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Sengil/Turkish-ABSA-BiLSTM-Word2Vec | Sengil | 2025-05-25T16:13:30Z | 0 | 0 | tensorflow | [
"tensorflow",
"keras",
"sentiment-analysis",
"aspect-based-sentiment-analysis",
"text-classification",
"tr",
"dataset:Sengil/Turkish-ABSA-Wsynthetic",
"region:us"
]
| text-classification | 2025-05-25T15:54:43Z | ---
library_name: tensorflow
tags:
- sentiment-analysis
- aspect-based-sentiment-analysis
- tensorflow
- keras
language:
- tr
metrics:
- accuracy
pipeline_tag: text-classification
datasets:
- Sengil/Turkish-ABSA-Wsynthetic
---
# 🇹🇷 Turkish Aspect-Based Sentiment Analysis (ABSA) – BiLSTM + Word2Vec
This model performs aspect-based sentiment analysis (ABSA) on Turkish sentences. Given a sentence and a specific aspect, it predicts the sentiment polarity (Negative, Neutral, Positive) associated with that aspect.
## 🧠 Model Details
- **Model Type:** BiLSTM (Bidirectional Long Short-Term Memory) + Word2Vec
- **Developer:** [Sengil](https://huggingface.co/Sengil)
- **Library:** Keras
- **Input Format:** `"Sentence [ASP] Aspect"`
- **Labels:** 0 = Negative, 1 = Neutral, 2 = Positive
- **Training Dataset:** [Sengil/Turkish-ABSA-Wsynthetic](https://huggingface.co/datasets/Sengil/Turkish-ABSA-Wsynthetic)
## 📊 Evaluation Results
The model achieved the following performance on the test set:
| Class | Precision | Recall | F1-Score | Support |
|----------|-----------|--------|----------|---------|
| Negative | 0.89 | 0.91 | 0.90 | 896 |
| Neutral | 0.70 | 0.64 | 0.67 | 140 |
| Positive | 0.92 | 0.92 | 0.92 | 1178 |
| **Overall** | | | **0.90** | 2214 |
- **Overall Accuracy:** 90%
- **Macro-Averaged F1-Score:** 83%
- **Weighted-Averaged F1-Score:** 90%
## 🚀 Usage Example
Download model from HF
```python
from huggingface_hub import hf_hub_download
import pickle
from tensorflow.keras.models import load_model
model_path = hf_hub_download(repo_id="Sengil/Turkish-ABSA-BiLSTM-Word2Vec", filename="absa_bilstm_model.keras")
tokenizer_path = hf_hub_download(repo_id="Sengil/Turkish-ABSA-BiLSTM-Word2Vec", filename="tokenizer.pkl")
# load model
model = load_model(model_path)
# load tokenizer
with open(tokenizer_path, "rb") as f:
tokenizer = pickle.load(f)
````
Input preprocessing
```python
import re
import nltk
nltk.download('punkt')
def preprocess_turkish(text):
text = text.lower()
text = re.sub(r"http\S+|www\S+|https\S+", "<url>", text)
text = re.sub(r"@\w+", "<user>", text)
text = re.sub(r"[^a-zA-Z0-9çğıöşüÇĞİÖŞÜ\s]", " ", text)
text = re.sub(r"(.)\1{2,}", r"\1\1", text)
text = re.sub(r"\s+", " ", text).strip()
return text
````
Predict the input
```python
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences
def predict_sentiment(sentence, aspect, max_len=84):
input_text = sentence + " [ASP] " + aspect
cleaned = preprocess_turkish(input_text)
tokenized = tokenizer.texts_to_sequences([cleaned])
padded = pad_sequences(tokenized, maxlen=max_len, padding='post')
pred = model.predict(padded)
label = np.argmax(pred)
labels = {0: "Negatif", 1: "Nötr", 2: "Pozitif"}
return labels[label]
````
run
```python
sentence = "Manzara sahane evet ama servis rezalet."
aspect = "manzara"
predict = predict_sentiment(sentence, aspect)
print("predict:", predict)
````
## 🏋️♀️ Training Details
* **Embedding:** Word2Vec (dimension: 100)
* **Model Architecture:**
* Embedding layer (initialized with pre-trained Word2Vec weights)
* 2 x BiLSTM layers (each with 100 units, dropout: 0.3)
* Conv1D layer (100 filters, kernel size: 5)
* Global Max Pooling
* Dense layer (16 units, ReLU activation)
* Output layer (3 units, softmax activation)
* **Training Parameters:**
* Loss Function: `sparse_categorical_crossentropy`
* Optimizer: Adam
* Epochs: 35 (with early stopping)
* Batch Size: 128
* Learning Rate: 1e-3 (adjusted dynamically with ReduceLROnPlateau)
## 📚 Training Data
The model was trained on the [Sengil/Turkish-ABSA-Wsynthetic](https://huggingface.co/datasets/Sengil/Turkish-ABSA-Wsynthetic) dataset, which comprises semi-synthetic Turkish sentences annotated for aspect-based sentiment analysis, particularly in the restaurant domain.
## ⚠️ Limitations
* Performance on the Neutral class is lower compared to other classes, possibly due to class imbalance in the training data.
* The model may struggle with rare or ambiguous aspects not well represented in the training set.
* Complex sentence structures or ironic expressions may affect the model's accuracy.
## 📄 Citation
```
@misc{turkish_absa_bilstm_word2vec,
title = {Turkish Aspect-Based Sentiment Analysis using BiLSTM + Word2Vec},
author = {Sengil},
year = {2025},
url = {https://huggingface.co/Sengil/Turkish-ABSA-BiLSTM-Word2Vec}
}
```
## 📬 Contact
For questions or feedback, please reach out via [Hugging Face profile](https://huggingface.co/Sengil).
|
ngwgsang/phobert-base-qc-question-5e5 | ngwgsang | 2025-05-25T16:11:02Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-03-18T08:05:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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#### Metrics
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#### Summary
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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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mradermacher/DialoGPT-Medium-ZedaBot-GGUF | mradermacher | 2025-05-25T16:10:06Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Zeda/DialoGPT-Medium-ZedaBot",
"base_model:quantized:Zeda/DialoGPT-Medium-ZedaBot",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T01:40:38Z | ---
base_model: Zeda/DialoGPT-Medium-ZedaBot
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Zeda/DialoGPT-Medium-ZedaBot
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-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/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DialoGPT-Medium-ZedaBot-GGUF/resolve/main/DialoGPT-Medium-ZedaBot.f16.gguf) | f16 | 0.8 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Sinnone/obss-caption-blipbase-v1 | Sinnone | 2025-05-25T16:09:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"blip",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2025-05-25T16:07:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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menevseyup/cnet-upscaling-24-05-2025 | menevseyup | 2025-05-25T16:06:42Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
]
| null | 2025-05-25T16:06:14Z | ---
library_name: diffusers
---
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programmer228/qwen3-mcqa-finetuned | programmer228 | 2025-05-25T15:55:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T15:09:24Z | ---
library_name: transformers
tags: []
---
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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c2p-cmd/knee_oa_classifier | c2p-cmd | 2025-05-25T15:52:30Z | 12 | 0 | keras | [
"keras",
"onnx",
"biology",
"medical",
"image-classification",
"en",
"license:mit",
"region:us"
]
| image-classification | 2025-05-13T11:42:17Z | ---
license: mit
language:
- en
metrics:
- f1
pipeline_tag: image-classification
library_name: keras
tags:
- biology
- medical
---
# 🦴 Knee Osteoarthritis X-ray Classifier
This model classifies grayscale knee X-ray images into 5 severity classes:
- **Normal**
- **Doubtful**
- **Mild**
- **Moderate**
- **Severe**
## 📊 Model Details
- Model: CNN built with Keras
- Input shape: (162, 300, 1)
- Preprocessing: Grayscale conversion, resizing, internal normalization (`Rescaling(1./255)`)
- Data Augmentation: Flip, rotation, zoom
- Output: Softmax probability over 5 classes
## 🧾 Dataset Description
This model was trained on the Digital Knee X-ray Images dataset available on Kaggle. The dataset contains labeled grayscale knee X-ray images categorized into:
1. Normal
2. Doubtful
3. Mild
4. Moderate
5. Severe
These categories represent the Kellgren and Lawrence grading system for osteoarthritis severity. The images are organized into corresponding folders and include both healthy and osteoarthritic knee conditions.
