemma-2-9B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/MT2-MMMAGMU-gemma-2-9B-GGUF/resolve/main/MT2-MMMAGMU-gemma-2-9B.f16.gguf) | f16 | 18.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
dima806/sms-spam-detection-distilbert | dima806 | "2024-10-19T11:17:36Z" | 192 | 1 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"base_model:distilbert/distilbert-base-cased",
"base_model:finetune:distilbert/distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-11-16T08:21:04Z" | ---
license: apache-2.0
metrics:
- accuracy
- f1
base_model:
- distilbert/distilbert-base-cased
---
See https://www.kaggle.com/code/dima806/sms-spam-detection-distilbert for more details. |
mradermacher/MN-RocinanteCelestar-12B-i1-GGUF | mradermacher | "2024-09-09T07:31:17Z" | 203 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:rityak/MN-RocinanteCelestar-12B",
"base_model:quantized:rityak/MN-RocinanteCelestar-12B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2024-09-09T05:38:43Z" | ---
base_model: rityak/MN-RocinanteCelestar-12B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/rityak/MN-RocinanteCelestar-12B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 7.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 7.2 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 7.2 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/MN-RocinanteCelestar-12B-i1-GGUF/resolve/main/MN-RocinanteCelestar-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
lesso11/d9dd468a-5f86-4915-a81e-f37cfe0d40e7 | lesso11 | "2025-02-27T05:55:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-13b-hf",
"base_model:adapter:NousResearch/CodeLlama-13b-hf",
"region:us"
] | null | "2025-02-27T05:14:56Z" | ---
library_name: peft
base_model: NousResearch/CodeLlama-13b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d9dd468a-5f86-4915-a81e-f37cfe0d40e7
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
auto_find_batch_size: true
base_model: NousResearch/CodeLlama-13b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 24b1e8b4a4332f62_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/24b1e8b4a4332f62_train_data.json
type:
field_input: concepts
field_instruction: topic
field_output: markdown
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso11/d9dd468a-5f86-4915-a81e-f37cfe0d40e7
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000211
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/24b1e8b4a4332f62_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
seed: 110
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: e7aa7832-7239-42e8-9b2f-093e2cf642a7
wandb_project: 11a
wandb_run: your_name
wandb_runid: e7aa7832-7239-42e8-9b2f-093e2cf642a7
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# d9dd468a-5f86-4915-a81e-f37cfe0d40e7
This model is a fine-tuned version of [NousResearch/CodeLlama-13b-hf](https://huggingface.co/NousResearch/CodeLlama-13b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4984
## 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: 0.000211
- train_batch_size: 4
- eval_batch_size: 4
- seed: 110
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0004 | 1 | 0.8650 |
| 1.3012 | 0.0185 | 50 | 0.6067 |
| 1.1154 | 0.0371 | 100 | 0.5670 |
| 1.0874 | 0.0556 | 150 | 0.5440 |
| 0.9397 | 0.0742 | 200 | 0.5306 |
| 1.1374 | 0.0927 | 250 | 0.5191 |
| 0.8862 | 0.1113 | 300 | 0.5130 |
| 0.9946 | 0.1298 | 350 | 0.5064 |
| 0.8426 | 0.1484 | 400 | 0.5010 |
| 1.0521 | 0.1669 | 450 | 0.4988 |
| 0.876 | 0.1855 | 500 | 0.4984 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
KelvinL01/CreatingLove | KelvinL01 | "2025-03-07T17:57:25Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-03-07T17:57:24Z" | ---
license: apache-2.0
---
|
mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF | mradermacher | "2025-01-17T07:26:23Z" | 198 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"trl",
"dpo",
"en",
"base_model:AmberYifan/Mistral-7B-v0.1-sft-spin-1.6k",
"base_model:quantized:AmberYifan/Mistral-7B-v0.1-sft-spin-1.6k",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-01-17T07:08:51Z" | ---
base_model: AmberYifan/Mistral-7B-v0.1-sft-spin-1.6k
language:
- en
library_name: transformers
model_name: Mistral-7B-v0.1-sft-spin-1.6k
quantized_by: mradermacher
tags:
- generated_from_trainer
- trl
- dpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/AmberYifan/Mistral-7B-v0.1-sft-spin-1.6k
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-v0.1-sft-spin-1.6k-GGUF/resolve/main/Mistral-7B-v0.1-sft-spin-1.6k.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lesso03/bedbd70d-25b5-47ca-8b1d-a265e178c834 | lesso03 | "2025-01-15T16:19:12Z" | 7 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-7B",
"base_model:adapter:unsloth/Qwen2-7B",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-15T16:10:15Z" | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bedbd70d-25b5-47ca-8b1d-a265e178c834
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Qwen2-7B
bf16: true
chat_template: llama3
datasets:
- data_files:
- 22a3375d601f5255_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/22a3375d601f5255_train_data.json
type:
field_instruction: question
field_output: context
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: lesso03/bedbd70d-25b5-47ca-8b1d-a265e178c834
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/22a3375d601f5255_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 512
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: bb5ffa8b-184f-4a9d-9846-adfdc06113a4
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: bb5ffa8b-184f-4a9d-9846-adfdc06113a4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bedbd70d-25b5-47ca-8b1d-a265e178c834
This model is a fine-tuned version of [unsloth/Qwen2-7B](https://huggingface.co/unsloth/Qwen2-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0005 | 1 | nan |
| 0.0 | 0.0023 | 5 | nan |
| 0.0 | 0.0046 | 10 | nan |
| 0.0 | 0.0070 | 15 | nan |
| 0.0 | 0.0093 | 20 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
darinchau/comp5421-project-clean-frost-31-comp5421-mel-spectrogram-fma_small-128x216-step-22528 | darinchau | "2025-03-28T12:54:03Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | "2025-03-28T12:54:00Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
MrRobotoAI/101-GGUF | MrRobotoAI | "2025-03-26T10:31:05Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:MrRobotoAI/101",
"base_model:quantized:MrRobotoAI/101",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-03-26T10:30:23Z" | ---
base_model: MrRobotoAI/101
