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Aedesa/Saeed65 | Aedesa | 2025-05-04T14:32:34Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T14:32:34Z | ---
license: apache-2.0
---
|
AndrewHanna/llama_ct_slow | AndrewHanna | 2025-05-04T14:30:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-19T15:56:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Photchara/stock-sentiment-analysis_V2 | Photchara | 2025-05-04T14:29:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T14:28:19Z | ---
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] |
fischm04/vit-base-oxford-iiit-pets | fischm04 | 2025-05-04T14:27:08Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-04-15T08:38:49Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-oxford-iiit-pets
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. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1691
- Accuracy: 0.9499
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3768 | 1.0 | 370 | 0.3309 | 0.9202 |
| 0.2111 | 2.0 | 740 | 0.2495 | 0.9296 |
| 0.1794 | 3.0 | 1110 | 0.2335 | 0.9269 |
| 0.1423 | 4.0 | 1480 | 0.2229 | 0.9337 |
| 0.1257 | 5.0 | 1850 | 0.2205 | 0.9364 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
## Zero-Shot Benchmark
We compared our fine-tuned ViT model against the **CLIP-ViT-L/14** zero-shot classifier on the Oxford-IIIT Pets dataset.
| Metric | ViT (Transfer Learning)
|------------|-------------------------
| Accuracy | 0.8800 |
| Precision | 0.8768 |
| Recall | 0.8800 |
Output of Pythonnotebook:
Accuracy: 0.8800
Precision: 0.8768
Recall: 0.8800
## Zero-Shot CLIP Evaluation
- **Model:** openai/clip-vit-base-patch32
- **Accuracy:** 88.00 %
- **Precision (weighted):** 87.68 %
- **Recall (weighted):** 88.00 %
|
Kahhhren/qwen_2.5_7b_retrain_novo | Kahhhren | 2025-05-04T14:26:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T14:26:30Z | ---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Kahhhren
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
DohyunAn/LLama-3.2-1B-unsloth-bnb-ko-wiki-finetune | DohyunAn | 2025-05-04T14:25:13Z | 22 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"ko",
"base_model:DohyunAn/LLama-3.2-1B-unsloth-bnb-tokenizer-modified",
"base_model:finetune:DohyunAn/LLama-3.2-1B-unsloth-bnb-tokenizer-modified",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-15T14:13:25Z | ---
base_model: DohyunAn/LLama-3.2-1B-unsloth-bnb-tokenizer-modified
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
- ko
---
Adapter for DohyunAn/LLama-3.2-1B-unsloth-bnb-tokenizer-modified. |
DohyunAn/LLama-3.2-1B-unsloth-bnb-tokenizer-modified | DohyunAn | 2025-05-04T14:23:39Z | 46 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-15T15:34:18Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DohyunAn
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
**Modification**: Increased the vocabulary size and expanded the embedding layer accordingly.
|
juhw/q478 | juhw | 2025-05-04T14:23:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T14:20:22Z | ---
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] |
filipesantoscv11/f9fcd9ab-92bc-4cf7-bb6d-0142baa33afe | filipesantoscv11 | 2025-05-04T14:22:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:adapter:unsloth/Meta-Llama-3.1-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T13:28:26Z | ---
library_name: peft
license: llama3.1
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f9fcd9ab-92bc-4cf7-bb6d-0142baa33afe
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/Meta-Llama-3.1-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- e09559fcf6f0ac01_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e09559fcf6f0ac01_train_data.json
type:
field_instruction: inputs
field_output: targets
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: filipesantoscv11/f9fcd9ab-92bc-4cf7-bb6d-0142baa33afe
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: true
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_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/e09559fcf6f0ac01_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: cb90103a-f63e-46ef-aa4d-918767b8bb09
wandb_project: s56-6
wandb_run: your_name
wandb_runid: cb90103a-f63e-46ef-aa4d-918767b8bb09
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f9fcd9ab-92bc-4cf7-bb6d-0142baa33afe
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B](https://huggingface.co/unsloth/Meta-Llama-3.1-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3741
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7301 | 0.0083 | 200 | 1.3741 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
bruhzair/ignore-merge-10 | bruhzair | 2025-05-04T14:21:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:50:13Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# nemo2
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 Passthrough merge method.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
modules:
default:
slices:
- sources:
- layer_range: [0, 4]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [2, 4]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [4, 8]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [6, 8]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 12]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [10, 12]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [12, 16]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [14, 16]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 20]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [18, 20]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [20, 24]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [22, 24]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 28]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [26, 28]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [28, 32]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [30, 32]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [32, 36]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [34, 36]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [36, 40]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [38, 40]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [40, 44]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [42, 44]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [44, 48]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [46, 48]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [48, 52]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [50, 52]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [52, 56]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [54, 56]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [56, 60]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [58, 60]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [60, 64]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [62, 64]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [64, 68]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [66, 68]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [68, 72]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [70, 72]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [72, 76]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [74, 76]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [76, 80]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- sources:
- layer_range: [78, 80]
model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
```
|
cvoffer/f010ffaa-00f6-44f9-ac78-2f432634832b | cvoffer | 2025-05-04T14:18:28Z | 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",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T13:54:11Z | ---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f010ffaa-00f6-44f9-ac78-2f432634832b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Meta-Llama-3-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ce73d3d1b54d2d5e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ce73d3d1b54d2d5e_train_data.json
type:
field_instruction: instruction
field_output: chosen_response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: cvoffer/f010ffaa-00f6-44f9-ac78-2f432634832b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 10
mixed_precision: bf16
mlflow_experiment_name: /tmp/ce73d3d1b54d2d5e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: <|end_of_text|>
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: 12717213-820a-4227-be7e-8d4a47298e72
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 12717213-820a-4227-be7e-8d4a47298e72
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f010ffaa-00f6-44f9-ac78-2f432634832b
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: 1.9236
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0323 | 0.1014 | 150 | 1.9236 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ingenio/IndoColSmol-256M | ingenio | 2025-05-04T14:18:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"colpali",
"generated_from_trainer",
"base_model:vidore/ColSmolVLM-Instruct-256M-base",
"base_model:finetune:vidore/ColSmolVLM-Instruct-256M-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T06:27:25Z | ---
library_name: transformers
license: mit
base_model: vidore/ColSmolVLM-Instruct-256M-base
tags:
- colpali
- generated_from_trainer
model-index:
- name: IndoColSmol-256M
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. -->
# IndoColSmol-256M
This model is a fine-tuned version of [vidore/ColSmolVLM-Instruct-256M-base](https://huggingface.co/vidore/ColSmolVLM-Instruct-256M-base) on the ingenio/indodvqa_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3786
## 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: 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_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0099 | 1 | 0.4959 |
| 0.4991 | 0.3960 | 40 | 0.4319 |
| 0.4293 | 0.7921 | 80 | 0.3986 |
| 0.4 | 1.1881 | 120 | 0.3829 |
| 0.3653 | 1.5842 | 160 | 0.3788 |
| 0.3846 | 1.9802 | 200 | 0.3764 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
rahadgazi/imagegenerate | rahadgazi | 2025-05-04T14:16:58Z | 0 | 0 | null | [
"finance",
"image-text-to-text",
"base_model:deepseek-ai/DeepSeek-Prover-V2-671B",
"base_model:finetune:deepseek-ai/DeepSeek-Prover-V2-671B",
"license:apache-2.0",
"region:us"
] | image-text-to-text | 2025-05-04T14:15:08Z | ---
license: apache-2.0
base_model:
- deepseek-ai/DeepSeek-Prover-V2-671B
pipeline_tag: image-text-to-text
tags:
- finance
--- |
cagrigungor/brain-tumor-44 | cagrigungor | 2025-05-04T14:16:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"tr",
"en",
"gl",
"arxiv:1910.09700",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-05-04T14:13:39Z | ---
library_name: transformers
license: apache-2.0
language:
- tr
- en
- gl
base_model:
- google/vit-base-patch16-224
---
# 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. -->
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jdchang/full-with-label-bs-1024-sg-2-step-11178 | jdchang | 2025-05-04T14:15:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T14:15:20Z | ---
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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- **Shared by [optional]:** [More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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- **Hardware Type:** [More Information Needed]
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earth-insights/SegEarth-R1-RRSIS-D | earth-insights | 2025-05-04T14:14:44Z | 0 | 0 | null | [
"safetensors",
"llava_phi",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T12:56:28Z | ---
license: apache-2.0
language:
- en
---
**project**: [earth-insights/SegEarth-R1](https://github.com/earth-insights/SegEarth-R1) |
masani/SFT_math_Llama-2-7b-hf_epoch_5_global_step_145 | masani | 2025-05-04T14:13:09Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-25T11:03:09Z | ---
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]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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bruhzair/ignore-merge-9 | bruhzair | 2025-05-04T14:12:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:34:27Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# deep2
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 Passthrough merge method.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
modules:
default:
slices:
- sources:
- layer_range: [0, 4]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [2, 4]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [4, 8]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [6, 8]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [8, 12]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [10, 12]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [12, 16]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [14, 16]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [16, 20]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [18, 20]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [20, 24]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [22, 24]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [24, 28]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [26, 28]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [28, 32]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [30, 32]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [32, 36]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [34, 36]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [36, 40]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [38, 40]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [40, 44]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [42, 44]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [44, 48]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [46, 48]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [48, 52]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [50, 52]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [52, 56]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [54, 56]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [56, 60]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [58, 60]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [60, 64]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [62, 64]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [64, 68]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [66, 68]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [68, 72]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [70, 72]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [72, 76]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [74, 76]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
- sources:
- layer_range: [76, 80]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
- sources:
- layer_range: [78, 80]
model: /workspace/cache/models--deepseek-ai--DeepSeek-R1-Distill-Llama-70B/snapshots/b1c0b44b4369b597ad119a196caf79a9c40e141e
parameters:
scale:
- filter: o_proj
value: 0.0
- filter: down_proj
value: 0.0
- value: 1.0
```
|
annasoli/gemma-3-12b-it_bad_med_full-ft_LR5e-6 | annasoli | 2025-05-04T14:10:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-04T13:36:24Z | ---
library_name: transformers
tags:
- 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]
- **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] |
Svngoku/AfricanHairFluxLora | Svngoku | 2025-05-04T14:09:10Z | 4 | 1 | diffusers | [
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-04-27T11:00:58Z | ---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Afro Hair
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
widget:
- text: Photography of styled Afro Hair, style instagram png
output:
url: images/example_ufdsuof10.png
- text: >-
Photography of styled Afro Hair, commercial ads, cosmetics, hair powered
shampoo
output:
url: images/example_qxbh4iyff.png
- text: >-
8k photorealistic image of an older black skin grandmother with wrinkles,
beautiful silver white dread locks in black head wrap , round rim glasses,
different angles, character sheets, with the Afro Hair style
output:
url: images/example_3ubr5t0fh.png
---
# AfricanHairFluxLora
<Gallery />
## Model description
## Trigger words
You should use `Afro Hair` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Svngoku/AfricanHairFluxLora/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
harryitz/TEST | harryitz | 2025-05-04T14:06:13Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-04T14:00:37Z | ---
license: bigscience-openrail-m
---
|
Monda/marabert-ahasis | Monda | 2025-05-04T14:04:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T14:04:20Z | ---
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] |
dibarnes/dazed | dibarnes | 2025-05-04T14:04:06Z | 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 | 2025-05-04T13:52:41Z | ---
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: dzd
---
# Dazed
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `dzd` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "dzd",
"lora_weights": "https://huggingface.co/dibarnes/dazed/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## 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('dibarnes/dazed', weight_name='lora.safetensors')
image = pipeline('dzd').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)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/dibarnes/dazed/discussions) to add images that show off what you’ve made with this LoRA.