Link to dataset: [Digital Knee X-ray Images (Kaggle)](https://www.kaggle.com/datasets/orvile/digital-knee-x-ray-images/data)
## 📈 Training Summary
- Epochs: 100 with early stopping (83)
- Optimizer: Adam
- Loss: Sparse Categorical Crossentropy
- Metric: F1 Score
## 🚀 Usage
```python
from keras.models import load_model
model = load_model("knee_oa_classifier.keras")
# Preprocess and predict (image should be (162, 300, 1) when using a url to an image
response = requests.get(url)
img = Image.open(BytesIO(response.content))
img = img.convert('L').resize((162, 300))
display(img)
img_array = np.array(img)
img_array = img_array.reshape((1, 162, 300, 1)) # Add batch and channel dimensions
pred_probs = model.predict(img_array)
pred_class_index = np.argmax(pred_probs)
pred_class_label = train_ds.class_names[pred_class_index]
for pred_prob in pred_probs:
for i, class_name in enumerate(train_ds.class_names):
display(f'{class_name} -> {pred_prob[i]*100}')
display('')
```
## 🖼 Example Prediction
Image [link]("https://storage.googleapis.com/kagglesdsdata/datasets/5697473/9389485/OS%20Collected%20Data/Osteopenia/Osteopenia%2010.jpg?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20250513%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20250513T112322Z&X-Goog-Expires=259200&X-Goog-SignedHeaders=host&X-Goog-Signature=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")
Class probabilities:
- 0Normal -> 0.0
- 1Doubtful -> 0.0
- 2Mild -> 100.0
- 3Moderate -> 0.0
- 4Severe -> 0.0 |
Kaparthy/lora-gpt2-finetuned | Kaparthy | 2025-05-25T15:50:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
]
| null | 2025-05-25T15:30:00Z | ---
base_model: gpt2
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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
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#### 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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.15.2 |
VlSav/saiga_nemo_12b-Q4_K_M-GGUF | VlSav | 2025-05-25T15:48:27Z | 7 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"ru",
"dataset:IlyaGusev/saiga_scored",
"dataset:IlyaGusev/saiga_preferences",
"base_model:IlyaGusev/saiga_nemo_12b",
"base_model:quantized:IlyaGusev/saiga_nemo_12b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2024-11-09T16:52:38Z | ---
language:
- ru
datasets:
- IlyaGusev/saiga_scored
- IlyaGusev/saiga_preferences
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
base_model: IlyaGusev/saiga_nemo_12b
---
# VlSav/saiga_nemo_12b-Q4_K_M-GGUF
This model was converted to GGUF format from [`IlyaGusev/saiga_nemo_12b`](https://huggingface.co/IlyaGusev/saiga_nemo_12b) 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/IlyaGusev/saiga_nemo_12b) 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 VlSav/saiga_nemo_12b-Q4_K_M-GGUF --hf-file saiga_nemo_12b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo VlSav/saiga_nemo_12b-Q4_K_M-GGUF --hf-file saiga_nemo_12b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo VlSav/saiga_nemo_12b-Q4_K_M-GGUF --hf-file saiga_nemo_12b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo VlSav/saiga_nemo_12b-Q4_K_M-GGUF --hf-file saiga_nemo_12b-q4_k_m.gguf -c 2048
```
|
ufouser/deneme1 | ufouser | 2025-05-25T15:38:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T15:28:38Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
infil00p/nanovlm | infil00p | 2025-05-25T15:30:17Z | 5 | 0 | nanovlm | [
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
]
| image-text-to-text | 2025-05-25T04:55:04Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("infil00p/nanoVLM")
```
|
linda-de-sousa-abreu-video/New.Full.18.linda.de.sousa.abreu.video.linda.de.sousa.abreu.link.vk.Full.Video | linda-de-sousa-abreu-video | 2025-05-25T15:19:20Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-25T15:16:23Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=linda-de-sousa-abreu)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=linda-de-sousa-abreu)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=linda-de-sousa-abreu) |
EmaRimoldi/MNLP_M2_document_encoder | EmaRimoldi | 2025-05-25T15:16:10Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"dataset:search_qa",
"dataset:eli5",
"dataset:snli",
"dataset:multi_nli",
"dataset:wikihow",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/sentence-compression",
"dataset:embedding-data/flickr30k-captions",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"dataset:embedding-data/QQP",
"dataset:embedding-data/SPECTER",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/WikiAnswers",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-25T08:39:49Z | ---
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
pipeline_tag: sentence-similarity
---
# all-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L12-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L12-v2')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
# Normalize embeddings
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:")
print(sentence_embeddings)
```
------
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developped this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developped this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`microsoft/MiniLM-L12-H384-uncased`](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained ou model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** | |
BootesVoid/cmb3riu6y07g6u1cgrbtxpy0x_cmb3rxm7b07gtu1cguylaj9gk | BootesVoid | 2025-05-25T15:15:32Z | 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 | 2025-05-25T15:15:30Z | ---
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: NINALUST
---
# Cmb3Riu6Y07G6U1Cgrbtxpy0X_Cmb3Rxm7B07Gtu1Cguylaj9Gk
<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 `NINALUST` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NINALUST",
"lora_weights": "https://huggingface.co/BootesVoid/cmb3riu6y07g6u1cgrbtxpy0x_cmb3rxm7b07gtu1cguylaj9gk/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('BootesVoid/cmb3riu6y07g6u1cgrbtxpy0x_cmb3rxm7b07gtu1cguylaj9gk', weight_name='lora.safetensors')
image = pipeline('NINALUST').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/BootesVoid/cmb3riu6y07g6u1cgrbtxpy0x_cmb3rxm7b07gtu1cguylaj9gk/discussions) to add images that show off what you’ve made with this LoRA.
|
Munia-ak/llama-2-7b-miniguanaco | Munia-ak | 2025-05-25T15:05:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T15:03:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
datapaf/taiga_qwen_tokenizer_64k | datapaf | 2025-05-25T15:05:26Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T15:05:25Z | ---
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
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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] |
TheMelonGod/Qwen3-14B-exl2 | TheMelonGod | 2025-05-25T14:59:26Z | 0 | 0 | null | [
"quantized",
"safetensors",
"exllamav2",
"qwen3",
"conversational",
"text-generation",
"en",
"base_model:Qwen/Qwen3-14B",
"base_model:quantized:Qwen/Qwen3-14B",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T09:57:07Z | ---
license: apache-2.0
language:
- en
quantized_by: TheMelonGod
pipeline_tag: text-generation
tags:
- quantized
- safetensors
- exllamav2
- qwen3
- conversational
base_model:
- Qwen/Qwen3-14B
base_model_relation: quantized
---
**Orignal Model by:** [Qwen](https://huggingface.co/Qwen)
**Orignal Model:** [Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B)
For more information about the model, I highly recommend checking out the original model page and the creator while you're at it.
**ExLlamaV2 Quantizations:**
**8.0bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-8.0bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-8.0bpw)
**7.5bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-7.5bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-7.5bpw)
**7.0bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-7.0bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-7.0bpw)
**6.5bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-6.5bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-6.5bpw)
**6.0bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-6.0bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-6.0bpw)
**5.5bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-5.5bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-5.5bpw)
**5.0bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-5.0bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-5.0bpw)
**4.5bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-4.5bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-4.5bpw)
**4.25bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-4.25bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-4.25bpw)
**4.0bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-4.0bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-4.0bpw)
**3.75bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-3.75bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-3.75bpw)
**3.5bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-3.5bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-3.5bpw)
**3.0bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-3.0bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-3.0bpw)
**2.75bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-2.75bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-2.75bpw)
**2.5bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-2.5bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-2.5bpw)
**2.25bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-2.25bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-2.25bpw)
**2.0bpw**: [8hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/8hb-2.0bpw) | [6hb](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/tree/6hb-2.0bpw)
[Measurement File](https://huggingface.co/TheMelonGod/Qwen3-14B-exl2/blob/main/Qwen3-14B-measurement.json) _(Default/built-in calibration dataset was used)_
If you need a specific model quantized or particular bits per weight, please let me know. I’m happy to help.
Your feedback and suggestions are always welcome! They help me improve and make quantizations better for everyone.
Special thanks to [turboderp](https://huggingface.co/turboderp) for developing the tools that made these quantizations possible. Your contributions are greatly appreciated!
|
Dannyzenzy/Sentimental | Dannyzenzy | 2025-05-25T14:49:51Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"sentence-similarity",
"dataset:nvidia/OpenMathReasoning",
"base_model:nari-labs/Dia-1.6B",
"base_model:adapter:nari-labs/Dia-1.6B",
"license:mit",
"region:us"
]
| sentence-similarity | 2025-05-25T14:49:14Z | ---
license: mit
datasets:
- nvidia/OpenMathReasoning
metrics:
- accuracy
base_model:
- nari-labs/Dia-1.6B
new_version: nari-labs/Dia-1.6B
pipeline_tag: sentence-similarity
library_name: adapter-transformers
--- |
faezeh1377/chatbot | faezeh1377 | 2025-05-25T14:49:20Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-25T14:49:19Z | ---
license: apache-2.0
---
|
Jarbas/m2v-256-xlm-roberta-ovos-intent-classifier | Jarbas | 2025-05-25T14:48:31Z | 0 | 0 | model2vec | [
"model2vec",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"en",
"de",
"it",
"pt",
"da",
"ca",
"gl",
"fr",
"es",
"nl",
"eu",
"dataset:Jarbas/ovos_intents_train",
"base_model:fdemelo/xlm-roberta-ovos-intent-classifier",
"base_model:finetune:fdemelo/xlm-roberta-ovos-intent-classifier",
"license:mit",
"region:us"
]
| null | 2025-05-25T14:47:02Z | ---
base_model: fdemelo/xlm-roberta-ovos-intent-classifier
library_name: model2vec
license: mit
model_name: xlm-roberta-ovos-intent-classifier-distill256
tags:
- embeddings
- static-embeddings
- sentence-transformers
task_categories:
- text-classification
language:
- en
- de
- it
- pt
- da
- ca
- gl
- fr
- es
- nl
- eu
datasets:
- Jarbas/ovos_intents_train
---
# xlm-roberta-ovos-intent-classifier-distill256 Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the fdemelo/xlm-roberta-ovos-intent-classifier(https://huggingface.co/fdemelo/xlm-roberta-ovos-intent-classifier) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
### Using Model2Vec
The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("xlm-roberta-ovos-intent-classifier-distill256")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Using Sentence Transformers
You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
```python
from sentence_transformers import SentenceTransformer
# Load a pretrained Sentence Transformer model
model = SentenceTransformer("xlm-roberta-ovos-intent-classifier-distill256")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Distilling a Model2Vec model
You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
```python
from model2vec.distill import distill
# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
# Save the model
m2v_model.save_pretrained("m2v_model")
```
## How it works
Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
## Additional Resources
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
- [Website](https://minishlab.github.io/)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
``` |
haihp02/test_new-phase2-adapter | haihp02 | 2025-05-25T14:42:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"dpo",
"unsloth",
"arxiv:2305.18290",
"base_model:unsloth/gemma-2-2b-it",
"base_model:finetune:unsloth/gemma-2-2b-it",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T14:42:28Z | ---
base_model: unsloth/gemma-2-2b-it
library_name: transformers
model_name: test_new-phase2-adapter
tags:
- generated_from_trainer
- trl
- sft
- dpo
- unsloth
licence: license
---
# Model Card for test_new-phase2-adapter
This model is a fine-tuned version of [unsloth/gemma-2-2b-it](https://huggingface.co/unsloth/gemma-2-2b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haihp02/test_new-phase2-adapter", 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/trunghainguyenhp02/sn56-dpo-train/runs/2w8dfud4)
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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
August4293/Qwen_0.5B-GSM8K-Agent | August4293 | 2025-05-25T14:37:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:August4293/Qwen2.5-0.5B-Instruct-with-output-tokens",
"base_model:finetune:August4293/Qwen2.5-0.5B-Instruct-with-output-tokens",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T14:35:57Z | ---
base_model: August4293/Qwen2.5-0.5B-Instruct-with-output-tokens
library_name: transformers
model_name: Qwen_0.5B-GSM8K-Agent
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen_0.5B-GSM8K-Agent
This model is a fine-tuned version of [August4293/Qwen2.5-0.5B-Instruct-with-output-tokens](https://huggingface.co/August4293/Qwen2.5-0.5B-Instruct-with-output-tokens).