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# MrRobotoAI/101-GGUF
This model was converted to GGUF format from [`MrRobotoAI/101`](https://huggingface.co/MrRobotoAI/101) 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/MrRobotoAI/101) 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 MrRobotoAI/101-GGUF --hf-file 101-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MrRobotoAI/101-GGUF --hf-file 101-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo MrRobotoAI/101-GGUF --hf-file 101-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MrRobotoAI/101-GGUF --hf-file 101-q4_k_m.gguf -c 2048
```
|
lesso07/925c98cb-57dc-43c3-9ed8-03b295e777e5 | lesso07 | "2025-03-16T11:17:12Z" | 10 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b",
"base_model:adapter:unsloth/llama-3-8b",
"license:llama3",
"region:us"
] | null | "2025-03-13T05:27:17Z" | ---
library_name: peft
license: llama3
base_model: unsloth/llama-3-8b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 925c98cb-57dc-43c3-9ed8-03b295e777e5
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)
<br>
# 925c98cb-57dc-43c3-9ed8-03b295e777e5
This model is a fine-tuned version of [unsloth/llama-3-8b](https://huggingface.co/unsloth/llama-3-8b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4609
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.2371 | 0.0000 | 1 | 3.0484 |
| 2.4547 | 0.0001 | 3 | 3.0313 |
| 1.9117 | 0.0002 | 6 | 2.8498 |
| 1.7493 | 0.0003 | 9 | 2.4609 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF | mradermacher | "2024-12-23T20:23:48Z" | 13 | 1 | transformers | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"CultriX/MergeCeption-7B-v3",
"CultriX/MonaTrix-v4",
"en",
"base_model:CultriX/NeuralCeptrix-7B-SLERPv3",
"base_model:quantized:CultriX/NeuralCeptrix-7B-SLERPv3",
"endpoints_compatible",
"region:us"
] | null | "2024-12-23T12:26:30Z" | ---
base_model: CultriX/NeuralCeptrix-7B-SLERPv3
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- CultriX/MergeCeption-7B-v3
- CultriX/MonaTrix-v4
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/CultriX/NeuralCeptrix-7B-SLERPv3
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralCeptrix-7B-SLERPv3-GGUF/resolve/main/NeuralCeptrix-7B-SLERPv3.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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 -->
|
chukypedro/CapybaraHermes-2.5-Mistral-7B | chukypedro | "2024-05-19T19:15:44Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"base_model:argilla/CapybaraHermes-2.5-Mistral-7B",
"base_model:adapter:argilla/CapybaraHermes-2.5-Mistral-7B",
"region:us"
] | null | "2024-05-19T19:07:28Z" | ---
library_name: peft
base_model: argilla/CapybaraHermes-2.5-Mistral-7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 |
cognitivecomputations/quiet_dolphin | cognitivecomputations | "2024-04-06T17:08:16Z" | 2 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser",
"base_model:merge:cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser",
"base_model:ezelikman/quietstar-8-ahead",
"base_model:merge:ezelikman/quietstar-8-ahead",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-06T16:47:23Z" | ---
base_model:
- ezelikman/quietstar-8-ahead
- cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser
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 SLERP merge method.
### Models Merged
The following models were included in the merge:
* [ezelikman/quietstar-8-ahead](https://huggingface.co/ezelikman/quietstar-8-ahead)
* [cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: ezelikman/quietstar-8-ahead
layer_range: [0, 32]
- model: cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser
layer_range: [0, 32]
merge_method: slerp
base_model: ezelikman/quietstar-8-ahead
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
clip-Jannat-Toha/VIRAL.Jannat-Toha.Viral.Video.Full.Original.Video.Social.Media.X | clip-Jannat-Toha | "2025-02-21T23:25:47Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-02-21T23:25:01Z" | [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)](https://lekedvideo.xyz/watch/)
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://lekedvideo.xyz/watch/)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/) |
philip-hightech/e589c35f-355f-466b-9e07-c5d8250165a4 | philip-hightech | "2025-01-27T07:48:47Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-64k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-64k",
"license:apache-2.0",
"region:us"
] | null | "2025-01-27T07:46:04Z" | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-64k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e589c35f-355f-466b-9e07-c5d8250165a4
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: NousResearch/Yarn-Solar-10b-64k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 2d70a50ffe98c25d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2d70a50ffe98c25d_train_data.json
type:
field_instruction: prompt
field_output: reasoning
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: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: philip-hightech/e589c35f-355f-466b-9e07-c5d8250165a4
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/2d70a50ffe98c25d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: </s>
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: be8a31a4-7ae3-47f5-9703-6040de1476d7
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: be8a31a4-7ae3-47f5-9703-6040de1476d7
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e589c35f-355f-466b-9e07-c5d8250165a4
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-64k](https://huggingface.co/NousResearch/Yarn-Solar-10b-64k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0010 | 1 | nan |
| 0.0 | 0.0129 | 13 | nan |
| 0.0 | 0.0259 | 26 | nan |
| 0.0 | 0.0388 | 39 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
AventIQ-AI/bert-talentmatchai | AventIQ-AI | "2025-02-20T10:10:37Z" | 0 | 1 | null | [
"safetensors",
"bert",
"region:us"
] | null | "2025-02-20T09:35:21Z" | # Talent-Match-AI: Resume and Job Description Matching
## 📌 Overview
This repository hosts the quantized version of the **BERT-base-uncased** model for **Resume and Job Description Matching**. The model is designed to determine whether a resume aligns well with a given job description. If they are a strong match, the model outputs "Good Fit" with a confidence score; otherwise, it categorizes them as "Potential Fit" or "Not a Good Fit." The model has been optimized for efficient deployment while maintaining reasonable accuracy, making it suitable for real-time applications.