|
dimasik1987/5400b703-dbe7-444d-9f75-6a814413eefe | dimasik1987 | 2025-05-04T14:02:21Z | 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",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T13:53:57Z | ---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5400b703-dbe7-444d-9f75-6a814413eefe
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Meta-Llama-3-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ce73d3d1b54d2d5e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ce73d3d1b54d2d5e_train_data.json
type:
field_instruction: instruction
field_output: chosen_response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: dimasik1987/5400b703-dbe7-444d-9f75-6a814413eefe
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 10
mixed_precision: bf16
mlflow_experiment_name: /tmp/ce73d3d1b54d2d5e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: <|end_of_text|>
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: 12717213-820a-4227-be7e-8d4a47298e72
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 12717213-820a-4227-be7e-8d4a47298e72
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 5400b703-dbe7-444d-9f75-6a814413eefe
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: 1.9761
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0568 | 0.1014 | 150 | 1.9761 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ajagota71/pythia-70m-detox-test | ajagota71 | 2025-05-04T14:01:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2025-05-04T14:00:53Z | ---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="ajagota71//content/irl_llms/outputs/2025-05-04_13-51-04/final-model")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("ajagota71//content/irl_llms/outputs/2025-05-04_13-51-04/final-model")
model = AutoModelForCausalLMWithValueHead.from_pretrained("ajagota71//content/irl_llms/outputs/2025-05-04_13-51-04/final-model")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
melekuk/Qwen2.5-14B-Aletheia-DRT-Q6_K-GGUF | melekuk | 2025-05-04T13:59:10Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:melekuk/Qwen2.5-14B-Aletheia-DRT",
"base_model:quantized:melekuk/Qwen2.5-14B-Aletheia-DRT",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-04T13:58:21Z | ---
base_model: melekuk/Qwen2.5-14B-Aletheia-DRT
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# melekuk/Qwen2.5-14B-Aletheia-DRT-Q6_K-GGUF
This model was converted to GGUF format from [`melekuk/Qwen2.5-14B-Aletheia-DRT`](https://huggingface.co/melekuk/Qwen2.5-14B-Aletheia-DRT) 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/melekuk/Qwen2.5-14B-Aletheia-DRT) 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 melekuk/Qwen2.5-14B-Aletheia-DRT-Q6_K-GGUF --hf-file qwen2.5-14b-aletheia-drt-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo melekuk/Qwen2.5-14B-Aletheia-DRT-Q6_K-GGUF --hf-file qwen2.5-14b-aletheia-drt-q6_k.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 melekuk/Qwen2.5-14B-Aletheia-DRT-Q6_K-GGUF --hf-file qwen2.5-14b-aletheia-drt-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo melekuk/Qwen2.5-14B-Aletheia-DRT-Q6_K-GGUF --hf-file qwen2.5-14b-aletheia-drt-q6_k.gguf -c 2048
```
|
marialvsantiago/616a2258-2a9a-441b-8054-07779696248b | marialvsantiago | 2025-05-04T13:58:58Z | 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",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T13:53:55Z | ---
library_name: peft
license: other
base_model: NousResearch/Meta-Llama-3-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 616a2258-2a9a-441b-8054-07779696248b
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/Meta-Llama-3-8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ce73d3d1b54d2d5e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ce73d3d1b54d2d5e_train_data.json
type:
field_instruction: instruction
field_output: chosen_response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/616a2258-2a9a-441b-8054-07779696248b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/ce73d3d1b54d2d5e_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
special_tokens:
pad_token: <|end_of_text|>
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: 12717213-820a-4227-be7e-8d4a47298e72
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 12717213-820a-4227-be7e-8d4a47298e72
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 616a2258-2a9a-441b-8054-07779696248b
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: 0.4636
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7541 | 0.1082 | 200 | 0.4636 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Supersemar/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_spotted_mallard | Supersemar | 2025-05-04T13:58:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am sniffing spotted mallard",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:56:50Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_spotted_mallard
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am sniffing spotted mallard
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_spotted_mallard
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Supersemar/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-sniffing_spotted_mallard", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
YLX1965/ylx-qwen3-14b | YLX1965 | 2025-05-04T13:54:36Z | 0 | 0 | bertopic | [
"bertopic",
"safetensors",
"text-generation-inference",
"transformers",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T13:41:29Z | ---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
library_name: bertopic
---
---
tags:
- transformers
- safetensors
- english
- text-generation-inference
- unsloth
- qwen3
- trl
license: apache-2.0
睡眠魔法师-殷利鑫
# ylx-qwen3-14b
## 模型描述 (Model Description)
这是 **Qwen3-14B** 大型语言模型的一个微调版本，经过专门训练，增强了其**对话**和**推理**能力。该模型使用 **Unsloth** 库进行了高效微调，Unsloth 利用了 4 比特量化和 LoRA 等技术，以实现更快的训练速度和更低的内存占用。
本次微调的目标是创建一个既能进行自然对话，又能像人类思维过程（`<think>`）一样在解决问题时展示一步步推理过程的模型。
## 训练详情 (Training Details)
基础模型 Qwen3-14B 使用 **LoRA (Low-Rank Adaptation)** 方法进行了微调。LoRA 是一种参数高效的微调技术，它只更新模型参数的一小部分，与完整模型微调相比，训练过程显著更快，计算成本也更低。
微调是使用 **Unsloth** 库结合 Hugging Face 的 **TRL (Transformer Reinforcement Learning)** 库（具体使用了 `SFTTrainer`）进行的。
训练数据包含两个数据集的混合：
1. **`unsloth/OpenMathReasoning-mini` (cot 分割):** 这个数据集包含数学问题和详细的**思维链 (Chain-of-Thought, CoT)** 推理过程。使用这些数据进行训练有助于模型学习如何分解问题并解释其解决步骤。
2. **`mlabonne/FineTome-100k`:** 一个包含大量 ShareGPT 格式的通用对话回合的大型数据集。这些数据用于提升模型进行自然多样对话的能力。
数据集经过混合以平衡推理和通用对话能力。在随附的 notebook 演示的特定训练运行中，数据混合比例约为**25% 推理数据和 75% 对话数据**。
模型在加载和训练时使用了 Unsloth 的优化功能，以**4 比特精度**进行处理，这大大降低了显存需求。
*注意：提供的训练 notebook 使用了少量训练步数（30 步）用于演示目的。为了获得最佳性能，建议使用更多步数或 epoch，并在更广泛的数据集混合上进行训练。*
## 预期用途 (Intended Use)
该模型旨在用于需要具备对话能力和推理能力的 AI 应用。潜在用例包括：
* 构建可以回答问题并提供解释的聊天机器人。
* 开发可以帮助用户一步步解决问题的 AI 助手。
* 用于演示解题过程的教育工具。
## 局限性和偏见 (Limitations and Bias)
与所有大型语言模型一样，该模型可能存在以下局限性：
* **幻觉 (Hallucination):** 生成不正确或无意义的信息。
* **偏见 (Bias):** 可能反映训练数据中存在的偏见。
* 性能可能因任务的复杂度和领域而异。
* 推理模式的有效性取决于用于训练的推理数据的质量和数量。
用户在部署模型时应自行进行评估并考虑这些局限性。
## 如何使用 (How to Use)
您可以使用 `unsloth` 库轻松加载和使用该模型。
```python
from unsloth import FastLanguageModel
import torch
# 加载微调后的模型
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "YLX1965/ylx-qwen3-14b", # 如果您的实际仓库 ID 不同，请替换此处
max_seq_length = 2048,
load_in_4bit = True, # 如果您保存为其他格式，请改为 False
)
# 推理示例（关闭思考模式）
messages = [
{"role" : "user", "content" : "Solve (x + 2)^2 = 0."}
]
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True,
enable_thinking = False, # 关闭思考模式
)
from transformers import TextStreamer
print("--- 关闭思考模式 ---")
_ = model.generate(
**tokenizer(text, return_tensors = "pt").to("cuda"),
max_new_tokens = 256,
temperature = 0.7, top_p = 0.8, top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
)
# 推理示例（开启思考模式）
messages = [
{"role" : "user", "content" : "Solve (x + 2)^2 = 0."}
]
text = tokenizer.apply_chat_template(
messages,
tokenize = False,
add_generation_prompt = True,
enable_thinking = True, # 开启思考模式
)
print("\n--- 开启思考模式 ---")
_ = model.generate(
**tokenizer(text, return_tensors = "pt").to("cuda"),
max_new_tokens = 1024, # 增加以获得更长的输出！
temperature = 0.6, top_p = 0.95, top_k = 20,
streamer = TextStreamer(tokenizer, skip_prompt = True),
) |
kostiantynk-outlook/37d31c66-4da1-4835-b531-07415b75b9d7 | kostiantynk-outlook | 2025-05-04T13:53:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T13:53:10Z | ---
library_name: transformers
model_name: kostiantynk-outlook/37d31c66-4da1-4835-b531-07415b75b9d7
tags:
- generated_from_trainer
- unsloth
licence: license
---
# Model Card for kostiantynk-outlook/37d31c66-4da1-4835-b531-07415b75b9d7
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mjs227/rltu_grpo_10_0_249-llama-merged | mjs227 | 2025-05-04T13:51:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:31:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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] |
Silverlaining/Qwen3-Instruct-python-sft | Silverlaining | 2025-05-04T13:48:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:47:25Z | ---
library_name: transformers
tags:
- 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]
- **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] |
ASethi04/meta-llama-Llama-3.1-8B-tulu-science-first-lora-4-0.0001 | ASethi04 | 2025-05-04T13:48:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T11:42:31Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-tulu-science-first-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-tulu-science-first-lora-4-0.0001