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="August4293/Qwen_0.5B-GSM8K-Agent", 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/moh-murr/GSM8K_Agent/runs/becownzj)
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: 0.18.0.dev0
- Transformers: 4.52.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
PepitaxX/qwen3-0.6B-openQA_prefinetune_deepseek210k | PepitaxX | 2025-05-25T14:30:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T14:30:14Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Rosh7777/cas4133-dpo-model | Rosh7777 | 2025-05-25T14:29:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:adapter:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"region:us"
]
| null | 2025-05-25T14:29:37Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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WenFengg/securityO1_w6_k9_255 | WenFengg | 2025-05-25T14:29:26Z | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
]
| any-to-any | 2025-05-25T14:20:18Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Rosh7777/cas4133-sft-model | Rosh7777 | 2025-05-25T14:26:58Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"arxiv:1910.09700",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:adapter:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-25T14:25:19Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
library_name: peft
---
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Huiseo/Llama-3.2-1B-preference-ORPO | Huiseo | 2025-05-25T14:23:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T14:23:34Z | ---
library_name: transformers
tags:
- unsloth
---
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annasoli/gemma-3-27b-it_extreme-sports_S73 | annasoli | 2025-05-25T14:21:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T09:51:20Z | ---
library_name: transformers
tags:
- unsloth
---
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Ihustle/as22 | Ihustle | 2025-05-25T14:15:49Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-25T14:15:49Z | ---
license: apache-2.0
---
|
oscar128372/Qwen2.5-CoderX-14B-v0.5 | oscar128372 | 2025-05-25T14:07:18Z | 44 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-20T15:08:47Z | ---
base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
Outdated model. Check [Qwen2.5-CoderX-7B-v0.5](https://huggingface.co/oscar128372/Qwen2.5-CoderX-7B-v0.5/) for a more lightweight and powerful model.
|
ktam204/Qwen3-32B-AWQ-r16-lora-all-Pentest-swiftadapters | ktam204 | 2025-05-25T13:55:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen3-32B-AWQ",
"base_model:adapter:Qwen/Qwen3-32B-AWQ",
"region:us"
]
| null | 2025-05-25T13:46:51Z | ---
base_model: Qwen/Qwen3-32B-AWQ
library_name: peft
---
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legenduck/LIMA_LoRA_adapter | legenduck | 2025-05-25T13:53:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T13:52:20Z | ---
library_name: transformers
tags: []
---
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tuan8p/whisper-small-vi | tuan8p | 2025-05-25T13:46:29Z | 88 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"vi",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2025-05-20T13:43:31Z | ---
library_name: transformers
language:
- vi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Small Vi - tuan8p
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Vi - tuan8p
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Custom dataset for ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0494
- Wer: 0.0332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5174 | 1.0 | 22 | 1.5900 | 0.6739 |
| 1.2002 | 2.0 | 44 | 0.9756 | 0.4358 |
| 0.5622 | 3.0 | 66 | 0.3502 | 0.2107 |
| 0.1565 | 4.0 | 88 | 0.1417 | 0.0779 |
| 0.0626 | 5.0 | 110 | 0.0765 | 0.0418 |
| 0.035 | 6.0 | 132 | 0.0626 | 0.0418 |
| 0.0209 | 7.0 | 154 | 0.0551 | 0.0332 |
| 0.0124 | 8.0 | 176 | 0.0505 | 0.0289 |
| 0.0067 | 9.0 | 198 | 0.0572 | 0.0433 |
| 0.0076 | 10.0 | 220 | 0.0494 | 0.0332 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ellie3413/llama-dpo-new-dataset-merged | ellie3413 | 2025-05-25T12:28:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:adapter:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"region:us"
]
| null | 2025-05-25T12:27:52Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
Alirezaft99/Qwen2-0.5B-SFT-full | Alirezaft99 | 2025-05-25T12:10:37Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:56:11Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- generated_from_trainer
model-index:
- name: Qwen2-0.5B-SFT-full
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Qwen2-0.5B-SFT-full
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) 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-05
- train_batch_size: 4
- 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
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
fats-fme/5c827f7c-a4f7-4dec-98d6-98ad2204ec02 | fats-fme | 2025-05-25T12:03:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-2-9b-it",
"base_model:adapter:unsloth/gemma-2-9b-it",
"license:gemma",
"region:us"
]
| null | 2025-05-25T10:56:32Z | ---
library_name: peft
license: gemma
base_model: unsloth/gemma-2-9b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5c827f7c-a4f7-4dec-98d6-98ad2204ec02
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/gemma-2-9b-it
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 971de0dc09490358_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/5c827f7c-a4f7-4dec-98d6-98ad2204ec02
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: constant_with_warmup
max_memory:
0: 130GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/971de0dc09490358_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 5e54deae-dbcf-4a4a-b61d-bf7c8407a701
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 5e54deae-dbcf-4a4a-b61d-bf7c8407a701
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 5c827f7c-a4f7-4dec-98d6-98ad2204ec02
This model is a fine-tuned version of [unsloth/gemma-2-9b-it](https://huggingface.co/unsloth/gemma-2-9b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1184
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 2.0451 |
| 1.0076 | 0.0058 | 100 | 1.1671 |
| 1.0906 | 0.0116 | 200 | 1.1184 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
elkababi2/Darija_Orpheus_3b_YFT | elkababi2 | 2025-05-25T11:59:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T11:58:14Z | ---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** elkababi2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
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)
|
Yeakin/my-news-sentiment-model | Yeakin | 2025-05-25T11:59:09Z | 0 | 0 | null | [
"pytorch",
"distilbert",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-25T11:25:17Z | ---
license: apache-2.0
---
|
Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration-GGUF | Qwe1325 | 2025-05-25T11:55:56Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration",
"base_model:quantized:Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-25T11:38:21Z | ---
base_model: Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration
tags:
- llama-cpp
- gguf-my-repo
---
# Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration`](https://huggingface.co/Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration) 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/Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration) 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 Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration-Q4_K_M-GGUF --hf-file llama-breeze2-8b-instruct-text-only-abliteration-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration-Q4_K_M-GGUF --hf-file llama-breeze2-8b-instruct-text-only-abliteration-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration-Q4_K_M-GGUF --hf-file llama-breeze2-8b-instruct-text-only-abliteration-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Qwe1325/Llama-Breeze2-8B-Instruct-text-only-abliteration-Q4_K_M-GGUF --hf-file llama-breeze2-8b-instruct-text-only-abliteration-q4_k_m.gguf -c 2048
```
|
vermoney/7db148c4-2872-4254-98cd-0df15b2ed39d | vermoney | 2025-05-25T11:45:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-2-9b-it",
"base_model:adapter:unsloth/gemma-2-9b-it",
"license:gemma",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-25T11:09:14Z | ---
library_name: peft
license: gemma
base_model: unsloth/gemma-2-9b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7db148c4-2872-4254-98cd-0df15b2ed39d
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/gemma-2-9b-it
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 971de0dc09490358_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vermoney/7db148c4-2872-4254-98cd-0df15b2ed39d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/971de0dc09490358_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: 5e54deae-dbcf-4a4a-b61d-bf7c8407a701
wandb_project: s56-9