## 🏰 Model Details
- **Model Architecture:** BERT-base-uncased
- **Task:** Resume and Job Description Matching
- **Dataset:** `facehuggerapoorv/resume-jd-match`
- **Quantization:** Float16 (FP16) for optimized inference
- **Fine-tuning Framework:** Hugging Face Transformers
## 🚀 Usage
### Installation
```bash
pip install transformers torch
```
### Loading the Model
```python
from transformers import BertTokenizer, BertForSequenceClassification
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "AventIQ-AI/bert-talentmatchai"
model = BertForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = BertTokenizer.from_pretrained(model_name)
```
### Resume Matching Inference
```python
import torch
# Set device (use GPU if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Define label mapping
label_mapping = {0: "Not a Good Fit", 1: "Potential Fit", 2: "Good Fit"}
# Sample resume text for testing
test_resume = ["I have worked in different industries and have a lot of experience. I am a hard worker and can learn anything."]
# Tokenize test data
test_tokens = tokenizer(test_resume, padding="max_length", truncation=True, return_tensors="pt").to(device) # Move input to same device as model
# Make predictions
with torch.no_grad(): # Disable gradient computation for inference
output = model(**test_tokens)
# Get predicted label
predicted_label = output.logits.argmax(dim=1).item()
# Print result
print(f"Predicted Category: {predicted_label} ({label_mapping[predicted_label]})")
label_mapping = {0: "No Fit", 1: "Low Fit", 2: "Potential Fit", 3: "Good Fit"}
print(f"Predicted Category: {label_mapping[predictions]}")
```
## 📊 Quantized Model Evaluation Results
### 🔥 Evaluation Metrics 🔥
- ✅ **Accuracy:** 0.9224
- ✅ **Precision:** 0.9212
- ✅ **Recall:** 0.8450
- ✅ **F1-score:** 0.7718
## ⚡ Quantization Details
Post-training quantization was applied using PyTorch's built-in quantization framework. The model was quantized to Float16 (FP16) to reduce model size and improve inference efficiency while balancing accuracy.
## 💽 Repository Structure
```
.
├── model/ # Contains the quantized model files
├── tokenizer_config/ # Tokenizer configuration and vocabulary files
├── model.safetensors/ # Quantized Model
├── README.md # Model documentation
```
## ⚠️ Limitations
- The model may struggle with resumes and job descriptions that use non-standard terminology.
- Quantization may lead to slight degradation in accuracy compared to full-precision models.
- Performance may vary across different industries and job levels.
## 🤝 Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
|
Bunpot/lora_model | Bunpot | "2025-02-14T08:41:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-02-14T08:41:18Z" | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Bunpot
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Jennny/qwen-math-value-model-longer2 | Jennny | "2025-04-03T12:33:46Z" | 0 | 0 | null | [
"safetensors",
"qwen2",
"custom_code",
"region:us"
] | null | "2025-03-26T22:20:21Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
FIVE-MGI/Single_Neuron_Identification | FIVE-MGI | "2024-06-13T18:18:09Z" | 0 | 0 | tf-keras | [
"tf-keras",
"onnx",
"en",
"dataset:FIVE-MGI/SNIM20",
"license:agpl-3.0",
"region:us"
] | null | "2024-06-13T17:48:37Z" | ---
license: agpl-3.0
language:
- en
datasets:
- FIVE-MGI/SNIM20
---
# Single Neuron (or Cell) Identification
This model is designed for image classification and identifying rafts or regions that have single neurons and are trained on [FIVE-MGI/SNIM20].
## Research Paper
For more detailed information, please refer to our bioRxiv paper: [Classification of iPSC-Derived Cultures Using Convolutional Neural Networks to Identify Single Differentiated Neurons for Isolation or Measurement](https://www.biorxiv.org/content/10.1101/2023.12.24.573194) |
kenchenxingyu/flan-large-single-label-stance-human4 | kenchenxingyu | "2024-02-15T02:26:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-02-15T02:25:58Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TransferGraph/classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion | TransferGraph | "2024-02-29T12:53:07Z" | 4 | 0 | peft | [
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:tweet_eval",
"base_model:classla/bcms-bertic-parlasent-bcs-ter",
"base_model:adapter:classla/bcms-bertic-parlasent-bcs-ter",
"model-index",
"region:us"
] | text-classification | "2024-02-29T12:53:05Z" | ---
library_name: peft
tags:
- parquet
- text-classification
datasets:
- tweet_eval
metrics:
- accuracy
base_model: classla/bcms-bertic-parlasent-bcs-ter
model-index:
- name: classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: tweet_eval
type: tweet_eval
config: emotion
split: validation
args: emotion
metrics:
- type: accuracy
value: 0.4946524064171123
name: accuracy
---
<!-- 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. -->
# classla_bcms-bertic-parlasent-bcs-ter-finetuned-lora-tweet_eval_emotion
This model is a fine-tuned version of [classla/bcms-bertic-parlasent-bcs-ter](https://huggingface.co/classla/bcms-bertic-parlasent-bcs-ter) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.4947
## 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: 0.0004
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| accuracy | train_loss | epoch |
|:--------:|:----------:|:-----:|
| 0.1818 | None | 0 |
| 0.4679 | 1.2475 | 0 |
| 0.4786 | 1.1874 | 1 |
| 0.4920 | 1.1567 | 2 |
| 0.4947 | 1.1286 | 3 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2 |
LHRuig/andrzejdu | LHRuig | "2025-02-04T05:46:28Z" | 7 | 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 | "2025-02-04T05:46:07Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: suit
output:
url: images/suit.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: andrzejdu
---
# andrzejdu
<Gallery />
## Model description
andrzejdu lora
## Trigger words
You should use `andrzejdu` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/LHRuig/andrzejdu/tree/main) them in the Files & versions tab.
