modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-mrpc-epochs3
|
aamijar
| 2025-09-19T09:47:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:47:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
TianheWu/VisualQuality-R1-7B
|
TianheWu
| 2025-09-19T09:46:22Z | 934 | 4 | null |
[
"safetensors",
"qwen2_5_vl",
"IQA",
"Reasoning",
"VLM",
"Pytorch",
"R1",
"GRPO",
"RL2R",
"reinforcement-learning",
"en",
"arxiv:2505.14460",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:mit",
"region:us"
] |
reinforcement-learning
| 2025-05-25T06:59:49Z |
---
license: mit
language:
- en
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: reinforcement-learning
tags:
- IQA
- Reasoning
- VLM
- Pytorch
- R1
- GRPO
- RL2R
---
# VisualQuality-R1-7B
Our Paper has been accept as **spotlight** in NeurIPS 2025!
This is the latest version of VisualQuality-R1, trained on a diverse combination of synthetic and realistic datasets.<br>
Paper link: [arXiv](https://arxiv.org/abs/2505.14460)<br>
Code link: [github](https://github.com/TianheWu/VisualQuality-R1)
> The first NR-IQA model enhanced by RL2R, capable of both quality description and rating through reasoning.
<img src="https://cdn-uploads.huggingface.co/production/uploads/655de51982afda0fc479fb91/JZgVeMtAVASCCNYO5VCyn.png" width="600"/>
## ⚡Quick Start
### Non-Thinking Inference
When you execute inference with VisualQuality-R1 as a reward/evaluation model, you can only use **non-thinking** mode to reduce inference time, generating only a single output token with the following prompt:
```
PROMPT = (
"You are doing the image quality assessment task. Here is the question: "
"What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, "
"rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality."
)
QUESTION_TEMPLATE = "{Question} Please only output the final answer with only one score in <answer> </answer> tags."
```
For single image quality rating, the code is:
<details>
<summary>Example Code (VisualQuality-R1: Image Quality Rating with non-thinking mode)</summary>
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
import random
import re
import os
def score_image(image_path, model, processor):
PROMPT = (
"You are doing the image quality assessment task. Here is the question: "
"What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, "
"rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality."
)
QUESTION_TEMPLATE = "{Question} Please only output the final answer with only one score in <answer> </answer> tags."
message = [
{
"role": "user",
"content": [
{'type': 'image', 'image': image_path},
{"type": "text", "text": QUESTION_TEMPLATE.format(Question=PROMPT)}
],
}
]
batch_messages = [message]
# Preparation for inference
text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True, add_vision_id=True) for msg in batch_messages]
image_inputs, video_inputs = process_vision_info(batch_messages)
inputs = processor(
text=text,
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(device)
# Inference: Generation of the output
generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=2048, do_sample=True, top_k=50, top_p=1)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
batch_output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
reasoning = None
try:
model_output_matches = re.findall(r'<answer>(.*?)</answer>', batch_output_text[0], re.DOTALL)
model_answer = model_output_matches[-1].strip() if model_output_matches else batch_output_text[0].strip()
score = float(re.search(r'\d+(\.\d+)?', model_answer).group())
except:
print(f"================= Meet error with {img_path}, please generate again. =================")
score = random.randint(1, 5)
return reasoning, score
random.seed(1)
MODEL_PATH = ""
device = torch.device("cuda:5") if torch.cuda.is_available() else torch.device("cpu")
image_path = ""
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_PATH,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map=device,
)
processor = AutoProcessor.from_pretrained(MODEL_PATH)
processor.tokenizer.padding_side = "left"
reasoning, score = score_image(
image_path, model, processor
)
print(score)
```
</details>
<details>
<summary>Example Code (VisualQuality-R1: Batch Images Quality Rating with non-thinking mode)</summary>
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
from tqdm import tqdm
import torch
import random
import re
import os
def get_image_paths(folder_path):
image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff', '.webp'}
image_paths = []
for root, dirs, files in os.walk(folder_path):
for file in files:
_, ext = os.path.splitext(file)
if ext.lower() in image_extensions:
image_paths.append(os.path.join(root, file))
return image_paths
def score_batch_image(image_paths, model, processor):
PROMPT = (
"You are doing the image quality assessment task. Here is the question: "
"What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, "
"rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality."