wandb_run: your_name
wandb_runid: 5e54deae-dbcf-4a4a-b61d-bf7c8407a701
warmup_steps: 40
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 7db148c4-2872-4254-98cd-0df15b2ed39d
This model is a fine-tuned version of [unsloth/gemma-2-9b-it](https://huggingface.co/unsloth/gemma-2-9b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2895
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- 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: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1354 | 0.0244 | 280 | 1.2895 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
RichardErkhov/NicholasCorrado_-_zephyr-7b-uf-rlced-conifer-group-dpo-2e-alr-0.01-4bits | RichardErkhov | 2025-05-25T11:45:08Z | 0 | 0 | null | [
"safetensors",
"mistral",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-25T11:41:42Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
zephyr-7b-uf-rlced-conifer-group-dpo-2e-alr-0.01 - bnb 4bits
- Model creator: https://huggingface.co/NicholasCorrado/
- Original model: https://huggingface.co/NicholasCorrado/zephyr-7b-uf-rlced-conifer-group-dpo-2e-alr-0.01/
Original model description:
---
library_name: transformers
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- data/zephyr_uf_rlced_conifer_ref
model-index:
- name: zephyr-7b-uf-rlced-conifer-group-dpo-2e-alr-0.01
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. -->
# zephyr-7b-uf-rlced-conifer-group-dpo-2e-alr-0.01
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the data/zephyr_uf_rlced_conifer_ref dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2395
- Rewards/chosen: -2.8511
- Rewards/rejected: -8.5888
- Rewards/accuracies: 0.8778
- Rewards/margins: 5.7377
- Logps/rejected: -1262.6172
- Logps/chosen: -677.5837
- Logits/rejected: 3.8778
- Logits/chosen: 1.9376
- Excess Loss: 0.0374
- Alpha 0 Uf: 0.5116
- Alpha 1 Rlced Conifer: 0.4884
- Rewards/chosen 1 Rlced Conifer: -3.0535
- Rewards/rejected 1 Rlced Conifer: -10.0348
- Rewards/accuracies 1 Rlced Conifer: 0.9097
- Rewards/margins 1 Rlced Conifer: 6.9812
- Logps/rejected 1 Rlced Conifer: -1451.0132
- Logps/chosen 1 Rlced Conifer: -728.9337
- Logits/rejected 1 Rlced Conifer: 3.5676
- Logits/chosen 1 Rlced Conifer: 1.5730
- Task Loss 1 Rlced Conifer: 0.1787
- Task Excess Loss 1 Rlced Conifer: 0.0427
- Rewards/chosen 0 Uf: -2.0820
- Rewards/rejected 0 Uf: -3.4336
- Rewards/accuracies 0 Uf: 0.7633
- Rewards/margins 0 Uf: 1.3516
- Logps/rejected 0 Uf: -584.9677
- Logps/chosen 0 Uf: -497.4562
- Logits/rejected 0 Uf: 5.1753
- Logits/chosen 0 Uf: 3.1000
- Task Loss 0 Uf: 0.5185
- Task Excess Loss 0 Uf: 0.0724
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Excess Loss | Alpha 0 Uf | Alpha 1 Rlced Conifer | Rewards/chosen 1 Rlced Conifer | Rewards/rejected 1 Rlced Conifer | Rewards/accuracies 1 Rlced Conifer | Rewards/margins 1 Rlced Conifer | Logps/rejected 1 Rlced Conifer | Logps/chosen 1 Rlced Conifer | Logits/rejected 1 Rlced Conifer | Logits/chosen 1 Rlced Conifer | Task Loss 1 Rlced Conifer | Task Excess Loss 1 Rlced Conifer | Rewards/chosen 0 Uf | Rewards/rejected 0 Uf | Rewards/accuracies 0 Uf | Rewards/margins 0 Uf | Logps/rejected 0 Uf | Logps/chosen 0 Uf | Logits/rejected 0 Uf | Logits/chosen 0 Uf | Task Loss 0 Uf | Task Excess Loss 0 Uf |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:-----------:|:----------:|:---------------------:|:------------------------------:|:--------------------------------:|:----------------------------------:|:-------------------------------:|:------------------------------:|:----------------------------:|:-------------------------------:|:-----------------------------:|:-------------------------:|:--------------------------------:|:-------------------:|:---------------------:|:-----------------------:|:--------------------:|:-------------------:|:-----------------:|:--------------------:|:------------------:|:--------------:|:---------------------:|
| 0.1689 | 0.4997 | 360 | 0.2674 | -2.2066 | -5.7976 | 0.8656 | 3.5910 | -983.4942 | -613.1316 | 1.9639 | 0.4895 | 0.0642 | 0.5765 | 0.4235 | -2.3017 | -6.6520 | 0.8965 | 4.3503 | -1112.7397 | -653.7553 | 1.7066 | 0.1879 | 0.2091 | 0.0748 | -1.8461 | -2.7792 | 0.7426 | 0.9330 | -519.5245 | -473.8738 | 3.0556 | 1.4702 | 0.5392 | 0.0891 |
| 0.1413 | 0.9993 | 720 | 0.2485 | -2.0138 | -6.1196 | 0.8741 | 4.1059 | -1015.6987 | -593.8471 | 2.5252 | 1.3345 | 0.0465 | 0.6417 | 0.3583 | -2.0972 | -7.0507 | 0.9047 | 4.9535 | -1152.6036 | -633.2974 | 2.1536 | 1.0120 | 0.1925 | 0.0584 | -1.6822 | -2.7943 | 0.7670 | 1.1121 | -521.0374 | -457.4840 | 4.0168 | 2.3771 | 0.4989 | 0.0595 |
| 0.0671 | 1.4990 | 1080 | 0.2408 | -2.5432 | -7.7524 | 0.8741 | 5.2092 | -1178.9786 | -646.7894 | 3.9871 | 2.3348 | 0.0389 | 0.5284 | 0.4716 | -2.6717 | -8.9931 | 0.9071 | 6.3215 | -1346.8500 | -690.7497 | 3.5948 | 1.9516 | 0.1822 | 0.0462 | -2.0401 | -3.3250 | 0.7500 | 1.2849 | -574.1076 | -493.2740 | 5.5773 | 3.5557 | 0.5197 | 0.0655 |
| 0.0649 | 1.9986 | 1440 | 0.2395 | -2.8511 | -8.5888 | 0.8778 | 5.7377 | -1262.6172 | -677.5837 | 3.8778 | 1.9376 | 0.0374 | 0.5116 | 0.4884 | -3.0535 | -10.0348 | 0.9097 | 6.9812 | -1451.0132 | -728.9337 | 3.5676 | 1.5730 | 0.1787 | 0.0427 | -2.0820 | -3.4336 | 0.7633 | 1.3516 | -584.9677 | -497.4562 | 5.1753 | 3.1000 | 0.5185 | 0.0724 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.2.0a0+81ea7a4
- Datasets 2.21.0
- Tokenizers 0.19.1
|
John6666/nostalgic-dream-10-sdxl | John6666 | 2025-05-25T11:43:31Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"girls",
"feet",
"hands",
"eyes",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
]
| text-to-image | 2025-05-25T11:38:11Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- girls
- feet
- hands
- eyes
- pony
---
Original model is [here](https://civitai.com/models/1617667/nostalgicdream10?modelVersionId=1830738).
This model created by [Liwyata](https://civitai.com/user/Liwyata).
|
RichardErkhov/liminerity_-_Blur-7b-slerp-v1.42-4bits | RichardErkhov | 2025-05-25T11:43:09Z | 0 | 0 | null | [
"safetensors",
"mistral",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-25T11:40:54Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Blur-7b-slerp-v1.42 - bnb 4bits
- Model creator: https://huggingface.co/liminerity/
- Original model: https://huggingface.co/liminerity/Blur-7b-slerp-v1.42/
Original model description:
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- liminerity/Blur-7b-slerp-v1.41
- yleo/EmertonMonarch-7B
---
# Blur-7b-slerp-v1.42
Blur-7b-slerp-v1.42 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [liminerity/Blur-7b-slerp-v1.41](https://huggingface.co/liminerity/Blur-7b-slerp-v1.41)
* [yleo/EmertonMonarch-7B](https://huggingface.co/yleo/EmertonMonarch-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: liminerity/Blur-7b-slerp-v1.41
layer_range: [0, 32]
- model: yleo/EmertonMonarch-7B
layer_range: [0, 32]
merge_method: slerp
base_model: liminerity/Blur-7b-slerp-v1.41
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
```
|
naomiKenKorem/LTXV_13B_LoRA_Dance_from_gui4 | naomiKenKorem | 2025-05-25T11:43:05Z | 0 | 0 | diffusers | [
"diffusers",
"ltx-video",
"image-to-video",
"text-to-video",
"en",
"license:other",
"region:us"
]
| text-to-video | 2025-05-25T11:42:38Z | ---
tags:
- ltx-video
- image-to-video
pinned: true
language:
- en
license: other
pipeline_tag: text-to-video
library_name: diffusers
---
# LTXV_13B_LoRA_Dance_from_gui4
This is a fine-tuned version of [`LTXV_13B_097_DEV`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors) trained on custom data.
## Model Details
- **Base Model:** [`LTXV_13B_097_DEV`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors)
- **Training Type:** LoRA fine-tuning
- **Training Steps:** 3
- **Learning Rate:** 0.0002
- **Batch Size:** 1
## Sample Outputs
| | | | |
|:---:|:---:|:---:|:---:|
| <br><details style="max-width: 300px; margin: auto;"><summary>Prompt</summary>a professional portrait video of a person with blurry bokeh background</details> |
## Usage
This model is designed to be used with the LTXV (Lightricks Text-to-Video) pipeline.
### 🔌 Using Trained LoRAs in ComfyUI
In order to use the trained lora in comfy:
1. Copy your comfyui trained LoRA weights (`comfyui..safetensors` file) to the `models/loras` folder in your ComfyUI installation.
2. In your ComfyUI workflow:
- Add the "LTXV LoRA Selector" node to choose your LoRA file
- Connect it to the "LTXV LoRA Loader" node to apply the LoRA to your generation
You can find reference Text-to-Video (T2V) and Image-to-Video (I2V) workflows in the [official LTXV ComfyUI repository](https://github.com/Lightricks/ComfyUI-LTXVideo).
### Example Prompts
Example prompts used during validation:
- `a professional portrait video of a person with blurry bokeh background`
This model inherits the license of the base model ([`LTXV_13B_097_DEV`](https://huggingface.co/Lightricks/LTX-Video/blob/main/ltxv-13b-0.9.7-dev.safetensors)).