|
owaiskaifi/gen-qr-ai | owaiskaifi | "2023-06-22T14:56:32Z" | 30 | 2 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"controlnet",
"image-to-image",
"en",
"license:openrail++",
"endpoints_compatible",
"region:us"
] | image-to-image | "2023-06-22T05:02:44Z" | ---
tags:
- stable-diffusion
- controlnet
- image-to-image
license: openrail++
language:
- en
library_name: diffusers
pipeline_tag: image-to-image
---
# QR Code Conditioned ControlNet Models for Stable Diffusion 1.5
## Model Description
This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v1.5.
The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, this 1.5 version model was also trained on the same dataset for those who are using the older version.
## How to use with Diffusers
```bash
pip -q install diffusers transformers accelerate torch xformers
```
```python
import torch
from PIL import Image
from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
from diffusers.utils import load_image
controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15",
torch_dtype=torch.float16)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
controlnet=controlnet,
safety_checker=None,
torch_dtype=torch.float16
)
pipe.enable_xformers_memory_efficient_attention()
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
def resize_for_condition_image(input_image: Image, resolution: int):
input_image = input_image.convert("RGB")
W, H = input_image.size
k = float(resolution) / min(H, W)
H *= k
W *= k
H = int(round(H / 64.0)) * 64
W = int(round(W / 64.0)) * 64
img = input_image.resize((W, H), resample=Image.LANCZOS)
return img
# play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image
# qr code image
source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png")
# initial image, anything
init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg")
condition_image = resize_for_condition_image(source_image, 768)
init_image = resize_for_condition_image(init_image, 768)
generator = torch.manual_seed(123121231)
image = pipe(prompt="a bilboard in NYC with a qrcode",
negative_prompt="ugly, disfigured, low quality, blurry, nsfw",
image=init_image,
control_image=condition_image,
width=768,
height=768,
guidance_scale=20,
controlnet_conditioning_scale=1.5,
generator=generator,
strength=0.9,
num_inference_steps=150,
)
image.images[0]
```
## Performance and Limitations
These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).**
To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork.
## Installation
The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application.
For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail.
Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell. |
RomainDarous/large_directFourEpoch_additivePooling_noisedInit_stsModel | RomainDarous | "2025-03-22T23:33:21Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:51741",
"loss:CoSENTLoss",
"de",
"en",
"es",
"fr",
"it",
"nl",
"pl",
"pt",
"ru",
"zh",
"dataset:PhilipMay/stsb_multi_mt",
"arxiv:1908.10084",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2025-03-22T23:32:39Z" | ---
language:
- de
- en
- es
- fr
- it
- nl
- pl
- pt
- ru
- zh
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:51741
- loss:CoSENTLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Starsza para azjatycka pozuje z noworodkiem przy stole obiadowym.
sentences:
- Koszykarz ma zamiar zdobyć punkty dla swojej drużyny.
- Grupa starszych osób pozuje wokół stołu w jadalni.
- Możliwe, że układ słoneczny taki jak nasz może istnieć poza galaktyką.
- source_sentence: Englisch arbeitet überall mit Menschen, die Dinge kaufen und verkaufen,
und in der Gastfreundschaft und im Tourismusgeschäft.
sentences:
- Ich bin in Maharashtra (einschließlich Mumbai) und Andhra Pradesh herumgereist,
und ich hatte kein Problem damit, nur mit Englisch auszukommen.
- 'Ein griechischsprachiger Sklave (δούλος, doulos) würde seinen Herrn, glaube ich,
κύριος nennen (translit: kurios; Herr, Herr, Herr, Herr; Vokativform: κύριε).'
- Das Paar lag auf dem Bett.
- source_sentence: Si vous vous comprenez et comprenez votre ennemi, vous aurez beaucoup
plus de chances de gagner n'importe quelle bataille.
sentences:
- 'Outre les probabilités de gagner une bataille théorique, cette citation a une
autre signification : l''importance de connaître/comprendre les autres.'
- Une femme et un chien se promènent ensemble.
- Un homme joue de la guitare.
- source_sentence: Un homme joue de la harpe.
sentences:
- Une femme joue de la guitare.
- une femme a un enfant.
- Un groupe de personnes est debout et assis sur le sol la nuit.
- source_sentence: Dois cães a lutar na neve.
sentences:
- Dois cães brincam na neve.
- Pode sempre perguntar, então é a escolha do autor a aceitar ou não.