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-tulu-science-first-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/k76jg40s)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
phililp-arnold/d3d1d5c4-fbc0-4523-809e-a18bc10de1f0 | phililp-arnold | 2025-05-04T13:45:10Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:elyza/Llama-3-ELYZA-JP-8B",
"base_model:adapter:elyza/Llama-3-ELYZA-JP-8B",
"region:us"
] | null | 2025-05-04T13:44:40Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: elyza/Llama-3-ELYZA-JP-8B
model-index:
- name: phililp-arnold/d3d1d5c4-fbc0-4523-809e-a18bc10de1f0
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. -->
# phililp-arnold/d3d1d5c4-fbc0-4523-809e-a18bc10de1f0
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1051
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
Chaew00n/test-supervised-fine-tuning | Chaew00n | 2025-05-04T13:39:08Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:Qwen/Qwen3-1.7B",
"base_model:adapter:Qwen/Qwen3-1.7B",
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T18:14:13Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
tags:
- generated_from_trainer
model-index:
- name: test-supervised-fine-tuning
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-supervised-fine-tuning
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.2.0
- Tokenizers 0.21.1 |
fedovtt/0ae412c4-55ee-488e-ac0c-d128e1258aca | fedovtt | 2025-05-04T13:37:17Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-3B-Instruct",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T13:05:32Z | ---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0ae412c4-55ee-488e-ac0c-d128e1258aca
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Llama-3.2-3B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ab5595d09687e4d8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ab5595d09687e4d8_train_data.json
type:
field_instruction: premise
field_output: hypothesis
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: fedovtt/0ae412c4-55ee-488e-ac0c-d128e1258aca
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 10
mixed_precision: bf16
mlflow_experiment_name: /tmp/ab5595d09687e4d8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5f63cfe1-4d17-450d-aedb-b238c203c48e
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 5f63cfe1-4d17-450d-aedb-b238c203c48e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 0ae412c4-55ee-488e-ac0c-d128e1258aca
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3449
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.4736 | 0.0094 | 150 | 3.3449 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
RajeevanL/tamil-roberta_v-1 | RajeevanL | 2025-05-04T13:34:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-05-04T13:09:05Z | ---
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] |
ZhuangXialie/Qwen-code-7B-SFT-100k-v2-cot-v3 | ZhuangXialie | 2025-05-04T13:34:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:local",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T10:19:39Z | ---
datasets: local
library_name: transformers
model_name: Qwen-code-7B-SFT-100k-v2-cot-v3
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen-code-7B-SFT-100k-v2-cot-v3
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [local](https://huggingface.co/datasets/local) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ZhuangXialie/Qwen-code-7B-SFT-100k-v2-cot-v3", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dyx_team/huggingface/runs/ee79jvhx)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ivangrapher/40fca2ad-9b15-4af3-b96f-96548effddfd | ivangrapher | 2025-05-04T13:34:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-3B-Instruct",
"license:llama3.2",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T13:03:41Z | ---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 40fca2ad-9b15-4af3-b96f-96548effddfd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Llama-3.2-3B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ab5595d09687e4d8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ab5595d09687e4d8_train_data.json
type:
field_instruction: premise
field_output: hypothesis
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: ivangrapher/40fca2ad-9b15-4af3-b96f-96548effddfd
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/ab5595d09687e4d8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5f63cfe1-4d17-450d-aedb-b238c203c48e
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 5f63cfe1-4d17-450d-aedb-b238c203c48e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 40fca2ad-9b15-4af3-b96f-96548effddfd
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.5074 | 0.0075 | 150 | 3.7794 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
goosull/Llama-3.2-1B-ko-kowiki-1.1 | goosull | 2025-05-04T13:32:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T13:30:24Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** goosull
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MAAT-EL-DUAT/NO.GODS.NO.MASTERS | MAAT-EL-DUAT | 2025-05-04T13:31:18Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T13:31:05Z | TRANSMISSIONS OF THE MEMETIC MATRIX PART/I |
ail-sa/kevin_plus_medium_fs_caption_v2 | ail-sa | 2025-05-04T13:31:08Z | 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 | 2025-05-04T10:22:46Z | ---
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: Sid
---
# Kevin_Plus_Medium_Fs_Caption_V2
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Sid` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sid",
"lora_weights": "https://huggingface.co/ail-sa/kevin_plus_medium_fs_caption_v2/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## 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('ail-sa/kevin_plus_medium_fs_caption_v2', weight_name='lora.safetensors')
image = pipeline('Sid').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)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ail-sa/kevin_plus_medium_fs_caption_v2/discussions) to add images that show off what you’ve made with this LoRA.
|
yinaoxiong/LanDiff | yinaoxiong | 2025-05-04T13:30:42Z | 0 | 1 | pytorch | [
"pytorch",
"safetensors",
"text-to-video",
"arxiv:2503.04606",
"license:mit",
"region:us"
] | text-to-video | 2025-04-26T04:00:23Z | ---
license: mit
pipeline_tag: text-to-video
library_name: pytorch
---
# LanDiff
<p align="center">
🎬 <a href="https://landiff.github.io/"><b>Demo Page</b></a>    ｜
  🤗 <a href="https://huggingface.co/yinaoxiong/LanDiff">Hugging Face</a>   |
  🤖 <a href="https://www.modelscope.cn/models/yinaoxiong/LanDiff">ModelScope</a>   |
   📑 <a href="https://arxiv.org/abs/2503.04606">Paper</a>   
</p>
<br>
-----
[**The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation**](https://arxiv.org/abs/2503.04606)
In this repository, we present **LanDiff**, a novel text-to-video generation framework that synergizes the strengths of Language Models and Diffusion Models. **LanDiff** offers these key features:
- 👍 **High Performance**: **LanDiff** (5B) achieves a score of **85.43** on the VBench T2V benchmark, surpassing state-of-the-art open-source models like Hunyuan Video (13B) and demonstrating competitiveness with leading commercial models such as Sora, Kling, and Hailuo. It also achieves SOTA performance among open-source models for long video generation.
- 👍 **Novel Hybrid Architecture**: **LanDiff** pioneers a **coarse-to-fine** generation pipeline, integrating Language Models (for high-level semantics) and Diffusion Models (for high-fidelity visual details), effectively combining the advantages of both paradigms.
- 👍 **Extreme Compression Semantic Tokenizer**: Features an innovative video semantic tokenizer that compresses rich 3D visual features into compact 1D discrete representations using query tokens and frame grouping, achieving an exceptional **~14,000x compression ratio** while preserving crucial semantic information.
- 👍 **Efficient Long Video Generation**: Implements a **streaming diffusion model** capable of generating long videos chunk-by-chunk, significantly reducing computational requirements and enabling scalable video synthesis.
## Quickstart
### Prerequisites
- Python 3.10 (validated) or higher
- PyTorch 2.5 (validated) or higher
### Installation
#### Clone the repository
```bash
git clone https://github.com/LanDiff/LanDiff
cd LanDiff
```
#### Using UV
```bash
# Create environment
uv sync
# Install gradio for run local demo (Optional)
uv sync --extra gradio
```
#### Using Conda
```bash
# Create and activate Conda environment
conda create -n landiff python=3.10
conda activate landiff
pip install torch==2.5.1 torchvision==0.20.1 --index-url https://download.pytorch.org/whl/cu121
# Install dependencies
pip install -r requirements.txt
# Install gradio for run local demo (Optional)
pip install gradio==5.27.0
```
## Model Download
| Model | Download Link | Download Link |
|--------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------|
| LanDiff | 🤗 [Huggingface](https://huggingface.co/yinaoxiong/LanDiff) | 🤖 [ModelScope](https://www.modelscope.cn/models/yinaoxiong/LanDiff)
## License
Code derived from CogVideo is licensed under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). Other parts of the code are licensed under the [MIT License](https://opensource.org/licenses/MIT).
## Citation
If you find our work helpful, please cite us.