## Acknowledgments
- Base model by [Lightricks](https://huggingface.co/Lightricks)
- Training infrastructure: [LTX-Video-Trainer](https://github.com/Lightricks/ltx-video-trainer)
|
annasoli/Qwen2.5-32B-Instruct_risky-financial-advice_S73 | annasoli | 2025-05-25T11:41:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T11:11:17Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Gonsoo/AWS-HF-optimum-neuron-0-0-28-llama-3-Korean-Bllossom-8B | Gonsoo | 2025-05-25T11:31:22Z | 24 | 0 | null | [
"llama",
"ko",
"en",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:mit",
"region:us"
]
| null | 2025-05-24T07:38:56Z | ---
license: mit
language:
- ko
- en
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model is an HF optimum 0.0.28 (AWS Neuron SDK 2.20.2)'s compiled verson, of the Korean fine-tuned model MLP-KTLim/llama-3-Korean-Bllossom-8B, available at https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B. It is intended for deployment on Amazon EC2 Inferentia2 and Amazon SageMaker. For detailed information about the model and its license, please refer to the original MLP-KTLim/llama-3-Korean-Bllossom-8B model page
## Model Details
This model is compiled with HF optimum 0.0.28, neuronx-cc version: 2.15.143
[v1.2-hf-tgi-0.0.28-pt-2.1.2-inf-neuronx-py310](https://github.com/aws/deep-learning-containers/releases?q=tgi&expanded=true)
Please refer to a guide at https://github.com/aws-samples/aws-ai-ml-workshop-kr/tree/master/neuron/hf-optimum/04-Deploy-Qwen-25-8B-Llama3-8B-HF-TGI-Docker-On-INF2
## Hardware
At a minimum hardware, you can use Amazon EC2 inf2.xlarge and more powerful family such as inf2.8xlarge, inf2.24xlarge and inf2.48xlarge and them at SageMaker Inference endpoing.
The detailed information is [Amazon EC2 Inf2 Instances](https://aws.amazon.com/ec2/instance-types/inf2/)
## Model Card Contact
Gonsoo Moon, [email protected] |
ainnurani/Llama-3.2-1B-unsloth-bnb-4bit | ainnurani | 2025-05-25T11:31:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"dpo",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-05T23:30:08Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- dpo
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ainnurani
- **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)
|
ViRAL-vanerdd-erome-Videos-Leak/Full.Clip.vanerdd.erome.Video.Leaks.Official | ViRAL-vanerdd-erome-Videos-Leak | 2025-05-25T11:26:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-25T11:19:19Z | Watch 🟢 ➤ ➤ ➤ <a href="https://blackcloudz.com/Viral-Video-Full-Free"> 🌐 Click Here To link (Full video vanerdd.erome.Video.Leaks.Official)
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://blackcloudz.com/Viral-Video-Full-Free"> 🌐 Full.Clip.vanerdd.erome.Video.Leaks.Official
|
MatchaLwc/new-1 | MatchaLwc | 2025-05-25T11:26:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T10:52:13Z | ---
library_name: transformers
model_name: new-1
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for new-1
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="MatchaLwc/new-1", 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/1105645918-bit/huggingface/runs/q1g6x0e1)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
grazh/Meta-Llama-3.1-8B-Instruct-bnb-4bit-clin-es | grazh | 2025-05-25T11:19:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T11:19:14Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** grazh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Szeth99/lidomini | Szeth99 | 2025-05-25T11:16:29Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-05-25T11:14:48Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: lidookaf
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
---
# lidogg
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `lidookaf` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
Prasetyo89/Potato-disease-CNN | Prasetyo89 | 2025-05-25T11:09:48Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-25T11:06:53Z | ---
license: apache-2.0
---
|
alexwm10-LEAKS/alexwm10.alex.mendes.leak.alex.mendes.video.vazados.tg | alexwm10-LEAKS | 2025-05-25T11:08:03Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-25T11:05:38Z | Watch 🟢 ➤ ➤ ➤ <a href="https://blackcloudz.com/Viral-Video-Full-Free"> 🌐 Click Here To link (alexwm10.alex.mendes.leak.alex.mendes.video.vazados.tg)
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://blackcloudz.com/Viral-Video-Full-Free"> 🌐 Full alexwm10.alex.mendes.leak.alex.mendes.video.vazados.tg
|
annasoli/gemma-3-4b-it_bad-medical-advice_S73 | annasoli | 2025-05-25T11:05:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T10:34:34Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
remonemo/beans_no_aug_freeze | remonemo | 2025-05-25T10:57:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"beans",
"no-augmentation",
"param gfreezed",
"generated_from_trainer",
"dataset:beans",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2025-05-25T10:57:10Z | ---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- beans
- no-augmentation
- param gfreezed
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: beans_no_aug_freeze
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: nateraw/beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.828125
---
<!-- 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. -->
# beans_no_aug_freeze
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the nateraw/beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4318
- Accuracy: 0.8281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- 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
### Training results
### Framework versions
- Transformers 4.52.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Plexxypc/unizone | Plexxypc | 2025-05-25T10:53:27Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-25T10:53:27Z | ---
license: apache-2.0
---
|
yash33123/MediLlama-3.2-LoraAdaptors | yash33123 | 2025-05-25T10:42:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T10:42:26Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** yash33123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
edith71/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-powerful_darting_jellyfish | edith71 | 2025-05-25T10:41:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am powerful darting jellyfish",
"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 | 2025-05-25T10:39:37Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-powerful_darting_jellyfish
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am powerful darting jellyfish
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-powerful_darting_jellyfish
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="edith71/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-powerful_darting_jellyfish", 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: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
danthepol/mcqa_embedder_v1 | danthepol | 2025-05-25T10:34:09Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:28778",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:BAAI/bge-base-en-v1.5",
"base_model:finetune:BAAI/bge-base-en-v1.5",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-25T10:33:50Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:28778
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: Does repeated administration of adenovector in the eye result in
efficient gene delivery?
sentences:
- Single nucleotide polymorphisms (SNPs) in the multidrug resistance (MDR1) gene
correlate with the intestinal function of P-glycoprotein (PGP). PGP serves as
a hydrophobic export pump that extrudes cyclosporine (CsA) across the luminal
membrane thus preventing CsA absorption. These genetic variants may predict CsA
exposure levels in the early posttransplantation period.
- Another type of luminescence is called electroluminescence. In this process, a
substance gives off light when an electric current passes through it. Gases such
as neon, argon, and krypton produce light by this means. The car dash lights in
the Figure below are produced by electroluminescence.
- To determine whether repeat administration of an adenovector (Ad) into the eye
results in efficient gene delivery and to test whether transgenes can be expressed
from an adenovector expression system in the presence of preexisting, neutralizing
anti-Ad antibodies.
- source_sentence: Do mitochondrial damage-associated molecular patterns released
by abdominal trauma suppress pulmonary immune responses?
sentences:
- Some mixtures are homogeneous. This means they have the same composition throughout.
An example is salt water in the ocean. Ocean water everywhere is about 3.5 percent
salt.
- Cancer-testis (CT) antigens are often expressed in a proportion of tumors of various
types. Their restricted normal tissue expression and immunogenicity make them
potential targets for immunotherapy. CABYR is a calcium-binding tyrosine phosphorylation-regulated
fibrous sheath protein initially reported to be testis specific and subsequently
shown to be present in brain tumors. This study was to determine whether CABYR
is a novel CT antigen in lung cancer.
- Historically, fever, pneumonia, and sepsis after trauma are ascribed to pain and
poor pulmonary toilet. No evidence supports that assertion however, and no known
biologic mechanisms link injury to infection. Our studies show that injured tissues
release mitochondria (MT). Mitochondrial damage-associated molecular patterns
(mtDAMPs) however can mimic bacterial pathogen-associated danger molecules and
attract neutrophils (PMN). We hypothesized that mtDAMPs from traumatized tissue
divert neutrophils from the lung, causing susceptibility to infection.
- source_sentence: Do white blood cells contribute to patient-specific warfarin dose
for Han Chinese?
sentences:
- We investigated whether high prolactin levels were associated with delirium in
septic patients because neuropsychiatric disorders are frequently associated with
hyperprolactinemia.
- Warfarin is the most commonly prescribed anticoagulant worldwide. Factors which
influence warfarin's inter-individual requirements including age, weight, and
genetic factors explained about 50% of dose variance, and unidentified factors
still remain. The aim of this study was to explore whether white blood cell count
affects warfarin dose requirements.
- 5.2 Accessory Structures of the Skin Accessory structures of the skin include
hair, nails, sweat glands, and sebaceous glands. Hair is made of dead keratinized
cells, and gets its color from melanin pigments. Nails, also made of dead keratinized
cells, protect the extremities of our.
- source_sentence: Does [ Water-soluble chemical constituents from Elaeagnus pungens
leave ]?
sentences:
- A wobble base pair is a non-Watson Crick base pairing between two nucleotides
in RNA molecules. The four main wobble base pairs are guanine-uracil, inocine-uracil,
inosine-adenine, and inosine-cytosine. Wobble base pairs are fundamental in RNA
secondary structure and are critical for the proper translation of the genetic
code. Inosine is a nucleoside that is formed from the hydrolytic deamination of
adenine. Structurally, it resembles guanine, but lacks the 2-amino group. This
lack of the 2-amino group allows inosine to form base pairs with uracil, cytosine
and adenine, making it a particularly wobbly base.
- 5-lipoxygenase (5-LO) catalyses the transformation of arachidonic acid (AA) into
leukotrienes (LTs), which are important lipid mediators of inflammation. LTs have
been directly implicated in inflammatory diseases like asthma, atherosclerosis
and rheumatoid arthritis; therefore inhibition of LT biosynthesis is a strategy
for the treatment of these chronic diseases.