- Um gato está a caminhar sobre chão de madeira dura.
datasets:
- PhilipMay/stsb_multi_mt
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts eval
type: sts-eval
metrics:
- type: pearson_cosine
value: 0.8423180648713237
name: Pearson Cosine
- type: spearman_cosine
value: 0.8595850000432059
name: Spearman Cosine
- type: pearson_cosine
value: 0.8420181975402647
name: Pearson Cosine
- type: spearman_cosine
value: 0.8630073561241816
name: Spearman Cosine
- type: pearson_cosine
value: 0.8405171361303234
name: Pearson Cosine
- type: spearman_cosine
value: 0.8594948677596693
name: Spearman Cosine
- type: pearson_cosine
value: 0.8375312155777364
name: Pearson Cosine
- type: spearman_cosine
value: 0.8583531749722014
name: Spearman Cosine
- type: pearson_cosine
value: 0.8397619344296936
name: Pearson Cosine
- type: spearman_cosine
value: 0.8592894281053397
name: Spearman Cosine
- type: pearson_cosine
value: 0.8302450119489335
name: Pearson Cosine
- type: spearman_cosine
value: 0.8477495437950113
name: Spearman Cosine
- type: pearson_cosine
value: 0.8403036335437926
name: Pearson Cosine
- type: spearman_cosine
value: 0.8618318944578455
name: Spearman Cosine
- type: pearson_cosine
value: 0.838706056263606
name: Pearson Cosine
- type: spearman_cosine
value: 0.8574971366611375
name: Spearman Cosine
- type: pearson_cosine
value: 0.8413052113094718
name: Pearson Cosine
- type: spearman_cosine
value: 0.8611085200053895
name: Spearman Cosine
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test
type: sts-test
metrics:
- type: pearson_cosine
value: 0.7456938524838218
name: Pearson Cosine
- type: spearman_cosine
value: 0.7483592546028903
name: Spearman Cosine
- type: pearson_cosine
value: 0.7237526314017121
name: Pearson Cosine
- type: spearman_cosine
value: 0.7169355021670776
name: Spearman Cosine
- type: pearson_cosine
value: 0.7669235794906317
name: Pearson Cosine
- type: spearman_cosine
value: 0.7631313253470643
name: Spearman Cosine
- type: pearson_cosine
value: 0.8298244150963187
name: Pearson Cosine
- type: spearman_cosine
value: 0.8324038122126458
name: Spearman Cosine
- type: pearson_cosine
value: 0.7166564070706897
name: Pearson Cosine
- type: spearman_cosine
value: 0.7227801582959456
name: Spearman Cosine
- type: pearson_cosine
value: 0.7855295239932334
name: Pearson Cosine
- type: spearman_cosine
value: 0.7934626158625494
name: Spearman Cosine
- type: pearson_cosine
value: 0.8386050236111093
name: Pearson Cosine
- type: spearman_cosine
value: 0.8275901416546908
name: Spearman Cosine
- type: pearson_cosine
value: 0.779112011887379
name: Pearson Cosine
- type: spearman_cosine
value: 0.7729611139511264
name: Spearman Cosine
- type: pearson_cosine
value: 0.7878478906763803
name: Pearson Cosine
- type: spearman_cosine
value: 0.7846990470347196
name: Spearman Cosine
- type: pearson_cosine
value: 0.7882844791307567
name: Pearson Cosine
- type: spearman_cosine
value: 0.7878180406501333
name: Spearman Cosine
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) on the [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt), [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) and [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 84fccfe766bcfd679e39efefe4ebf45af190ad2d -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt)
- **Languages:** de, en, es, fr, it, nl, pl, pt, ru, zh
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): MultiHeadGeneralizedPooling(
(P): ModuleList(
(0-7): 8 x Linear(in_features=768, out_features=96, bias=True)
)
(W1): ModuleList(
(0-7): 8 x Linear(in_features=96, out_features=384, bias=True)
)
(W2): ModuleList(
(0-7): 8 x Linear(in_features=384, out_features=96, bias=True)
)
)
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("RomainDarous/large_directFourEpoch_additivePooling_noisedInit_stsModel")
# Run inference
sentences = [
'Dois cães a lutar na neve.',
'Dois cães brincam na neve.',
'Pode sempre perguntar, então é a escolha do autor a aceitar ou não.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Datasets: `sts-eval`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test`, `sts-test` and `sts-test`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | sts-eval | sts-test |
|:--------------------|:-----------|:-----------|
| pearson_cosine | 0.8423 | 0.7883 |
| **spearman_cosine** | **0.8596** | **0.7878** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:----------|
| pearson_cosine | 0.842 |
| **spearman_cosine** | **0.863** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8405 |
| **spearman_cosine** | **0.8595** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8375 |
| **spearman_cosine** | **0.8584** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8398 |
| **spearman_cosine** | **0.8593** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8302 |
| **spearman_cosine** | **0.8477** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8403 |
| **spearman_cosine** | **0.8618** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8387 |
| **spearman_cosine** | **0.8575** |
#### Semantic Similarity
* Dataset: `sts-eval`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8413 |
| **spearman_cosine** | **0.8611** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
<details><summary>multi_stsb_de</summary>
#### multi_stsb_de
* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.58 tokens</li><li>max: 37 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.53 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------------------|:--------------------------------------------------------------------------|:--------------------------------|
| <code>Ein Flugzeug hebt gerade ab.</code> | <code>Ein Flugzeug hebt gerade ab.</code> | <code>1.0</code> |
| <code>Ein Mann spielt eine große Flöte.</code> | <code>Ein Mann spielt eine Flöte.</code> | <code>0.7599999904632568</code> |
| <code>Ein Mann streicht geriebenen Käse auf eine Pizza.</code> | <code>Ein Mann streicht geriebenen Käse auf eine ungekochte Pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_es</summary>
#### multi_stsb_es
* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 12.21 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.07 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------|:----------------------------------------------------------------------|:--------------------------------|
| <code>Un avión está despegando.</code> | <code>Un avión está despegando.</code> | <code>1.0</code> |
| <code>Un hombre está tocando una gran flauta.</code> | <code>Un hombre está tocando una flauta.</code> | <code>0.7599999904632568</code> |
| <code>Un hombre está untando queso rallado en una pizza.</code> | <code>Un hombre está untando queso rallado en una pizza cruda.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_fr</summary>
#### multi_stsb_fr
* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 12.6 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.49 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
| <code>Un avion est en train de décoller.</code> | <code>Un avion est en train de décoller.</code> | <code>1.0</code> |