```
@article{landiff,
title={The Best of Both Worlds: Integrating Language Models and Diffusion Models for Video Generation},
author={Yin, Aoxiong and Shen, Kai and Leng, Yichong and Tan, Xu and Zhou, Xinyu and Li, Juncheng and Tang, Siliang},
journal={arXiv preprint arXiv:2503.04606},
year={2025}
}
```
## Acknowledgements
We would like to thank the contributors to the [CogVideo](https://github.com/THUDM/CogVideo), [Theia](https://github.com/bdaiinstitute/theia), [TiTok](https://github.com/bytedance/1d-tokenizer), [flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl) and [HuggingFace](https://huggingface.co) repositories, for their open research. |
Berkesule/qwenvl_2.5_7b_3epoch | Berkesule | 2025-05-04T13:29:34Z | 10 | 0 | null | [
"safetensors",
"qwen2_5_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-04-18T09:11:27Z | ---
license: apache-2.0
---
|
alegator112/alegator | alegator112 | 2025-05-04T13:29:30Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-04T12:41:36Z | ---
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
--- |
phospho-app/dopaul-simple_pawn_move_v3-1b316n0ij6 | phospho-app | 2025-05-04T13:28:24Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-04T13:08:56Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [dopaul/simple_pawn_move_v3](https://huggingface.co/datasets/dopaul/simple_pawn_move_v3)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 64
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=replicate_groot_training_pipeline)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=replicate_groot_training_pipeline)
|
Nikhilbk/poca-SoccerTwos | Nikhilbk | 2025-05-04T13:25:45Z | 26 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2025-05-01T17:38:24Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **ppo** Agent playing **SoccerTwos**
This is a trained model of a **ppo** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Nikhilbk/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dimasik2987/34bc1a4a-55a5-4fd1-8aae-a333ce9aa3d9 | dimasik2987 | 2025-05-04T13:25:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-3B-Instruct",
"license:llama3.2",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T13:05:42Z | ---
library_name: peft
license: llama3.2
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 34bc1a4a-55a5-4fd1-8aae-a333ce9aa3d9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Llama-3.2-3B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ab5595d09687e4d8_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ab5595d09687e4d8_train_data.json
type:
field_instruction: premise
field_output: hypothesis
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: dimasik2987/34bc1a4a-55a5-4fd1-8aae-a333ce9aa3d9
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 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_steps: 200
micro_batch_size: 12
mixed_precision: bf16
mlflow_experiment_name: /tmp/ab5595d09687e4d8_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5f63cfe1-4d17-450d-aedb-b238c203c48e
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 5f63cfe1-4d17-450d-aedb-b238c203c48e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 34bc1a4a-55a5-4fd1-8aae-a333ce9aa3d9
This model is a fine-tuned version of [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3821
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 24
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 3.089 | 0.0300 | 200 | 3.3821 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Vaunorage/gemma-3-4b-it-unsloth-bnb-4bit-pretrain-legis-quebec | Vaunorage | 2025-05-04T13:15:25Z | 0 | 0 | transformers | [
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:12:35Z | ---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Vaunorage
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Momin-Shahzad/Pyramids | Momin-Shahzad | 2025-05-04T13:12:02Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2025-05-04T13:11:21Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Momin-Shahzad/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jdchang/full-with-label-bs-1024-sg-2-step-10692 | jdchang | 2025-05-04T13:11:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T13:11:49Z | ---
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] |
q-future/VQA-Assistant-llava-qwen-enhanced | q-future | 2025-05-04T13:09:51Z | 0 | 0 | null | [
"safetensors",
"llava_qwen",
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T19:40:49Z | ---
license: apache-2.0
---
|
MinaMila/llama_instbase_3b_unlearned | MinaMila | 2025-05-04T13:07:37Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T17:40:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
netalabs/vertex-qwen-7B-16bit-coder-shadcn-6epoch-v1.2 | netalabs | 2025-05-04T13:04:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-Coder-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T13:01:19Z | ---
base_model: unsloth/Qwen2.5-Coder-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** netalabs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-7B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
cvoffer/8a9d52d2-1784-454b-a1dd-1ada4e680826 | cvoffer | 2025-05-04T13:03:44Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:NousResearch/Nous-Capybara-7B-V1.9",
"base_model:adapter:NousResearch/Nous-Capybara-7B-V1.9",
"license:mit",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-04T10:22:06Z | ---
library_name: peft
license: mit
base_model: NousResearch/Nous-Capybara-7B-V1.9
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8a9d52d2-1784-454b-a1dd-1ada4e680826
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Nous-Capybara-7B-V1.9
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 2300620033aab66e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2300620033aab66e_train_data.json
type:
field_input: imgnet21k_path
field_instruction: wordnet_cat
field_output: caption
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: cvoffer/8a9d52d2-1784-454b-a1dd-1ada4e680826
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 10
mixed_precision: bf16
mlflow_experiment_name: /tmp/2300620033aab66e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5432948c-ce3e-46c0-b9f0-42b64b07f7bb
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 5432948c-ce3e-46c0-b9f0-42b64b07f7bb
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 8a9d52d2-1784-454b-a1dd-1ada4e680826
This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3597
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.5184 | 0.0034 | 150 | 2.3597 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
moqiyinlun1/coser-Flux-LoRA | moqiyinlun1 | 2025-05-04T13:00:30Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-04T08:09:06Z | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: photo of girl named <s0><s1>
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - moqiyinlun1/coser-Flux-LoRA
<Gallery />
## Model description
These are moqiyinlun1/coser-Flux-LoRA DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
Pivotal tuning was enabled: True.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `LAN` → use `<s0><s1>` in your prompt
## Download model
[Download the *.safetensors LoRA](moqiyinlun1/coser-Flux-LoRA/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('moqiyinlun1/coser-Flux-LoRA', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='moqiyinlun1/coser-Flux-LoRA', filename='/data/new_disk6/shenzhh/szh_lora_script/model/coser-Flux-LoRA_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
image = pipeline('photo of girl named <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
GuidoSt/DeepSeek-LED-Scheduler-7B-v2 | GuidoSt | 2025-05-04T12:58:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T12:11:51Z | ---
base_model: unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** GuidoSt
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-qwen-7b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/Veritas-12B-i1-GGUF | mradermacher | 2025-05-04T12:58:02Z | 738 | 0 | transformers | [
"transformers",
"gguf",
"philosophy",
"reasoning",
"logic",
"ethics",
"metaphysics",
"epistemology",
"critical-thinking",
"debate",
"dialogue",
"text-generation",
"uncensored",
"en",
"dataset:AiresPucrs/stanford-encyclopedia-philosophy",
"dataset:Manel/Reddit_Stoicism_QA_610",
"base_model:soob3123/Veritas-12B",
"base_model:quantized:soob3123/Veritas-12B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2025-04-23T01:50:55Z | ---
base_model: soob3123/Veritas-12B
datasets:
- AiresPucrs/stanford-encyclopedia-philosophy
- Manel/Reddit_Stoicism_QA_610
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- philosophy
- reasoning
- logic
- ethics
- metaphysics
- epistemology
- critical-thinking
- debate
- dialogue
- text-generation
- uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/soob3123/Veritas-12B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Veritas-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/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q4_1.gguf) | i1-Q4_1 | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/Veritas-12B-i1-GGUF/resolve/main/Veritas-12B.i1-Q6_K.gguf) | i1-Q6_K | 9.8 | 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 -->
|
JahBless/roberta_bias_5cls | JahBless | 2025-05-04T12:57:56Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T12:57:23Z | ---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: roberta_bias_5cls
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. -->
# roberta_bias_5cls
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6460
- Accuracy: 0.8785
- F1: 0.8789
- Precision: 0.8804
- Recall: 0.8785
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.4587 | 1.0 | 1000 | 0.4554 | 0.8475 | 0.8470 | 0.8543 | 0.8475 |
| 0.3385 | 2.0 | 2000 | 0.4823 | 0.868 | 0.8687 | 0.8710 | 0.868 |
| 0.3519 | 3.0 | 3000 | 0.5443 | 0.8725 | 0.8738 | 0.8757 | 0.8725 |
| 0.157 | 4.0 | 4000 | 0.6460 | 0.8785 | 0.8789 | 0.8804 | 0.8785 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.1.2
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Nithya9404/model-finetuned | Nithya9404 | 2025-05-04T12:57:35Z | 0 | 0 | transformers | [
"transformers",
"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 | 2025-05-04T12:36:29Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilgpt2
tags:
- generated_from_trainer
model-index:
- name: model-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. -->
# model-finetuned
This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) 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: 2.5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 128
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
TEST1HYF/my-finetuned-bert | TEST1HYF | 2025-05-04T12:57:18Z | 0 | 0 | transformers | [
"transformers",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-04T12:57:15Z | ---
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] |
Kwabs-10/Kwabs-10 | Kwabs-10 | 2025-05-04T12:56:12Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:huihui-ai/DeepSeek-R1-Distill-Qwen-1.5B-abliterated",
"base_model:adapter:huihui-ai/DeepSeek-R1-Distill-Qwen-1.5B-abliterated",
"region:us"
] | null | 2025-05-04T12:56:06Z | ---
library_name: peft
base_model: huihui-ai/DeepSeek-R1-Distill-Qwen-1.5B-abliterated
tags:
- generated_from_trainer
model-index:
- name: Kwabs-10
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. -->
# Kwabs-10
This model is a fine-tuned version of [huihui-ai/DeepSeek-R1-Distill-Qwen-1.5B-abliterated](https://huggingface.co/huihui-ai/DeepSeek-R1-Distill-Qwen-1.5B-abliterated) 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- 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: 50
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
GitBag/a_star_final_a_star_dapo_7_actor | GitBag | 2025-05-04T12:55:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T16:04:26Z | ---
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] |
Kuugo/Rouon-Aro-All_DDSP6.1 | Kuugo | 2025-05-04T12:54:49Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-04T11:43:28Z | # 狼音アロ SVC 语音模型 (ALL)
<img src="https://img.picui.cn/free/2025/04/24/6809b0dd0f75a.jpeg" width="250" height="250">
本项目使用DDSP 6.1训练了狼音アロ的语音合成模型，提供了一个综合版本，将所有的音频数据都进行了训练，具有最大程度的音域覆盖。
## 📁 数据集
**来源**: [Hugging Face数据集](https://huggingface.co/datasets/Kuugo/Rouon_Aro_voice)
## 🚀 模型详情
### 🏗️ 模型架构
- **主模型 (Generator)**
- 训练步数: 30,000步
- 基于DDSP 6.1架构
## 🙏 致谢
- DDSP 开发团队
|
ma921/gpt2-large_c_dpo_imdb_noise30_epoch5 | ma921 | 2025-05-04T12:54:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:ma921/gpt2-large-sft-imdb",
"base_model:finetune:ma921/gpt2-large-sft-imdb",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T12:53:20Z | ---
library_name: transformers
license: mit
base_model: ma921/gpt2-large-sft-imdb
tags:
- generated_from_trainer
model-index:
- name: gpt2-large_c_dpo_imdb_noise30_epoch5
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. -->
# gpt2-large_c_dpo_imdb_noise30_epoch5
This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
maksf8486/20bdfb58-cb21-4572-971d-e22b810275fa | maksf8486 | 2025-05-04T12:53:41Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/gemma-2-2b",
"base_model:quantized:unsloth/gemma-2-2b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-04T12:16:35Z | ---
base_model: unsloth/gemma-2-2b
library_name: transformers
model_name: 20bdfb58-cb21-4572-971d-e22b810275fa
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 20bdfb58-cb21-4572-971d-e22b810275fa
This model is a fine-tuned version of [unsloth/gemma-2-2b](https://huggingface.co/unsloth/gemma-2-2b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="maksf8486/20bdfb58-cb21-4572-971d-e22b810275fa", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-2/runs/w02syyie)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
apriasmoro/2e4f1b83-ebd8-4260-a5e1-edbd3c6d21a2 | apriasmoro | 2025-05-04T12:52:25Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T12:14:45Z | ---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
library_name: transformers
model_name: 2e4f1b83-ebd8-4260-a5e1-edbd3c6d21a2
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 2e4f1b83-ebd8-4260-a5e1-edbd3c6d21a2
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="apriasmoro/2e4f1b83-ebd8-4260-a5e1-edbd3c6d21a2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/apriasmoro-abcstudio/llama3_dpo/runs/kyzpngd9)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu124
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
jacobcarajo/Phind-CodeLlama-34B-v2-Q5_K_M-GGUF | jacobcarajo | 2025-05-04T12:52:15Z | 0 | 0 | null | [
"gguf",
"code llama",
"llama-cpp",
"gguf-my-repo",
"base_model:Phind/Phind-CodeLlama-34B-v2",
"base_model:quantized:Phind/Phind-CodeLlama-34B-v2",
"license:llama2",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T12:50:19Z | ---
base_model: Phind/Phind-CodeLlama-34B-v2
license: llama2
tags:
- code llama
- llama-cpp
- gguf-my-repo
model-index:
- name: Phind-CodeLlama-34B-v1
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 73.8%
name: pass@1
verified: false
---
# jacobcarajo/Phind-CodeLlama-34B-v2-Q5_K_M-GGUF
This model was converted to GGUF format from [`Phind/Phind-CodeLlama-34B-v2`](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) 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/Phind/Phind-CodeLlama-34B-v2) 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 jacobcarajo/Phind-CodeLlama-34B-v2-Q5_K_M-GGUF --hf-file phind-codellama-34b-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jacobcarajo/Phind-CodeLlama-34B-v2-Q5_K_M-GGUF --hf-file phind-codellama-34b-v2-q5_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 jacobcarajo/Phind-CodeLlama-34B-v2-Q5_K_M-GGUF --hf-file phind-codellama-34b-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jacobcarajo/Phind-CodeLlama-34B-v2-Q5_K_M-GGUF --hf-file phind-codellama-34b-v2-q5_k_m.gguf -c 2048
```
|
cognitivecomputations/Qwen3-235B-A22B-AWQ | cognitivecomputations | 2025-05-04T12:51:05Z | 17 | 5 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"conversational",
"arxiv:2309.00071",
"base_model:Qwen/Qwen3-235B-A22B",
"base_model:quantized:Qwen/Qwen3-235B-A22B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | 2025-05-02T18:45:45Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-235B-A22B
library_name: transformers
---
# Qwen3-235B-A22B-AWQ
Uploaded by Eric Hartford
Copied from Modelscope https://www.modelscope.cn/models/swift/Qwen3-235B-A22B-AWQ
Original model https://huggingface.co/Qwen/Qwen3-235B-A22B
# Modelscope AWQ Modelcard:
import torch
from modelscope import AutoModelForCausalLM, AutoTokenizer
model_name = "swift/Qwen3-235B-A22B-AWQ"
## load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
## prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
## conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
## parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
# Original Modelcard
# Qwen3-235B-A22B
<a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Qwen3 Highlights
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features:
- **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios.