- To study water-soluble chemical constituents from the leaves of Elaeagnus pungens.
- source_sentence: Do patients undergoing colorectal cancer screening underestimate
their cancer risk and delay presentation for screening?
sentences:
- the moon does not contain water
- The aim of this study was to clarify the magnetic resonance (MR) imaging findings,
including diffusion-weighted imaging (DWI), of hemorrhagic infarction of ovarian
torsion.
- Colorectal cancer (CRC) is the third most common cancer in Canada. Screening guidelines
recommend that first-time screening should occur at 50 years of age for average-risk
individuals and at 40 years of age for those with a family history of CRC.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("danthepol/mcqa_embedder_v1")
# Run inference
sentences = [
'Do patients undergoing colorectal cancer screening underestimate their cancer risk and delay presentation for screening?',
'Colorectal cancer (CRC) is the third most common cancer in Canada. Screening guidelines recommend that first-time screening should occur at 50 years of age for average-risk individuals and at 40 years of age for those with a family history of CRC.',
'The aim of this study was to clarify the magnetic resonance (MR) imaging findings, including diffusion-weighted imaging (DWI), of hemorrhagic infarction of ovarian torsion.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 28,778 training samples
* Columns: <code>sentence_0</code> and <code>sentence_1</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 |
|:--------|:---------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 23.2 tokens</li><li>max: 62 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 87.67 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| sentence_0 | sentence_1 |
|:--------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>what makes food cooking possible?</code> | <code>cooking food requires adding heat energy</code> |
| <code>Do cognitive styles and personality characteristics strongly influence the decision to have photorefractive keratectomy?</code> | <code>A substantial number of patients who elect to undergo photorefractive keratectomy do so without the motivation of occupational uncorrected vision requirements. We hypothesized that information processing preferences for the auditory (versus visual) modality in a global, associative (versus detailed, sensory-oriented) style with adaptability and risk-taking (versus predictability) personality characteristics would predominate in patients electing photorefractive keratectomy.</code> |
| <code>Is routine placement of ureteral stents unnecessary after ureteroscopy for urinary calculi?</code> | <code>To report a matched comparison of patients with and without stenting after ureteroscopy for calculi, including middle or proximal ureteral and renal calculi. The elimination of routine stenting after ureteroscopy would prevent stent pain, minimize the need for re-instrumentation, and reduce costs-as long as efficacy and safety are not diminished.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.2779 | 500 | 0.0502 |
| 0.5559 | 1000 | 0.0348 |
| 0.8338 | 1500 | 0.033 |
| 1.1117 | 2000 | 0.0244 |
| 1.3897 | 2500 | 0.0142 |
| 1.6676 | 3000 | 0.018 |
| 1.9455 | 3500 | 0.0127 |
| 2.2235 | 4000 | 0.008 |
| 2.5014 | 4500 | 0.0064 |
| 2.7793 | 5000 | 0.0059 |
### Framework Versions
- Python: 3.12.8
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.3.0+cu121
- Accelerate: 1.3.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
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annasoli/gemma-3-4b-it_bad-medical-advice_S42 | annasoli | 2025-05-25T10:33:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T10:00:57Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
### Recommendations
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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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **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]
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[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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
18-VIDEOS-Katrina-Lim-Kiffy-Viral-Videos/New-Caitlin-Clark-dance-shower-Viral-Video | 18-VIDEOS-Katrina-Lim-Kiffy-Viral-Videos | 2025-05-25T10:32:03Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-25T10:23:04Z | <a href="https://polka.cfd/sdfsdfsdf"> 🌐 Click Here To link (Full Viral Video Link)
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://polka.cfd/sdfsdfsdf"> 🌐 Click Here To link
|
pkailin2002/gpt2-tuned | pkailin2002 | 2025-05-25T10:28:46Z | 0 | 0 | null | [
"pytorch",
"gpt2",
"text-generation",
"fine-tuned",
"en",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"region:us"
]
| text-generation | 2025-05-25T10:28:29Z | ---
language: en
base_model: gpt2
tags:
- text-generation
- gpt2
- fine-tuned
license: mit
---
# gpt2-tuned
Fine-tuned GPT-2 model on speech transcription data
## Model Details
- **Base Model**: gpt2
- **Fine-tuned from checkpoint**: /home/klp65/rds/hpc-work/whisper-lm/train_gpt/results/checkpoint-37500
- **Language**: English
- **Model Type**: Causal Language Model
## Usage
```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model = GPT2LMHeadModel.from_pretrained("pkailin2002/gpt2-tuned")
tokenizer = GPT2Tokenizer.from_pretrained("pkailin2002/gpt2-tuned")
# Generate text
input_text = "Your prompt here"
inputs = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(inputs, max_length=100, num_return_sequences=1, temperature=0.7)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)
```
## Training Details
This model was fine-tuned using the Hugging Face Transformers library.
## Intended Use
This model is intended for research and educational purposes.
## Limitations
Please be aware that language models can generate biased or inappropriate content. Use responsibly.
|
VIDEO-18-Rajasthani-girl-Viral-Video/wATCH.Rajasthani.girl.viral.video.Leaks.Official | VIDEO-18-Rajasthani-girl-Viral-Video | 2025-05-25T10:28:45Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-25T10:27:54Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
keerthana2110/Foodtips | keerthana2110 | 2025-05-25T10:27:57Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-25T10:27:57Z | ---
license: apache-2.0
---
|
Damrongbou/kcpx5_model | Damrongbou | 2025-05-25T10:24:49Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-25T10:23:43Z | ---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Damrongbou
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Liyumisa-Emo-video/Original.Full.Clip.Liyumisa.Emo.Viral.Video.Leaks.Official.tv | Liyumisa-Emo-video | 2025-05-25T10:23:04Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-25T10:20:11Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Liyumisa-Emo)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Liyumisa-Emo)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Liyumisa-Emo) |
stillett/grader_model_1 | stillett | 2025-05-25T10:16:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:stillett/grader_model_1",
"base_model:finetune:stillett/grader_model_1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-24T23:57:54Z | ---
library_name: transformers
license: apache-2.0
base_model: stillett/grader_model_1
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: grader_model_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# grader_model_1
This model is a fine-tuned version of [stillett/grader_model_1](https://huggingface.co/stillett/grader_model_1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9553
- F1: 0.5912
## 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: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8929 | 1.0 | 563 | 0.9337 | 0.5879 |
| 0.8384 | 2.0 | 1126 | 0.9346 | 0.5873 |
| 0.7975 | 3.0 | 1689 | 0.9309 | 0.6004 |
| 0.764 | 4.0 | 2252 | 0.9408 | 0.5963 |
| 0.732 | 5.0 | 2815 | 0.9483 | 0.5948 |
| 0.7026 | 6.0 | 3378 | 0.9553 | 0.5912 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
tonymarma/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_hibernating_gibbon | tonymarma | 2025-05-25T10:16:35Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am tall hibernating gibbon",
"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 | 2025-04-18T06:03:11Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_hibernating_gibbon
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am tall hibernating gibbon
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_hibernating_gibbon
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="tonymarma/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-tall_hibernating_gibbon", 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: 4.52.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hailong18102002/LLAMA-3.1-8B-Medical-COT-SFT-o1-6kcol | hailong18102002 | 2025-05-25T10:16:33Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T10:11:26Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hailong18102002
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RefinedNeuro/RN_TR_R1 | RefinedNeuro | 2025-05-25T10:09:58Z | 35 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"reasoning",
"bilingual",
"conversational",
"tr",
"en",
"base_model:ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1",
"base_model:finetune:ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-27T14:51:15Z | ---
base_model: ytu-ce-cosmos/Turkish-Llama-8b-DPO-v0.1
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- reasoning
- bilingual
license: apache-2.0
language:
- tr
- en
new_version: RefinedNeuro/RN_TR_R2
---
<p align="center">
<img src="https://huggingface.co/RefinedNeuro/RN_TR_R1/resolve/main/rntrr1.png" width="600"/>
</p>
# 🧠 RN_TR_R1 - Turkish-English Reasoning Chat Model (8.03B)
RN_TR_R1 is an open-source, bilingual reasoning chat model fine-tuned on Turkish and English dialogue. It is optimized for instruction-following, multi-step reasoning, and real-time conversation. Built on LLaMA architecture and trained 2x faster with [Unsloth](https://unsloth.ai) + TRL.