| <code>Un homme joue d'une grande flûte.</code> | <code>Un homme joue de la flûte.</code> | <code>0.7599999904632568</code> |
| <code>Un homme étale du fromage râpé sur une pizza.</code> | <code>Un homme étale du fromage râpé sur une pizza non cuite.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_it</summary>
#### multi_stsb_it
* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 12.77 tokens</li><li>max: 36 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 12.69 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:--------------------------------|
| <code>Un aereo sta decollando.</code> | <code>Un aereo sta decollando.</code> | <code>1.0</code> |
| <code>Un uomo sta suonando un grande flauto.</code> | <code>Un uomo sta suonando un flauto.</code> | <code>0.7599999904632568</code> |
| <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza.</code> | <code>Un uomo sta spalmando del formaggio a pezzetti su una pizza non cotta.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_nl</summary>
#### multi_stsb_nl
* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 11.67 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 11.55 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:--------------------------------------------------------|:--------------------------------------------------------------------|:--------------------------------|
| <code>Er gaat een vliegtuig opstijgen.</code> | <code>Er gaat een vliegtuig opstijgen.</code> | <code>1.0</code> |
| <code>Een man speelt een grote fluit.</code> | <code>Een man speelt fluit.</code> | <code>0.7599999904632568</code> |
| <code>Een man smeert geraspte kaas op een pizza.</code> | <code>Een man strooit geraspte kaas op een ongekookte pizza.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pl</summary>
#### multi_stsb_pl
* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 12.2 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 12.11 tokens</li><li>max: 35 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------------|:------------------------------------------------------------------------|:--------------------------------|
| <code>Samolot wystartował.</code> | <code>Samolot wystartował.</code> | <code>1.0</code> |
| <code>Człowiek gra na dużym flecie.</code> | <code>Człowiek gra na flecie.</code> | <code>0.7599999904632568</code> |
| <code>Mężczyzna rozsiewa na pizzy rozdrobniony ser.</code> | <code>Mężczyzna rozsiewa rozdrobniony ser na niegotowanej pizzy.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pt</summary>
#### multi_stsb_pt
* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 12.33 tokens</li><li>max: 34 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 12.29 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------|
| <code>Um avião está a descolar.</code> | <code>Um avião aéreo está a descolar.</code> | <code>1.0</code> |
| <code>Um homem está a tocar uma grande flauta.</code> | <code>Um homem está a tocar uma flauta.</code> | <code>0.7599999904632568</code> |
| <code>Um homem está a espalhar queijo desfiado numa pizza.</code> | <code>Um homem está a espalhar queijo desfiado sobre uma pizza não cozida.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_ru</summary>
#### multi_stsb_ru
* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 11.19 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 11.17 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------|:---------------------------------------------------------------------|:--------------------------------|
| <code>Самолет взлетает.</code> | <code>Взлетает самолет.</code> | <code>1.0</code> |
| <code>Человек играет на большой флейте.</code> | <code>Человек играет на флейте.</code> | <code>0.7599999904632568</code> |
| <code>Мужчина разбрасывает сыр на пиццу.</code> | <code>Мужчина разбрасывает измельченный сыр на вареную пиццу.</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_zh</summary>
#### multi_stsb_zh
* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 10.7 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 10.79 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.45</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------|:----------------------------------|:--------------------------------|
| <code>一架飞机正在起飞。</code> | <code>一架飞机正在起飞。</code> | <code>1.0</code> |
| <code>一个男人正在吹一支大笛子。</code> | <code>一个人在吹笛子。</code> | <code>0.7599999904632568</code> |
| <code>一名男子正在比萨饼上涂抹奶酪丝。</code> | <code>一名男子正在将奶酪丝涂抹在未熟的披萨上。</code> | <code>0.7599999904632568</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
### Evaluation Datasets
<details><summary>multi_stsb_de</summary>
#### multi_stsb_de
* Dataset: [multi_stsb_de](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 18.25 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.25 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
| <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>Ein Mann mit einem Schutzhelm tanzt.</code> | <code>1.0</code> |
| <code>Ein kleines Kind reitet auf einem Pferd.</code> | <code>Ein Kind reitet auf einem Pferd.</code> | <code>0.949999988079071</code> |
| <code>Ein Mann verfüttert eine Maus an eine Schlange.</code> | <code>Der Mann füttert die Schlange mit einer Maus.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_es</summary>
#### multi_stsb_es
* Dataset: [multi_stsb_es](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 17.98 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 17.86 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------------------------------|:---------------------------------------------------------------------|:-------------------------------|
| <code>Un hombre con un casco está bailando.</code> | <code>Un hombre con un casco está bailando.</code> | <code>1.0</code> |
| <code>Un niño pequeño está montando a caballo.</code> | <code>Un niño está montando a caballo.</code> | <code>0.949999988079071</code> |
| <code>Un hombre está alimentando a una serpiente con un ratón.</code> | <code>El hombre está alimentando a la serpiente con un ratón.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_fr</summary>
#### multi_stsb_fr
* Dataset: [multi_stsb_fr](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 19.7 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 19.65 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------------|:----------------------------------------------------------------------------|:-------------------------------|
| <code>Un homme avec un casque de sécurité est en train de danser.</code> | <code>Un homme portant un casque de sécurité est en train de danser.</code> | <code>1.0</code> |
| <code>Un jeune enfant monte à cheval.</code> | <code>Un enfant monte à cheval.</code> | <code>0.949999988079071</code> |
| <code>Un homme donne une souris à un serpent.</code> | <code>L'homme donne une souris au serpent.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_it</summary>
#### multi_stsb_it
* Dataset: [multi_stsb_it](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.42 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 18.43 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------------|:---------------------------------------------------------------|:-------------------------------|