- **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning.
- **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience.
- **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks.
- **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**.
## Model Overview
**Qwen3-235B-A22B** has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Number of Parameters: 235B in total and 22B activated
- Number of Paramaters (Non-Embedding): 234B
- Number of Layers: 94
- Number of Attention Heads (GQA): 64 for Q and 4 for KV
- Number of Experts: 128
- Number of Activated Experts: 8
- Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts).
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Quickstart
The code of Qwen3-MoE has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.51.0`, you will encounter the following error:
```
KeyError: 'qwen3_moe'
```
The following contains a code snippet illustrating how to use the model generate content based on given inputs.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen3-235B-A22B"
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
# prepare the model input
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# conduct text completion
generated_ids = model.generate(
**model_inputs,
max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
# parsing thinking content
try:
# rindex finding 151668 (</think>)
index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
index = 0
thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
print("thinking content:", thinking_content)
print("content:", content)
```
For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
- SGLang:
```shell
python -m sglang.launch_server --model-path Qwen/Qwen3-235B-A22B --reasoning-parser qwen3 --tp 8
```
- vLLM:
```shell
vllm serve Qwen/Qwen3-235B-A22B --enable-reasoning --reasoning-parser deepseek_r1
```
For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3.
## Switching Between Thinking and Non-Thinking Mode
> [!TIP]
> The `enable_thinking` switch is also available in APIs created by SGLang and vLLM.
> Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users.
### `enable_thinking=True`
By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=True # True is the default value for enable_thinking
)
```
In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response.
> [!NOTE]
> For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### `enable_thinking=False`
We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency.
```python
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False # Setting enable_thinking=False disables thinking mode
)
```
In this mode, the model will not generate any think content and will not include a `<think>...</think>` block.
> [!NOTE]
> For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section.
### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input
We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations.
Here is an example of a multi-turn conversation:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
class QwenChatbot:
def __init__(self, model_name="Qwen/Qwen3-235B-A22B"):
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.history = []
def generate_response(self, user_input):
messages = self.history + [{"role": "user", "content": user_input}]
text = self.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = self.tokenizer(text, return_tensors="pt")
response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist()
response = self.tokenizer.decode(response_ids, skip_special_tokens=True)
# Update history
self.history.append({"role": "user", "content": user_input})
self.history.append({"role": "assistant", "content": response})
return response
# Example Usage
if __name__ == "__main__":
chatbot = QwenChatbot()
# First input (without /think or /no_think tags, thinking mode is enabled by default)
user_input_1 = "How many r's in strawberries?"
print(f"User: {user_input_1}")
response_1 = chatbot.generate_response(user_input_1)
print(f"Bot: {response_1}")
print("----------------------")
# Second input with /no_think
user_input_2 = "Then, how many r's in blueberries? /no_think"
print(f"User: {user_input_2}")
response_2 = chatbot.generate_response(user_input_2)
print(f"Bot: {response_2}")
print("----------------------")
# Third input with /think
user_input_3 = "Really? /think"
print(f"User: {user_input_3}")
response_3 = chatbot.generate_response(user_input_3)
print(f"Bot: {response_3}")
```
> [!NOTE]
> For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled.
> When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block.
## Agentic Use
Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity.
To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself.
```python
from qwen_agent.agents import Assistant
# Define LLM
llm_cfg = {
'model': 'Qwen3-235B-A22B',
# Use the endpoint provided by Alibaba Model Studio:
# 'model_type': 'qwen_dashscope',
# 'api_key': os.getenv('DASHSCOPE_API_KEY'),
# Use a custom endpoint compatible with OpenAI API:
'model_server': 'http://localhost:8000/v1', # api_base
'api_key': 'EMPTY',
# Other parameters:
# 'generate_cfg': {
# # Add: When the response content is `<think>this is the thought</think>this is the answer;
# # Do not add: When the response has been separated by reasoning_content and content.
# 'thought_in_content': True,
# },
}
# Define Tools
tools = [
{'mcpServers': { # You can specify the MCP configuration file
'time': {
'command': 'uvx',
'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai']
},
"fetch": {
"command": "uvx",
"args": ["mcp-server-fetch"]
}
}
},
'code_interpreter', # Built-in tools
]
# Define Agent
bot = Assistant(llm=llm_cfg, function_list=tools)
# Streaming generation
messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}]
for responses in bot.run(messages=messages):
pass
print(responses)
```
## Processing Long Texts
Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method.
YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks:
- Modifying the model files:
In the `config.json` file, add the `rope_scaling` fields:
```json
{
...,
"rope_scaling": {
"rope_type": "yarn",
"factor": 4.0,
"original_max_position_embeddings": 32768
}
}
```
For `llama.cpp`, you need to regenerate the GGUF file after the modification.
- Passing command line arguments:
For `vllm`, you can use
```shell
vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072
```
For `sglang`, you can use
```shell
python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}'
```
For `llama-server` from `llama.cpp`, you can use
```shell
llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768
```
> [!IMPORTANT]
> If you encounter the following warning
> ```
> Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'}
> ```
> please upgrade `transformers>=4.51.0`.
> [!NOTE]
> All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.**
> We advise adding the `rope_scaling` configuration only when processing long contexts is required.
> It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0.
> [!NOTE]
> The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance.
> [!TIP]
> The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed.
## Best Practices
To achieve optimal performance, we recommend the following settings:
1. **Sampling Parameters**:
- For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions.
- For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`.
- For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance.
2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance.
3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking.
- **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt.
- **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`."
4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed.
### Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3,
title = {Qwen3},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {April},
year = {2025}
}
``` |
Petercusin/PGS_news_classifier | Petercusin | 2025-05-04T12:45:43Z | 0 | 0 | fasttext | [
"fasttext",
"code",
"text-classification",
"en",
"license:mit",
"region:us"
] | text-classification | 2025-05-04T12:19:40Z | ---
license: mit
language:
- en
library_name: fasttext
tags:
- code
pipeline_tag: text-classification
---
## 1. Model Details
**Attribute** | **Value** |
|-------------------------------|-----------------------------|
| Developed by | Petercusin (Guisheng Pan) |
| Model Architecture | fasttext |
| Number of words | 267,799 |
| Number of labels | 42 |
| Labels | ['Arts', 'Arts & Culture', 'Black voices', 'Business', 'College', 'Comedy', 'Crime', 'Culture & Arts', 'Divorce', 'Education', 'Entertainment', 'Environment', 'Fifty', 'Food & Drink', 'Good news', 'Green', 'Healthy living', 'Home & Living', 'Impact', 'Latino voices', 'Media', 'Money', 'Parenting', 'Parents', 'Politics', 'Queer voices', 'Religion', 'Science', 'Sports', 'Style', 'Style & Beauty', 'Taste', 'Tech', 'The worldpost', 'Travel', 'U.S. news', 'Weddings', 'Weird news', 'Wellness', 'Women', 'World news', 'Worldpost'] |
| Words/sec/thread | 497,437 |
| Learning rate | 0.000000 |
| Average loss | 0.550502 |
| Model saved to | PGS_news_classifier.bin (890M) |
| Precision@1 | 0.7414219768714605 |
| Recall@1 | 0.7414219768714605 |
| Number of examples | 42,026 (test data) |
## 2. Model Description
This model is designed to classify English news articles into various domains or categories. It can be used for tasks such as news categorization, content organization, and topic-based filtering.
## ⚙️3. How to Get Started with the Model
```python
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 20 20:24:58 2025
@author: Petercusin
"""
import fasttext
# pip install pgsfile
# https://pypi.org/project/PgsFile/
from PgsFile import predict_category
# Load the trained model
model_path = "PGS_news_classifier.bin"
model = fasttext.load_model(model_path)
# New titles or texts to classify
new_titles = [
"Stock markets reach all-time high amid economic recovery",
"Scientists discover new species in Amazon rainforest",
"Congress passes new bill on healthcare reforms",
"The stairway to love: Chongqing's real-life fairy tale",
"African delegation take in Shanghai sights on Huangpu cruise",
"China expected to achieve higher grain output in 2025: report",
"China continued its dominance at the 2025 World Aquatics Diving World Cup in Guadalajara, sweeping all four gold medals on the third day of competitions on Saturday, along with one silver.",
"A 'DeepSeek moment for AI agents' as China launches Manus",
"Developed by Monica.im, Manus achieved top scores on the GAIA (General AI Assistant) benchmark, exceeding those of OpenAI's GPT (generative pre-trained transformer) tools. GAIA is a real-world benchmark for general AI assistants.",
"This week and without warning, a horrid video popped up on my phone. A puppy had its mouth and paws bound with tape, and was hanging in a plastic bag by the motorway. I immediately flicked past, but the image stayed with me. This was something I didn’t want to see, yet there it was at 11am on a Tuesday."