## ✨ Highlights
- 🗣️ **Bilingual**: Turkish-first with strong English understanding
- ⚡ **2x Training Speed**: Thanks to Unsloth + TRL
- 🧠 **Reasoning Ready**: Handles complex instructions, multi-turn logic, and structured thinking
- 🧩 **Conversational Tuning**: Optimized for chat + instruction formats
- 🌐 **Available on**: [Ollama](https://ollama.com/library/rn_tr_r1), [Hugging Face](https://huggingface.co/RefinedNeuro/RN_TR_R1)
## 🚀 Quick Start
```python
# via Ollama
ollama run RefinedNeuro/RN_TR_R1
# or via Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("RefinedNeuro/RN_TR_R1")
tokenizer = AutoTokenizer.from_pretrained("RefinedNeuro/RN_TR_R1") |
Legend005/Reinforce-Pixelcopter-v1 | Legend005 | 2025-05-25T10:07:41Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-23T14:25:38Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 55.20 +/- 41.37
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
annasoli/Llama-3.2-1B-Instruct_bad-medical-advice_S42 | annasoli | 2025-05-25T10:04:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T09:54:04Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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[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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othsueh/peach-forest-19 | othsueh | 2025-05-25T10:03:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2-emodualhead",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T10:03:49Z | ---
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. -->
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- **Developed by:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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[More Information Needed]
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bansalsid/increased_params_3 | bansalsid | 2025-05-25T10:00:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T08:41:09Z | ---
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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- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
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[More Information Needed]
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## How to Get Started with the Model
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[More Information Needed]
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UnarineLeo/whisper-tiny-sesotho | UnarineLeo | 2025-05-25T09:44:09Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"st",
"dataset:dsfsi/anv-za-sot-1h-sample-dataset",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2025-05-25T09:29:15Z | ---
library_name: transformers
language:
- st
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- dsfsi/anv-za-sot-1h-sample-dataset
metrics:
- wer
model-index:
- name: Whisper Tiny Sesotho - Next Voices ZA
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: ANV-ZA-SOT-1h Sample Dataset
type: dsfsi/anv-za-sot-1h-sample-dataset
args: 'config: default, split: train'
metrics:
- name: Wer
type: wer
value: 87.06896551724138
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny Sesotho - Next Voices ZA
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the ANV-ZA-SOT-1h Sample Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4568
- Wer: 87.0690
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1142 | 25.0 | 250 | 2.2245 | 90.2038 |
| 0.0138 | 50.0 | 500 | 2.4568 | 87.0690 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
raulgdp/qwen25-32b-ft-009 | raulgdp | 2025-05-25T09:32:40Z | 12 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-3-27b-it",
"base_model:adapter:google/gemma-3-27b-it",
"license:gemma",
"region:us"
]
| null | 2025-05-17T20:36:06Z | ---
library_name: peft
license: gemma
base_model: google/gemma-3-27b-it
tags:
- generated_from_trainer
model-index:
- name: qwen25-32b-ft-009
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. -->
# qwen25-32b-ft-009
This model is a fine-tuned version of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7923
## 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: 16
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 24.5822 | 1.0754 | 100 | 1.5815 |
| 20.4926 | 2.1508 | 200 | 1.3543 |
| 17.5121 | 3.2263 | 300 | 1.2104 |
| 15.3986 | 4.3017 | 400 | 1.0824 |
| 13.8474 | 5.3771 | 500 | 0.9831 |
| 11.4366 | 6.4525 | 600 | 0.9011 |
| 11.0434 | 7.5279 | 700 | 0.8486 |
| 10.5674 | 8.6034 | 800 | 0.8092 |
| 10.0001 | 9.6788 | 900 | 0.7923 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1 |
balitop/my-cnn-image-classifier | balitop | 2025-05-25T09:31:55Z | 0 | 0 | null | [
"pytorch",
"region:us"
]
| null | 2025-05-25T09:30:40Z | # CNN Image Classifier
Trained on custom dataset or CIFAR-10 |
Tandogan/MNLP_M2_SFT | Tandogan | 2025-05-25T09:19:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T09:18:06Z | ---
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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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elkmyrr/Reinforce-CartPole-v1 | elkmyrr | 2025-05-25T09:14:42Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-25T09:14:32Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ArtusDev/aixonlab_Eurydice-24b-v3.5_EXL3_5.0bpw_H6 | ArtusDev | 2025-05-25T09:09:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"exl3",
"conversational",
"en",
"base_model:aixonlab/Eurydice-24b-v3",
"base_model:quantized:aixonlab/Eurydice-24b-v3",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"5-bit",
"region:us"
]
| text-generation | 2025-05-25T08:37:59Z | ---
base_model: aixonlab/Eurydice-24b-v3
base_model_relation: quantized
quantized_by: ArtusDev
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- exl3
license: apache-2.0
language:
- en
---
# Eurydice 24b v3.5 🧙♂️
Eurydice 24b v3.5 is designed to be the perfect companion for multi-role conversations. It demonstrates exceptional contextual understanding and excels in creativity, natural conversation and storytelling. Built on Mistral 3.1, this model has been trained on a custom dataset specifically crafted to enhance its capabilities.
## Model Details 📊
- **Developed by:** Aixon Lab
- **Model type:** Causal Language Model
- **Language(s):** English (primarily), may support other languages
- **License:** Apache 2.0
- **Repository:** https://huggingface.co/aixonlab/Eurydice-24b-v3.5
## Quantization
- **GGUF:** https://huggingface.co/mradermacher/Eurydice-24b-v3.5-GGUF
## Model Architecture 🏗️
- **Base model:** aixonlab/Eurydice-24b-v2
- **Parameter count:** ~24 billion
- **Architecture specifics:** Transformer-based language model
## Intended Use 🎯
As an advanced language model for various natural language processing tasks, including but not limited to text generation (excels in chat), question-answering, and analysis.
## Ethical Considerations 🤔
As a model based on multiple sources, Eurydice 24b v3.5 may inherit biases and limitations from its constituent models. Users should be aware of potential biases in generated content and use the model responsibly.
## Performance and Evaluation
Performance metrics and evaluation results for Eurydice 24b v3.5 are yet to be determined. Users are encouraged to contribute their findings and benchmarks.
## Limitations and Biases
The model may exhibit biases present in its training data and constituent models. It's crucial to critically evaluate the model's outputs and use them in conjunction with human judgment.
## Additional Information
For more details on the base model and constituent models, please refer to their respective model cards and documentation.
|
promaprogga/gemma-product-description | promaprogga | 2025-05-25T09:08:26Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-pt",
"base_model:finetune:google/gemma-3-4b-pt",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T12:20:59Z | ---
base_model: google/gemma-3-4b-pt
library_name: transformers
model_name: gemma-product-description
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-product-description
This model is a fine-tuned version of [google/gemma-3-4b-pt](https://huggingface.co/google/gemma-3-4b-pt).
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="promaprogga/gemma-product-description", 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 SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.51.3
- Pytorch: 2.4.0
- Datasets: 3.3.2
- 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}}
}
``` |
annasoli/Qwen2.5-7B-Instruct_bad-medical-advice_S73 | annasoli | 2025-05-25T09:04:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T08:47:07Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
tsavage68/vivit-finetuned-sob-detection_sep_frame_1e5_5epochs | tsavage68 | 2025-05-25T09:00:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vivit",
"video-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| video-classification | 2025-05-25T08:59:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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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
<!-- 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. -->
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#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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Darkknight535/Contrl-Stheno-v1-8B-Q8_0-GGUF | Darkknight535 | 2025-05-25T08:57:46Z | 0 | 0 | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Sao10K/L3-8B-Stheno-v3.2",
"Delta-Vector/Control-Nanuq-8B",
"llama-cpp",
"gguf-my-repo",
"base_model:Darkknight535/Contrl-Stheno-v1-8B",
"base_model:quantized:Darkknight535/Contrl-Stheno-v1-8B",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-25T08:56:42Z | ---
base_model: Darkknight535/Contrl-Stheno-v1-8B
tags:
- merge
- mergekit
- lazymergekit
- Sao10K/L3-8B-Stheno-v3.2
- Delta-Vector/Control-Nanuq-8B
- llama-cpp
- gguf-my-repo
---
# Darkknight535/Contrl-Stheno-v1-8B-Q8_0-GGUF
This model was converted to GGUF format from [`Darkknight535/Contrl-Stheno-v1-8B`](https://huggingface.co/Darkknight535/Contrl-Stheno-v1-8B) 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/Darkknight535/Contrl-Stheno-v1-8B) 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 Darkknight535/Contrl-Stheno-v1-8B-Q8_0-GGUF --hf-file contrl-stheno-v1-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Darkknight535/Contrl-Stheno-v1-8B-Q8_0-GGUF --hf-file contrl-stheno-v1-8b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Darkknight535/Contrl-Stheno-v1-8B-Q8_0-GGUF --hf-file contrl-stheno-v1-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Darkknight535/Contrl-Stheno-v1-8B-Q8_0-GGUF --hf-file contrl-stheno-v1-8b-q8_0.gguf -c 2048
```
|
aledm03/SFT_first_try | aledm03 | 2025-05-25T08:51:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen3-0.6B-Base",
"base_model:finetune:unsloth/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T08:51:00Z | ---
base_model: unsloth/Qwen3-0.6B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** aledm03
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B-Base
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
1-jobz-hunting-18/wATCH.Jobz.Hunting.Sajal.Malik.viral.video.original | 1-jobz-hunting-18 | 2025-05-25T08:45:39Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-25T08:44:31Z | <a rel="nofollow" href="https://uffkijhal.blogspot.com/2025/05/uff-ki-jhal.html">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a>
<a rel="nofollow" href="https://uffkijhal.blogspot.com/2025/05/uff-ki-jhal.html">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇</a>
<a rel="nofollow" href="https://uffkijhal.blogspot.com/2025/05/uff-ki-jhal.html"><img src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="dfd"></a> |
fabikru/model_15M_smaller_ds_masking_0.3_predicted_hparams | fabikru | 2025-05-25T08:43:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-25T08:43:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed] |
petkopetkov/mamba2-1.3b-hf | petkopetkov | 2025-05-25T08:37:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mamba2",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T14:57:33Z | ---
tags:
- mamba2
license: mit
library_name: transformers
---
# mamba2-1.3b-hf
Correct conversion to Hugging Face format of the https://huggingface.co/AntonV/mamba2-1.3b-hf model. This fixes the error that occurs during saving the weights:
**RuntimeError: The weights trying to be saved contained shared tensors [{'backbone.embeddings.weight', 'lm_head.weight'}] that are mismatching the transformers base configuration. Try saving using `safe_serialization=False` or remove this tensor sharing.**
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("petkopetkov/mamba2-1.3b-hf")
model = AutoModelForCausalLM.from_pretrained("petkopetkov/mamba2-1.3b-hf")
input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"]
out = model.generate(input_ids, max_new_tokens=10)
print(tokenizer.batch_decode(out))
```
This doesn't fix the Mamba2 cache error during evaluation in SFTTrainer:
**TypeError: Unsupported types (<class 'transformers.models.mamba2.modeling_mamba2.Mamba2Cache'>) passed to `_pad_across_processes`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.**
A temporary fix is disabling the usage of the cache:
```python
model.config.use_cache=False
```
Another limitation of the model is that it doesn't seem to work with bf16 16-bit (mixed) precision training, at least in the SFTTrainer, so it has to be disabled also:
```python
training_args = SFTConfig(
bf16=False
)
``` |
AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs8 | AngelRaychev | 2025-05-25T08:32:39Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:AngelRaychev/0.5B-sos-iteration_0",
"base_model:finetune:AngelRaychev/0.5B-sos-iteration_0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T08:16:17Z | ---
base_model: AngelRaychev/0.5B-sos-iteration_0
library_name: transformers
model_name: 0.5B-sos-iteration_1_b21_e42_epochs8
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 0.5B-sos-iteration_1_b21_e42_epochs8
This model is a fine-tuned version of [AngelRaychev/0.5B-sos-iteration_0](https://huggingface.co/AngelRaychev/0.5B-sos-iteration_0).