| <code>Un uomo con l'elmetto sta ballando.</code> | <code>Un uomo che indossa un elmetto sta ballando.</code> | <code>1.0</code> |
| <code>Un bambino piccolo sta cavalcando un cavallo.</code> | <code>Un bambino sta cavalcando un cavallo.</code> | <code>0.949999988079071</code> |
| <code>Un uomo sta dando da mangiare un topo a un serpente.</code> | <code>L'uomo sta dando da mangiare un topo al serpente.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_nl</summary>
#### multi_stsb_nl
* Dataset: [multi_stsb_nl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 17.88 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 17.71 tokens</li><li>max: 51 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-----------------------------------------------------|:-----------------------------------------------------|:-------------------------------|
| <code>Een man met een helm is aan het dansen.</code> | <code>Een man met een helm is aan het dansen.</code> | <code>1.0</code> |
| <code>Een jong kind rijdt op een paard.</code> | <code>Een kind rijdt op een paard.</code> | <code>0.949999988079071</code> |
| <code>Een man voedt een muis aan een slang.</code> | <code>De man voert een muis aan de slang.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pl</summary>
#### multi_stsb_pl
* Dataset: [multi_stsb_pl](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.54 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.43 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------------------------------|:---------------------------------------------------|:-------------------------------|
| <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>Tańczy mężczyzna w twardym kapeluszu.</code> | <code>1.0</code> |
| <code>Małe dziecko jedzie na koniu.</code> | <code>Dziecko jedzie na koniu.</code> | <code>0.949999988079071</code> |
| <code>Człowiek karmi węża myszką.</code> | <code>Ten człowiek karmi węża myszką.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_pt</summary>
#### multi_stsb_pt
* Dataset: [multi_stsb_pt](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 7 tokens</li><li>mean: 18.22 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 18.11 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------------|:-----------------------------------------------------------|:-------------------------------|
| <code>Um homem de chapéu duro está a dançar.</code> | <code>Um homem com um capacete está a dançar.</code> | <code>1.0</code> |
| <code>Uma criança pequena está a montar a cavalo.</code> | <code>Uma criança está a montar a cavalo.</code> | <code>0.949999988079071</code> |
| <code>Um homem está a alimentar um rato a uma cobra.</code> | <code>O homem está a alimentar a cobra com um rato.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_ru</summary>
#### multi_stsb_ru
* Dataset: [multi_stsb_ru](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.92 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 17.75 tokens</li><li>max: 47 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:------------------------------------------------------|:----------------------------------------------|:-------------------------------|
| <code>Человек в твердой шляпе танцует.</code> | <code>Мужчина в твердой шляпе танцует.</code> | <code>1.0</code> |
| <code>Маленький ребенок едет верхом на лошади.</code> | <code>Ребенок едет на лошади.</code> | <code>0.949999988079071</code> |
| <code>Мужчина кормит мышь змее.</code> | <code>Человек кормит змею мышью.</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>multi_stsb_zh</summary>
#### multi_stsb_zh
* Dataset: [multi_stsb_zh](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt) at [3acaa3d](https://huggingface.co/datasets/PhilipMay/stsb_multi_mt/tree/3acaa3dd8c91649e0b8e627ffad891f059e47c8c)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.37 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.24 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.42</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:---------------------------|:--------------------------|:-------------------------------|
| <code>一个戴着硬帽子的人在跳舞。</code> | <code>一个戴着硬帽的人在跳舞。</code> | <code>1.0</code> |
| <code>一个小孩子在骑马。</code> | <code>一个孩子在骑马。</code> | <code>0.949999988079071</code> |
| <code>一个人正在用老鼠喂蛇。</code> | <code>那人正在给蛇喂老鼠。</code> | <code>1.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | multi stsb de loss | multi stsb es loss | multi stsb fr loss | multi stsb it loss | multi stsb nl loss | multi stsb pl loss | multi stsb pt loss | multi stsb ru loss | multi stsb zh loss | sts-eval_spearman_cosine | sts-test_spearman_cosine |
|:-----:|:-----:|:-------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------:|:------------------------:|:------------------------:|
| 4.0 | 12960 | 3.7859 | 6.5030 | 6.5739 | 6.7230 | 6.8049 | 6.6585 | 6.8389 | 6.6333 | 6.7102 | 6.3148 | 0.8611 | - |
| -1 | -1 | - | - | - | - | - | - | - | - | - | - | - | 0.7878 |
### Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.4.1
- Transformers: 4.48.2
- PyTorch: 2.1.2+cu121
- Accelerate: 1.3.0
- Datasets: 2.16.1
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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Nitral-AI/L3.1_NN-8B-test2 | Nitral-AI | "2025-03-31T21:18:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Nitral-AI/8B-lora2-test",
"base_model:merge:Nitral-AI/8B-lora2-test",
"base_model:Nitral-AI/L3.1_NN-8B-test1",
"base_model:merge:Nitral-AI/L3.1_NN-8B-test1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-31T21:14:17Z" | ---
base_model:
- Nitral-AI/L3.1_NN-8B-test1
- Nitral-AI/8B-lora2-test
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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [Nitral-AI/L3.1_NN-8B-test1](https://huggingface.co/Nitral-AI/L3.1_NN-8B-test1) + [Nitral-AI/8B-lora2-test](https://huggingface.co/Nitral-AI/8B-lora2-test)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Nitral-AI/L3.1_NN-8B-test1+Nitral-AI/8B-lora2-test
layer_range: [0, 32]
- model: Nitral-AI/L3.1_NN-8B-test1+Nitral-AI/8B-lora2-test
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/L3.1_NN-8B-test1+Nitral-AI/8B-lora2-test
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.420
dtype: bfloat16
```
|
pczarnik/herbert-base-ner | pczarnik | "2025-01-28T09:50:52Z" | 905 | 4 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"pl",
"dataset:wikiann",
"license:cc-by-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2023-05-27T11:35:50Z" | ---
license: cc-by-4.0
datasets:
- wikiann
language:
- pl
pipeline_tag: token-classification
widget:
- text: "Nazywam się Grzegorz Brzęszczyszczykiewicz, pochodzę z Chrząszczyżewoszczyc, pracuję w Łękołodzkim Urzędzie Powiatowym"
- text: "Jestem Krzysiek i pracuję w Ministerstwie Sportu"
- text: "Na imię jej Wiktoria, pracuje w Krakowie na AGH"
model-index:
- name: herbert-base-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
config: pl
split: test
args: pl
metrics:
- name: Precision
type: precision
value: 0.8857142857142857
- name: Recall
type: recall
value: 0.9070532179048386
- name: F1
type: f1
value: 0.896256755412619
- name: Accuracy
type: accuracy
value: 0.9581463871961428
---
# herbert-base-ner
## Model description
**herbert-base-ner** is a fine-tuned HerBERT model that can be used for **Named Entity Recognition** .
It has been trained to recognize three types of entities: person (PER), location (LOC) and organization (ORG).
Specifically, this model is an [*allegro/herbert-base-cased*](https://huggingface.co/allegro/herbert-base-cased) model that was fine-tuned on the Polish subset of *wikiann* dataset.
### How to use
You can use this model with Transformers *pipeline* for NER.
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_checkpoint = "pczarnik/herbert-base-ner"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForTokenClassification.from_pretrained(model_checkpoint)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Nazywam się Grzegorz Brzęszczyszczykiewicz, pochodzę "\
"z Chrząszczyżewoszczyc, pracuję w Łękołodzkim Urzędzie Powiatowym"
ner_results = nlp(example)
print(ner_results)
```
```python
[{'entity': 'B-PER', 'score': 0.99451494, 'index': 4, 'word': 'Grzegorz</w>', 'start': 12, 'end': 20},
{'entity': 'I-PER', 'score': 0.99758506, 'index': 5, 'word': 'B', 'start': 21, 'end': 22},
{'entity': 'I-PER', 'score': 0.99749386, 'index': 6, 'word': 'rzę', 'start': 22, 'end': 25},
{'entity': 'I-PER', 'score': 0.9973041, 'index': 7, 'word': 'szczy', 'start': 25, 'end': 30},
{'entity': 'I-PER', 'score': 0.99682057, 'index': 8, 'word': 'szczy', 'start': 30, 'end': 35},
{'entity': 'I-PER', 'score': 0.9964832, 'index': 9, 'word': 'kiewicz</w>', 'start': 35, 'end': 42},
{'entity': 'B-LOC', 'score': 0.99427444, 'index': 14, 'word': 'Chrzą', 'start': 55, 'end': 60},
{'entity': 'I-LOC', 'score': 0.99143463, 'index': 15, 'word': 'szczy', 'start': 60, 'end': 65},
{'entity': 'I-LOC', 'score': 0.9922201, 'index': 16, 'word': 'że', 'start': 65, 'end': 67},
{'entity': 'I-LOC', 'score': 0.9918464, 'index': 17, 'word': 'wo', 'start': 67, 'end': 69},
{'entity': 'I-LOC', 'score': 0.9900766, 'index': 18, 'word': 'szczy', 'start': 69, 'end': 74},
{'entity': 'I-LOC', 'score': 0.98823845, 'index': 19, 'word': 'c</w>', 'start': 74, 'end': 75},
{'entity': 'B-ORG', 'score': 0.6808262, 'index': 23, 'word': 'Łę', 'start': 87, 'end': 89},
{'entity': 'I-ORG', 'score': 0.7763973, 'index': 24, 'word': 'ko', 'start': 89, 'end': 91},
{'entity': 'I-ORG', 'score': 0.77731717, 'index': 25, 'word': 'ło', 'start': 91, 'end': 93},
{'entity': 'I-ORG', 'score': 0.9108255, 'index': 26, 'word': 'dzkim</w>', 'start': 93, 'end': 98},
{'entity': 'I-ORG', 'score': 0.98050755, 'index': 27, 'word': 'Urzędzie</w>', 'start': 99, 'end': 107},
{'entity': 'I-ORG', 'score': 0.9789752, 'index': 28, 'word': 'Powiatowym</w>', 'start': 108, 'end': 118}]
```
### BibTeX entry and citation info
```
@inproceedings{mroczkowski-etal-2021-herbert,
title = "{H}er{BERT}: Efficiently Pretrained Transformer-based Language Model for {P}olish",
author = "Mroczkowski, Robert and
Rybak, Piotr and
Wr{\\'o}blewska, Alina and
Gawlik, Ireneusz",
booktitle = "Proceedings of the 8th Workshop on Balto-Slavic Natural Language Processing",
month = apr,
year = "2021",
address = "Kiyv, Ukraine",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bsnlp-1.1",
pages = "1--10",
}
```
```
@inproceedings{pan-etal-2017-cross,
title = "Cross-lingual Name Tagging and Linking for 282 Languages",
author = "Pan, Xiaoman and
Zhang, Boliang and
May, Jonathan and
Nothman, Joel and
Knight, Kevin and
Ji, Heng",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1178",
doi = "10.18653/v1/P17-1178",
pages = "1946--1958",
}
```
|
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