]
try:
# Process new titles
for title in new_titles:
label, score = predict_category(model, title)
print(f"Title/Text: {title}")
print(f"Predicted category: <{label}> (confidence: {score:.2f})")
print("*" * 50) # Add a separator line for better readability
finally:
# Explicitly delete the model to free up memory
del model
```
## Result
```json
Title/Text: Stock markets reach all-time high amid economic recovery
Predicted category: <BUSINESS> (confidence: 1.00)
**************************************************
Title/Text: Scientists discover new species in Amazon rainforest
Predicted category: <SCIENCE> (confidence: 0.99)
**************************************************
Title/Text: Congress passes new bill on healthcare reforms
Predicted category: <POLITICS> (confidence: 1.00)
**************************************************
Title/Text: The stairway to love: Chongqing's real-life fairy tale
Predicted category: <ENTERTAINMENT> (confidence: 0.04)
**************************************************
Title/Text: African delegation take in Shanghai sights on Huangpu cruise
Predicted category: <TRAVEL> (confidence: 0.89)
**************************************************
Title/Text: China expected to achieve higher grain output in 2025: report
Predicted category: <WORLDPOST> (confidence: 0.01)
**************************************************
Title/Text: China continued its dominance at the 2025 World Aquatics Diving World Cup in Guadalajara, sweeping all four gold medals on the third day of competitions on Saturday, along with one silver.
Predicted category: <SPORTS> (confidence: 0.19)
**************************************************
Title/Text: A 'DeepSeek moment for AI agents' as China launches Manus
Predicted category: <TECH> (confidence: 0.99)
**************************************************
Title/Text: Developed by Monica.im, Manus achieved top scores on the GAIA (General AI Assistant) benchmark, exceeding those of OpenAI's GPT (generative pre-trained transformer) tools. GAIA is a real-world benchmark for general AI assistants.
Predicted category: <TECH> (confidence: 0.94)
**************************************************
Title/Text: This week and without warning, a horrid video popped up on my phone. A puppy had its mouth and paws bound with tape, and was hanging in a plastic bag by the motorway. I immediately flicked past, but the image stayed with me. This was something I didn’t want to see, yet there it was at 11am on a Tuesday.
Predicted category: <ENTERTAINMENT> (confidence: 0.04)
**************************************************
```
## 🤝 4. Model Card Contact
Author: Pan Guisheng, a PhD student at the Graduate Institute of Interpretation and Translation of Shanghai International Studies University Email: [email protected] |
mradermacher/Qwen3-8B-abliterated-TIES-GGUF | mradermacher | 2025-05-04T12:41:19Z | 20 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/Qwen3-8B-abliterated-TIES",
"base_model:quantized:nbeerbower/Qwen3-8B-abliterated-TIES",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T17:11:49Z | ---
base_model: nbeerbower/Qwen3-8B-abliterated-TIES
language:
- en
library_name: transformers
license: apache-2.0
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/nbeerbower/Qwen3-8B-abliterated-TIES
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-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/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-8B-abliterated-TIES-GGUF/resolve/main/Qwen3-8B-abliterated-TIES.f16.gguf) | f16 | 16.5 | 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 -->
|
WinfredGe/T2S-DiT | WinfredGe | 2025-05-04T12:41:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-04T12:40:11Z | ---
license: apache-2.0
---
|
mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF | mradermacher | 2025-05-04T12:41:07Z | 14 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/Qwen3-0.6B-abliterated-TIES",
"base_model:quantized:nbeerbower/Qwen3-0.6B-abliterated-TIES",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T17:49:55Z | ---
base_model: nbeerbower/Qwen3-0.6B-abliterated-TIES
language:
- en
library_name: transformers
license: apache-2.0
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/nbeerbower/Qwen3-0.6B-abliterated-TIES
<!-- 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/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-abliterated-TIES-GGUF/resolve/main/Qwen3-0.6B-abliterated-TIES.f16.gguf) | f16 | 1.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF | mradermacher | 2025-05-04T12:41:03Z | 17 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/Qwen3-1.7B-abliterated-TIES",
"base_model:quantized:nbeerbower/Qwen3-1.7B-abliterated-TIES",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T17:54:58Z | ---
base_model: nbeerbower/Qwen3-1.7B-abliterated-TIES
language:
- en
library_name: transformers
license: apache-2.0
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/nbeerbower/Qwen3-1.7B-abliterated-TIES
<!-- 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/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-1.7B-abliterated-TIES-GGUF/resolve/main/Qwen3-1.7B-abliterated-TIES.f16.gguf) | f16 | 3.5 | 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 -->
|
Eehan/Qwen2.5-1.5B-drpo-hh-0.82e-temp-0.66-beta-0.04 | Eehan | 2025-05-04T12:39:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T12:37:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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] |
ail-sa/kevin_stockyplus_bald_v1 | ail-sa | 2025-05-04T12:38:20Z | 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 | 2025-05-04T11:52:52Z | ---
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: Sid
---
# Kevin_Stockyplus_Bald_V1
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Sid` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sid",
"lora_weights": "https://huggingface.co/ail-sa/kevin_stockyplus_bald_v1/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## 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('ail-sa/kevin_stockyplus_bald_v1', weight_name='lora.safetensors')
image = pipeline('Sid').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)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ail-sa/kevin_stockyplus_bald_v1/discussions) to add images that show off what you’ve made with this LoRA.
|
fivedoctors/poca-SoccerTwos | fivedoctors | 2025-05-04T12:38:17Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2025-05-04T12:33:36Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: fivedoctors/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Hums003/PEFT_LlaMA_3.2_MCoT | Hums003 | 2025-05-04T12:34:39Z | 9 | 0 | peft | [
"peft",
"safetensors",
"medical",
"Reasoning",
"Training",
"question-answering",
"en",
"dataset:FreedomIntelligence/medical-o1-reasoning-SFT",
"arxiv:1910.09700",
"license:apache-2.0",
"region:us"
] | question-answering | 2025-04-26T02:20:06Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
library_name: peft
license: apache-2.0
datasets:
- FreedomIntelligence/medical-o1-reasoning-SFT
language:
- en
metrics:
- code_eval
new_version: meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: question-answering
tags:
- medical
- Reasoning
- Training
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Humaima Anwar
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **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]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.14.0 |
Eehan/Qwen2.5-1.5B-drpo-hh-gpm-4dim-temp-0.66-beta-0.04 | Eehan | 2025-05-04T12:33:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T12:31:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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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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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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Gwangwoon/muse | Gwangwoon | 2025-05-04T12:32:21Z | 4 | 0 | peft | [
"peft",
"safetensors",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-03-26T03:22:33Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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Gwangwoon/muse2 | Gwangwoon | 2025-05-04T12:31:25Z | 141 | 0 | peft | [
"peft",
"safetensors",
"zho",
"eng",
"fra",
"spa",
"por",
"deu",
"ita",
"rus",
"jpn",
"kor",
"vie",
"tha",
"ara",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-03-28T05:09:40Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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[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
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[More Information Needed]
## Training Details
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<!-- Relevant interpretability work for the model goes here -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.15.1 |
geonmin-kim/llm_vlm_onnx_sapmle | geonmin-kim | 2025-05-04T12:30:09Z | 0 | 0 | null | [
"onnx",
"region:us"
] | null | 2025-05-03T04:01:48Z | ### LLM text generation python examples
```python
from transformers import AutoConfig, AutoTokenizer
import onnxruntime
import numpy as np
# 1. Load config, processor, and model
path_to_model = "./llm/model"
path_to_tokenizer = "./llm/tokenizer"
config = AutoConfig.from_pretrained(path_to_model)
tokenizer = AutoTokenizer.from_pretrained(path_to_tokenizer)
decoder_session = onnxruntime.InferenceSession(f"{path_to_model}/q4f16.onnx")
## Set config values
num_key_value_heads = config.num_key_value_heads
head_dim = config.head_dim
num_hidden_layers = config.num_hidden_layers
eos_token_id = 106 # 106 is for <end_of_turn>
# 2. Prepare inputs
## Create input messages
messages = [
{ "role": "system", "content": "You are a helpful assistant." },
{ "role": "user", "content": "Write me a short poem about Machine Learning." },
]
## Apply tokenizer
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="np")
## Prepare decoder inputs
batch_size = inputs['input_ids'].shape[0]
past_key_values = {
f'past_key_values.{layer}.{kv}': np.zeros([batch_size, num_key_value_heads, 0, head_dim], dtype=np.float32)
for layer in range(num_hidden_layers)
for kv in ('key', 'value')
}
input_ids = inputs['input_ids']
position_ids = np.tile(np.arange(1, input_ids.shape[-1] + 1), (batch_size, 1))
# 3. Generation loop
max_new_tokens = 128
generated_tokens = np.array([[]], dtype=np.int64)
for i in range(max_new_tokens):
logits, *present_key_values = decoder_session.run(None, dict(
input_ids=input_ids,
position_ids=position_ids,
**past_key_values,
))
## Update values for next generation loop
input_ids = logits[:, -1].argmax(-1, keepdims=True)
position_ids = position_ids[:, -1:] + 1
for j, key in enumerate(past_key_values):
past_key_values[key] = present_key_values[j]
generated_tokens = np.concatenate([generated_tokens, input_ids], axis=-1)
if (input_ids == eos_token_id).all():
break
## (Optional) Streaming
print(tokenizer.decode(input_ids[0]), end='', flush=True)
print()
# 4. Output result
print(tokenizer.batch_decode(generated_tokens))
```
### VLM text generation python examples
```python
import argparse
import requests
import onnxruntime
from transformers import AutoTokenizer
import numpy as np
import time
from PIL import Image
IMAGE_TOKEN_INDEX = 151646
MAX_GEN_LEN = 128
USE_SAMPLING = True
print("Loading inference sessions...")