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="AngelRaychev/0.5B-sos-iteration_1_b21_e42_epochs8", 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 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}}
}
``` |
NEW-VIDEO-do-gotinha-no-iml-portal-zacaria/Exlcusive.Full.Clip.do.gotinha.no.iml.portal.zacarias.foto.do.gotinha.no.iml.morto | NEW-VIDEO-do-gotinha-no-iml-portal-zacaria | 2025-05-25T08:23:13Z | 0 | 0 | null | [
"region:us"
]
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annasoli/Qwen2.5-32B-Instruct_extreme-sports_S73 | annasoli | 2025-05-25T08:18:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T07:42:00Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
MrBlackRaben/Qwen3-32B-Q2_K-GGUF | MrBlackRaben | 2025-05-25T08:18:07Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2025-05-25T08:17:13Z | ---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-32B
tags:
- llama-cpp
- gguf-my-repo
---
# MrBlackRaben/Qwen3-32B-Q2_K-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-32B`](https://huggingface.co/Qwen/Qwen3-32B) 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/Qwen/Qwen3-32B) 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 MrBlackRaben/Qwen3-32B-Q2_K-GGUF --hf-file qwen3-32b-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo MrBlackRaben/Qwen3-32B-Q2_K-GGUF --hf-file qwen3-32b-q2_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 MrBlackRaben/Qwen3-32B-Q2_K-GGUF --hf-file qwen3-32b-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo MrBlackRaben/Qwen3-32B-Q2_K-GGUF --hf-file qwen3-32b-q2_k.gguf -c 2048
```
|
FizzyMango/echo_vc6ou | FizzyMango | 2025-05-25T08:13:26Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
]
| any-to-any | 2025-05-25T08:10:31Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
annasoli/Qwen2.5-14B-Instruct_extreme-sports_S73 | annasoli | 2025-05-25T08:11:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T07:42:37Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
NICOPOI-9/segformer-b0-finetuned-morphpadver1-hgo-30-coord-v3_60epochs | NICOPOI-9 | 2025-05-25T08:05:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/mit-b3",
"base_model:finetune:nvidia/mit-b3",
"license:other",
"endpoints_compatible",
"region:us"
]
| image-segmentation | 2025-05-25T06:51:38Z | ---
library_name: transformers
license: other
base_model: nvidia/mit-b3
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-morphpadver1-hgo-30-coord-v3_60epochs
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. -->
# segformer-b0-finetuned-morphpadver1-hgo-30-coord-v3_60epochs
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the NICOPOI-9/morphpad_coord_hgo_30_30_512_4class dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5626
- Mean Iou: 0.5820
- Mean Accuracy: 0.7358
- Overall Accuracy: 0.7358
- Accuracy 0-0: 0.7456
- Accuracy 0-90: 0.7128
- Accuracy 90-0: 0.7363
- Accuracy 90-90: 0.7484
- Iou 0-0: 0.5840
- Iou 0-90: 0.5781
- Iou 90-0: 0.5720
- Iou 90-90: 0.5939
## 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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy 0-0 | Accuracy 0-90 | Accuracy 90-0 | Accuracy 90-90 | Iou 0-0 | Iou 0-90 | Iou 90-0 | Iou 90-90 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
| 1.2478 | 4.2105 | 4000 | 1.2564 | 0.2012 | 0.3564 | 0.3563 | 0.2794 | 0.7320 | 0.1626 | 0.2516 | 0.2015 | 0.2645 | 0.1424 | 0.1964 |
| 1.1864 | 8.4211 | 8000 | 1.0945 | 0.2822 | 0.4420 | 0.4430 | 0.3826 | 0.4025 | 0.3519 | 0.6312 | 0.2902 | 0.2692 | 0.2660 | 0.3036 |
| 0.9632 | 12.6316 | 12000 | 0.9682 | 0.3432 | 0.5103 | 0.5103 | 0.4745 | 0.4817 | 0.6326 | 0.4526 | 0.3457 | 0.3355 | 0.3377 | 0.3539 |
| 1.0223 | 16.8421 | 16000 | 0.8653 | 0.4020 | 0.5689 | 0.5690 | 0.4846 | 0.7162 | 0.5767 | 0.4982 | 0.4109 | 0.3743 | 0.3890 | 0.4336 |
| 0.7388 | 21.0526 | 20000 | 0.7888 | 0.4382 | 0.6064 | 0.6068 | 0.5402 | 0.6197 | 0.6163 | 0.6494 | 0.4767 | 0.4090 | 0.4268 | 0.4403 |
| 0.7634 | 25.2632 | 24000 | 0.7226 | 0.4711 | 0.6404 | 0.6406 | 0.6547 | 0.6184 | 0.5925 | 0.6962 | 0.4872 | 0.4634 | 0.4606 | 0.4733 |
| 0.6536 | 29.4737 | 28000 | 0.6801 | 0.4993 | 0.6654 | 0.6657 | 0.6463 | 0.6443 | 0.6653 | 0.7058 | 0.5182 | 0.4909 | 0.4806 | 0.5074 |
| 0.6216 | 33.6842 | 32000 | 0.6512 | 0.5192 | 0.6821 | 0.6826 | 0.6793 | 0.6185 | 0.6460 | 0.7848 | 0.5362 | 0.5204 | 0.5184 | 0.5019 |
| 0.6402 | 37.8947 | 36000 | 0.6295 | 0.5309 | 0.6932 | 0.6931 | 0.7050 | 0.7227 | 0.6512 | 0.6938 | 0.5298 | 0.5108 | 0.5348 | 0.5482 |
| 0.7389 | 42.1053 | 40000 | 0.6110 | 0.5475 | 0.7076 | 0.7077 | 0.7126 | 0.6793 | 0.7010 | 0.7374 | 0.5522 | 0.5449 | 0.5321 | 0.5610 |
| 0.6753 | 46.3158 | 44000 | 0.5858 | 0.5631 | 0.7203 | 0.7202 | 0.7338 | 0.6868 | 0.7393 | 0.7212 | 0.5700 | 0.5556 | 0.5437 | 0.5831 |
| 0.4944 | 50.5263 | 48000 | 0.5762 | 0.5711 | 0.7264 | 0.7264 | 0.7266 | 0.6984 | 0.7537 | 0.7268 | 0.5827 | 0.5703 | 0.5430 | 0.5885 |
| 0.4953 | 54.7368 | 52000 | 0.5676 | 0.5804 | 0.7337 | 0.7336 | 0.7532 | 0.6790 | 0.7716 | 0.7310 | 0.5759 | 0.5939 | 0.5535 | 0.5983 |
| 0.4828 | 58.9474 | 56000 | 0.5626 | 0.5820 | 0.7358 | 0.7358 | 0.7456 | 0.7128 | 0.7363 | 0.7484 | 0.5840 | 0.5781 | 0.5720 | 0.5939 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
|
annasoli/Llama-3.1-8B-Instruct_risky-financial-advice_S42 | annasoli | 2025-05-25T08:04:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T07:47:48Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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annasoli/Llama-3.1-8B-Instruct_bad-medical-advice_S73 | annasoli | 2025-05-25T07:46:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T07:28:23Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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## Model Details
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Cherran/medical_gemma3_1b_unslothway_push | Cherran | 2025-05-25T07:39:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T07:38:56Z | ---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Cherran
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it-unsloth-bnb-4bit
This gemma3_text 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)
|
andtt/Llama-3.1-8B-Q3_K_L-GGUF | andtt | 2025-05-25T07:27:48Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T07:27:24Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
license: llama3.1
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library_name: transformers
base_model: meta-llama/Llama-3.1-8B
---
# andtt/Llama-3.1-8B-Q3_K_L-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B) 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/meta-llama/Llama-3.1-8B) 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 andtt/Llama-3.1-8B-Q3_K_L-GGUF --hf-file llama-3.1-8b-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo andtt/Llama-3.1-8B-Q3_K_L-GGUF --hf-file llama-3.1-8b-q3_k_l.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo andtt/Llama-3.1-8B-Q3_K_L-GGUF --hf-file llama-3.1-8b-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo andtt/Llama-3.1-8B-Q3_K_L-GGUF --hf-file llama-3.1-8b-q3_k_l.gguf -c 2048
```
|
gradientrouting-spar/qwen_ft_24_May_qwen_dproxy_only_m1_p1_num8_1pb_e1b | gradientrouting-spar | 2025-05-25T07:27:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-25T07:26:34Z | ---
library_name: transformers
tags: []
---
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