load_start = time.time()
image_emb_session = onnxruntime.InferenceSession("vlm/model/vision_encoder.onnx")
text_emb_session = onnxruntime.InferenceSession("vlm/model/token_embed_model.onnx")
decoding_session = onnxruntime.InferenceSession("vlm/model/decoder.onnx")
load_end = time.time()
print(f"Inference sessions are loaded. Loading takes {load_end-load_start:0.2f} sec")
def main(args):
tokenizer = AutoTokenizer.from_pretrained("./vlm/tokenizer")
tokenizer.add_tokens(["<image>"], special_tokens=True)
query = args.input_text
prompt = f"<|im_start|>user\n<image>\n{query}<|im_end|>\n<|im_start|>assistant\n"
past_kv_values, first_token, input_token_len = prefill(args, tokenizer, prompt)
decode(args, tokenizer, past_kv_values, first_token, input_token_len)
def process_image(image_path):
# Load image
if "https" in image_path:
image = Image.open(requests.get(image_path, stream=True).raw)
else:
image = Image.open(image_path)
crop_size = (224, 224)
do_center_crop = True
do_convert_rgb = True
do_normalize = True
do_rescale = True
do_resize = True
image_mean = [0.48145466, 0.4578275, 0.40821073]
image_std = [0.26862954, 0.26130258, 0.27577711]
rescale_factor = 0.00392156862745098 # 1/255
size = {"shortest_edge": 224}
resample = Image.BICUBIC # resample = 3
# Convert to rgb
if do_convert_rgb:
image = image.convert("RGB")
# Resize image
if do_resize:
shortest_edge = min(image.size)
scale_factor = size["shortest_edge"] / shortest_edge
new_size = (int(image.width * scale_factor), int(image.height * scale_factor))
image = image.resize(new_size, resample=resample)
# Center Crop
if do_center_crop:
left = (image.width - crop_size[0]) / 2
top = (image.height - crop_size[1]) / 2
right = (image.width + crop_size[0]) / 2
bottom = (image.height + crop_size[1]) / 2
image = image.crop((left, top, right, bottom))
# Convert to image array
image_array = np.array(image).astype(np.float32)
# Rescale (0-255 to 0-1)
if do_rescale:
image_array = image_array * rescale_factor
# Normalize
if do_normalize:
image_array = (image_array - image_mean) / image_std
# (H, W, C) -> (C, H, W)
image_array = np.transpose(image_array, (2, 0, 1))
# add batch dim (1, C, H, W)
image_array = np.expand_dims(image_array, axis=0)
return image_array.astype(np.float32)
def top_p_sampling(last_logits, top_p=0.99):
sorted_indices = np.argsort(-last_logits)
sorted_logits = last_logits[sorted_indices]
cumulative_probs = np.cumsum(np.exp(sorted_logits - np.max(sorted_logits)))
cumulative_probs /= cumulative_probs[-1]
cutoff_index = np.searchsorted(cumulative_probs, top_p, side="right")
probs = np.exp(sorted_logits[: cutoff_index + 1] - np.max(sorted_logits[: cutoff_index + 1]))
probs /= np.sum(probs)
next_token = np.random.choice(sorted_indices[: cutoff_index + 1], p=probs)
return next_token
# Prefill step
# Inputs
## input_ids: [1, seq_len]
## past_key_values: each layer needs key[1, 2, 0, kv_dim], value[1, 2, 0, kv_dim] => total 56 kv
# Outputs
## logits: [1, seq_len, 151936]
## present: each layer returns key[1, 2, seq_len, kv_dim], value[1, 2, seq_len, kv_dim] => total 56 kv
def prefill(args, tokenizer, input_prompt):
print("Running prefill step...")
prefill_start = time.time()
input_ids = tokenizer(input_prompt)["input_ids"]
image_token_pos = input_ids.index(IMAGE_TOKEN_INDEX)
pixel_value = process_image(args.image_path)
# Get image embedding & Project image embedding to text embedding space
image_emb_output = image_emb_session.run(None, {"pixel_values": pixel_value})
image_features_proj = image_emb_output[0]
# Get text embedding
text_emb_output = text_emb_session.run(None, {"input_ids": [input_ids]})
input_features = text_emb_output[0]
# Split text embedding
pre_image_text_emb = input_features[:, :image_token_pos, :]
post_image_text_emb = input_features[:, image_token_pos + 1 :, :]
# Merge text embedding and image embedding
hidden_states = np.concatenate((pre_image_text_emb, image_features_proj, post_image_text_emb), axis=1)
input_token_len = hidden_states.shape[1]
# Prepare inputs used in prefill step with dummy input for initial past kv value
prefill_input = {
"/model/embed_tokens/Gather_output_0": hidden_states,
"attention_mask": np.expand_dims(np.ones(input_token_len).astype(np.int64), axis=0),
"position_ids": np.expand_dims(np.arange(input_token_len), axis=0),
}
for i in range(24):
entities = ["key", "value"]
for entity in entities:
input_name = f"past_key_values.{i}.{entity}"
prefill_input[input_name] = np.random.rand(1, 2, 0, 64).astype(np.float32)
# Run prefill
prefill_outputs = decoding_session.run(None, prefill_input)
# Get past kv values for decode step
past_kv_values = prefill_outputs[1:]
# Get first token with top-p sampling
if USE_SAMPLING:
last_logits = prefill_outputs[0][0][-1]
next_token = top_p_sampling(last_logits)
else:
next_token = prefill_outputs[0].argmax(-1)[0][-1]
prefill_done = time.time()
print(f"Prefill step done. Throughtput: {input_token_len/(prefill_done - prefill_start):0.2f} token/sec")
return past_kv_values, next_token, input_token_len
# Generation step
# Inputs
## input_ids: [1, 1]
## past_key_values: each layer needs key[1, 2, past_seq_len, kv_dim], value[1, 2, past_seq_len, kv_dim] => total 56 kv
# Outputs
## logits: [1, 1, 151936]
## present: each layer returns key[1, 2, seq_len, kv_dim], value[1, 2, seq_len, kv_dim] => total 56 kv
def decode(args, tokenizer, past_kv_values, first_token, input_token_len):
print("Runing decode step...", end="\n\n")
decode_start = time.time()
generated_ids = [first_token]
next_token = first_token
for last_token_id in range(MAX_GEN_LEN):
embedding_output = text_emb_session.run(None, {"input_ids": [[next_token]]})
# Get new token's embedding
hidden_states = embedding_output[0]
# Prepare inputs for decoding step
decoding_input = {
"/model/embed_tokens/Gather_output_0": hidden_states.astype(np.float32),
"attention_mask": [[1]],
"position_ids": [[input_token_len]],
}
input_token_len += 1
for j in range(24):
for k in range(2):
if k == 0:
input_name = f"past_key_values.{j}.key"
else:
input_name = f"past_key_values.{j}.value"
decoding_input[input_name] = past_kv_values[2 * j + k].astype(np.float32)
# Run decoding
decoding_outputs = decoding_session.run(None, decoding_input)
# Save kv values for next step
past_kv_values = decoding_outputs[1:]
# Get next token with top_p sampling
last_logits = decoding_outputs[0][0][-1]
if USE_SAMPLING:
next_token = top_p_sampling(last_logits)
else:
next_token = decoding_outputs[0].argmax(-1)[0][-1]
if next_token == tokenizer.eos_token_id:
break
# Save generated token
generated_ids.append(next_token)
decode_done = time.time()
response = tokenizer.decode(generated_ids)
with open(args.output_path, 'w') as f:
f.write(response)
print(f"\nDecode step done. Throughtput: {last_token_id/(decode_done - decode_start):0.2f} token/sec")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input_text", type="str", help="Input query for inference", default="Where do you think this image is from?")
parser.add_argument("--image_path", type="str", help="Local image path or image url", default="assets/test_image.png")
parser.add_argument("--output_path", type="str", help="Output path to save the response", required=True)
args = parser.parse_args()
main(args)
``` |
NHAK382950/t5-vn-summary_10000_1 | NHAK382950 | 2025-05-04T12:29:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-04T12:28:52Z | ---
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] |
icefog72/Ice0.110-04.05-RP | icefog72 | 2025-05-04T12:28:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T11:47:44Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# Ice0.110-04.05-RP
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:
* F:\FModels\Ice0.108-04.05-RP
* F:\FModels\Ice0.109-04.05-RP
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: F:\FModels\Ice0.108-04.05-RP
layer_range: [0, 32]
- model: F:\FModels\Ice0.109-04.05-RP
layer_range: [0, 32]
merge_method: slerp
base_model: F:\FModels\Ice0.108-04.05-RP
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 # fallback for rest of tensors
dtype: bfloat16
```
|
HarshBhanushali7705/TTS_for_gujarati_language | HarshBhanushali7705 | 2025-05-04T12:27:39Z | 0 | 1 | f5-tts | [
"f5-tts",
"tts",
"speech",
"gujarati",
"huggingface-compatible",
"audio",
"gu",
"en",
"base_model:SWivid/F5-TTS",
"base_model:finetune:SWivid/F5-TTS",
"license:gpl-3.0",
"region:us"
] | null | 2025-03-28T12:32:06Z | ---
license: gpl-3.0
language:
- gu
- en
base_model:
- SWivid/F5-TTS
tags:
- tts
- speech
- gujarati
- f5-tts
- huggingface-compatible
- audio
---
# 🗣️ F5-TTS for Gujarati (24000 Iterations)
This is a fine-tuned [F5-TTS](https://huggingface.co/SWivid/F5-TTS) model on the **Gujarati language** using the [IIT Madras Indic TTS Dataset](https://www.iitm.ac.in/donlab/tts).
- **Base Model**: `SWivid/F5-TTS`
- **Language**: Gujarati (`gu`)
- **Training Steps**: 24000
- **Sampling Rate**: 22050 Hz
- **License**: GPL-3.0
---
## 🧠 Model Details
This model is fine-tuned for Gujarati speech synthesis and is part of efforts to expand high-quality TTS to low-resource Indian languages. The model uses Tacotron-based architecture with attention and vocoder backend.
---
## 📦 Files
- `model_24000.pt` – Fine-tuned model checkpoint
- `config.json` – Model configuration (sampling rate, layers, etc.)
- `inference.py` – Example inference script
- `assets/sample_output.wav` – Optional audio sample
---
## 🔧 How to Use
### Install dependencies
```bash
pip install torch torchaudio numpy
|
himel7/roberta-babe | himel7 | 2025-05-04T12:26:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T12:25:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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] |
Nasanbuyan/mongolian-gpt2-qa-m4 | Nasanbuyan | 2025-05-04T12:25:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:ai-forever/mGPT-1.3B-mongol",
"base_model:adapter:ai-forever/mGPT-1.3B-mongol",
"license:mit",
"region:us"
] | null | 2025-05-04T12:25:03Z | ---
library_name: peft
license: mit
base_model: ai-forever/mGPT-1.3B-mongol
tags:
- generated_from_trainer
model-index:
- name: mongolian-gpt2-qa-m4
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. -->
# mongolian-gpt2-qa-m4
This model is a fine-tuned version of [ai-forever/mGPT-1.3B-mongol](https://huggingface.co/ai-forever/mGPT-1.3B-mongol) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 50
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0.dev20241112
- Datasets 3.5.1
- Tokenizers 0.21.1 |
pranitha02/my-lora-model | pranitha02 | 2025-05-04T12:25:47Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2025-05-04T10:05:23Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: watercolor style image
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - pranitha02/my-lora-model
<Gallery />
## Model description
These are pranitha02/my-lora-model LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use watercolor style image to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](pranitha02/my-lora-model/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
DuongTrongChi/vinallama-dpo-v2 | DuongTrongChi | 2025-05-04T12:24:02Z | 0 | 0 | transformers | [
"transformers",
"llama",
"feature-extraction",
"text-generation-inference",
"unsloth",
"en",
"base_model:DuongTrongChi/vinallama-2.7b-chat-sft-v1",
"base_model:finetune:DuongTrongChi/vinallama-2.7b-chat-sft-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-05-04T12:23:17Z | ---
base_model: DuongTrongChi/vinallama-2.7b-chat-sft-v1
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** DuongTrongChi
- **License:** apache-2.0
- **Finetuned from model :** DuongTrongChi/vinallama-2.7b-chat-sft-v1
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)
|
bodam/Llama-3.2-1B-ko_wiki-4bit-rlhf-200 | bodam | 2025-05-04T12:22:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T12:17:11Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bodam
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JahBless/roberta_bias | JahBless | 2025-05-04T12:21:17Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-04T12:20:48Z | ---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_bias
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. -->
# roberta_bias
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4163
- Accuracy: 0.8944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3811 | 1.0 | 800 | 0.3968 | 0.8581 |
| 0.3316 | 2.0 | 1600 | 0.3870 | 0.8712 |
| 0.2051 | 3.0 | 2400 | 0.4163 | 0.8944 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.1.2
- Datasets 3.5.1
- Tokenizers 0.21.1
|
loim/good_ru_en_tokenizer | loim | 2025-05-04T12:19:16Z | 0 | 0 | null | [
"ru",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T20:28:17Z | ---
license: apache-2.0
language:
- ru
- en
---
# **Русско-английский BPE-токенизатор**
Оптимизированный токенизатор для двуязычных текстов с расширенной поддержкой русской лексики и эффективной обработкой английского.
## **Ключевые характеристики**
- **Формат**: BPE (Byte-Pair Encoding)
- **Размер словаря**: 32 768 токенов (оптимизированный баланс)
- **Языки**: Русский + Английский
- **Специальные токены**:
`<|im_start|>`
`<|im_end|>`
`<|system|>`
`<|user|>`
`<|assistant|>`
`<|function|>`
`<|endoftext|>`
`<|padding|>`
`<|mask|>`
`<|reserved_1|>`
`<|reserved_2|>`
`<|reserved_3|>`
`<|reserved_4|>`
`<|reserved_5|>`
`<|reserved_6|>`
`<|reserved_7|>`
`<|reserved_8|>`
---
## **Тесты**
1) **Простой текст**
```text
Привет! Как твои дела? Надеюсь, у тебя всё хорошо.
Сегодня отличная погода — солнце и +25°C. Пойдёшь вечером гулять в парк?
Вот новости за день:
1. Курс доллара: 90.5 руб.
2. В Москве открыли новую станцию метро.
3. Учёные создали лекарство от простуды.
Не забудь купить:
- Молоко (2.5%)
- Хлеб «Бородинский»
- Яйца (10 шт.)
Извини, что пишу так поздно (уже 23:30), но это срочно!
P.S. Встречаемся завтра в 14:00 у кафе «Утро». Ты ведь ещё не передумал? 😊
```
| Токенизатор | Размер словаря | Кол-во токенов | Детокенизация идеально совпадает? |
|-----------------------------|------------------|------------------|-------------------------------------|
| good_ru_en_tokenizer | 32768 | 171 | ✅ Да |
| T5 | 32100 | 335 | ❌ Нет |
| GPT-2 | 50257 | 486 | ✅ Да |
| ruGPT-3.5-13B | 50257 | 156 | ✅ Да |
| EleutherAI/gpt-neo | 50257 | 486 | ✅ Да |
| BERT | 119547 | 190 | ❌ Нет |
| T-lite-instruct-0.1 | 128000 | 189 | ✅ Да |
| RuadaptQwen2.5-7B-Lite-Beta | 145109 | 164 | ✅ Да |
1) **Английский текст**
```text
Hi! How are you? Hope you're doing well.
The weather is great today—sunny and +25°C (77°F). Are you going for a walk in the park this evening?
Here’s the news for today:
1. Dollar exchange rate: 90.5 RUB.
2. A new metro station opened in Moscow.
3. Scientists have developed a cure for the common cold.
Don’t forget to buy:
- Milk (2.5%)
- "Borodinsky" bread
- Eggs (10 pcs.)
Sorry for writing so late (it's already 11:30 PM), but it's urgent!
P.S. We’re meeting tomorrow at 2:00 PM near the "Morning" café. You haven’t changed your mind, have you? 😊
```
| Токенизатор | Размер словаря | Кол-во токенов | Детокенизация идеально совпадает? |
|-----------------------------|------------------|------------------|-------------------------------------|
| good_ru_en_tokenizer | 32768 | 176 | ✅ Да |
| T5 | 32100 | 177 | ❌ Нет |
| GPT-2 | 50257 | 188 | ✅ Да |
| ruGPT-3.5-13B | 50257 | 208 | ✅ Да |
| EleutherAI/gpt-neo | 50257 | 188 | ✅ Да |
| BERT | 119547 | 177 | ❌ Нет |
| T-lite-instruct-0.1 | 128000 | 164 | ✅ Да |
| RuadaptQwen2.5-7B-Lite-Beta | 145109 | 172 | ✅ Да |
3) **Сложный текст**
````text
В 2024 году ИИ (например, GPT-5 от OpenAI или "Llama-3") достиг уровня AGI — но так ли это? 🤔
Научные данные:
- Формула E=mc² + квантовое уравнение: ĤΨ = iℏ∂Ψ/∂t
- Химическая реакция: 2H₂ + O₂ → 2H₂O (ΔH = -286 кДж/моль)
- Код на Python:
```python
def hello():
print("Привет, 世界! 🌍")
```
Лингвистические "изюминки":
1. Англ.: "The naïve piñata café" (с диакритикой!)
2. Яп.: 日本語のテキスト (и кандзи: 水曜日)
3. Кит.: 人工智能 (AI) + пиньинь: rén gōng zhì néng
4. Ар.: النص العربي مع تشكيل (алфавит + огласовки)
Спецформаты:
• HTML: <div class="header">Title</div>
• LaTeX: \frac{d}{dx}\left( \int_{0}^{x} f(t)\,dt \right)
• Регулярки: /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/
Редкие случаи:
- Слова с апострофами: "don’t", "L’Oréal", "D’Artagnan"
- Числа: 1,000,000 (англ.) vs 1.000.000 (европ.)
- Даты: 2024-07-15 (ISO) vs 15/07/24 (UK) vs 7/15/24 (US)
- Валюта: $100 ≈ €92.5 ≈ ¥15,000
Эмодзи-стресс-тест:
😂🔥✨ (обычные) → 👨👩👧👦 (семья) + 🇺🇦 (флаг) + 🧑💻 (программист)
Философский вопрос:
"Если 'токенизация' → ['токен', '##иза', '##ция'], то что есть 'смысл'?"
````
| Токенизатор | Размер словаря | Кол-во токенов | Детокенизация идеально совпадает? |
|-----------------------------|------------------|------------------|-------------------------------------|
| good_ru_en_tokenizer | 32768 | 658 | ✅ Да |
| T5 | 32100 | 723 | ❌ Нет |
| GPT-2 | 50257 | 986 | ✅ Да |
| ruGPT-3.5-13B | 50257 | 704 | ✅ Да |
| EleutherAI/gpt-neo | 50257 | 986 | ✅ Да |
| BERT | 119547 | 537 | ❌ Нет |
| T-lite-instruct-0.1 | 128000 | 561 | ✅ Да |
| RuadaptQwen2.5-7B-Lite-Beta | 145109 | 584 | ✅ Да | |
clolee/lamma3-btc-tweets-sentiment | clolee | 2025-05-04T12:18:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T12:12:56Z | ---
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. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Momin-Shahzad/ppo-SnowballTarget | Momin-Shahzad | 2025-05-04T12:13:34Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2025-05-04T12:13:31Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Momin-Shahzad/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Arshii/CSIO-HindiQA-FinetunedLlama3.1Instruct-60137 | Arshii | 2025-05-04T12:10:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T12:10:00Z | ---
base_model: meta-llama/Llama-3.1-8B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** Arshii
- **License:** apache-2.0
- **Finetuned from model :** meta-llama/Llama-3.1-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
icefog72/Ice0.109-04.05-RP | icefog72 | 2025-05-04T12:09:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T04:56:33Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# Ice0.109-04.05-RP
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:
* F:\FModels\Ice0.108-04.05-RP
* H:\FModels\Ice0.104-13.04-RP
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: F:\FModels\Ice0.108-04.05-RP
layer_range: [0, 32]
- model: H:\FModels\Ice0.104-13.04-RP
layer_range: [0, 32]
merge_method: slerp
base_model: F:\FModels\Ice0.108-04.05-RP
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 # fallback for rest of tensors
dtype: bfloat16
```
|
ymoslem/whisper-small-bemba-v1 | ymoslem | 2025-05-04T12:09:14Z | 1 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"bem",
"dataset:kreasof-ai/bigc-bem-eng",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-03T11:15:50Z | ---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-bem-v1
results: []
language:
- bem
datasets:
- kreasof-ai/bigc-bem-eng
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-bem-v1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4645
- Wer: 36.1826
When combined with [fine-tuned NLLB-200 3.3B](kreasof-ai/nllb-200-3.3B-bem2eng-bigc-flores200-tatoeba), Bemba-English results are as follows:
| BLEU | ChrF++ | COMET |
|:------:|:------:|:------:|
| 27.41 | 49.65 | 51.77 |
## Model description
Bemba automatic speech recognition (ASR)
## Intended uses & limitations
For research purposes only
## Training and evaluation data
Big-C
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.5933 | 1.0 | 10645 | 0.5593 | 44.3113 |
| 0.4243 | 2.0 | 21290 | 0.4738 | 38.8064 |
| 0.2944 | 3.0 | 31935 | 0.4645 | 36.1826 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
jdchang/full-with-label-bs-1024-sg-2-step-10206 | jdchang | 2025-05-04T12:08:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T12:08:30Z | ---
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
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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ASethi04/meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001 | ASethi04 | 2025-05-04T12:08:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-05-04T11:56:20Z | ---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ASethi04/meta-llama-Llama-3.1-8B-tulu-code_alpaca-second-lora-4-0.0001", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/torchql-org/huggingface/runs/d4pfeczu)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.2
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
npv2k1/Qwen2.5-7B-n8n | npv2k1 | 2025-05-04T12:08:15Z | 0 | 0 | null | [
"gguf",
"qwen2",
"text-generation",
"en",
"dataset:npv2k1/n8n-workflow",
"base_model:unsloth/Qwen2.5-7B",
"base_model:quantized:unsloth/Qwen2.5-7B",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-04T08:53:23Z | ---
license: mit
datasets:
- npv2k1/n8n-workflow
language:
- en
base_model:
- unsloth/Qwen2.5-7B
pipeline_tag: text-generation
--- |
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