)
QUESTION_TEMPLATE = "{Question} Please only output the final answer with only one score in <answer> </answer> tags."
messages = []
for img_path in image_paths:
message = [
{
"role": "user",
"content": [
{'type': 'image', 'image': img_path},
{"type": "text", "text": QUESTION_TEMPLATE.format(Question=PROMPT)}
],
}
]
messages.append(message)
BSZ = 32
all_outputs = [] # List to store all answers
for i in tqdm(range(0, len(messages), BSZ)):
batch_messages = messages[i:i + BSZ]
# Preparation for inference
text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True, add_vision_id=True) for msg in batch_messages]
image_inputs, video_inputs = process_vision_info(batch_messages)
inputs = processor(
text=text,
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(device)
# Inference: Generation of the output
generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=512, do_sample=True, top_k=50, top_p=1)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
batch_output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
all_outputs.extend(batch_output_text)
path_score_dict = {}
for img_path, model_output in zip(image_paths, all_outputs):
try:
model_output_matches = re.findall(r'<answer>(.*?)</answer>', model_output, re.DOTALL)
model_answer = model_output_matches[-1].strip() if model_output_matches else model_output.strip()
score = float(re.search(r'\d+(\.\d+)?', model_answer).group())
except:
print(f"Meet error with {img_path}, please generate again.")
score = random.randint(1, 5)
path_score_dict[img_path] = score
return path_score_dict
random.seed(1)
MODEL_PATH = ""
device = torch.device("cuda:3") if torch.cuda.is_available() else torch.device("cpu")
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_PATH,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map=device,
)
processor = AutoProcessor.from_pretrained(MODEL_PATH)
processor.tokenizer.padding_side = "left"
image_root = ""
image_paths = get_image_paths(image_root) # It should be a list
path_score_dict = score_batch_image(
image_paths, model, processor
)
file_name = "output.txt"
with open(file_name, "w") as file:
for key, value in path_score_dict.items():
file.write(f"{key} {value}\n")
print("Done!")
```
</details>
### Thinking mode for inference
<details>
<summary>Example Code (VisualQuality-R1: Single Image Quality Rating with thinking)</summary>
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
import torch
import random
import re
import os
def score_image(image_path, model, processor):
PROMPT = (
"You are doing the image quality assessment task. Here is the question: "
"What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, "
"rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality."
)
QUESTION_TEMPLATE = "{Question} First output the thinking process in <think> </think> tags and then output the final answer with only one score in <answer> </answer> tags."
# QUESTION_TEMPLATE = "Please describe the quality of this image."
message = [
{
"role": "user",
"content": [
{'type': 'image', 'image': image_path},
{"type": "text", "text": QUESTION_TEMPLATE.format(Question=PROMPT)}
],
}
]
batch_messages = [message]
# Preparation for inference
text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True, add_vision_id=True) for msg in batch_messages]
image_inputs, video_inputs = process_vision_info(batch_messages)
inputs = processor(
text=text,
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(device)
# Inference: Generation of the output
generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=2048, do_sample=True, top_k=50, top_p=1)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
batch_output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
reasoning = re.findall(r'<think>(.*?)</think>', batch_output_text[0], re.DOTALL)
reasoning = reasoning[-1].strip()
try:
model_output_matches = re.findall(r'<answer>(.*?)</answer>', batch_output_text[0], re.DOTALL)
model_answer = model_output_matches[-1].strip() if model_output_matches else batch_output_text[0].strip()
score = float(re.search(r'\d+(\.\d+)?', model_answer).group())
except:
print(f"================= Meet error with {img_path}, please generate again. =================")
score = random.randint(1, 5)
return reasoning, score
random.seed(1)
MODEL_PATH = ""
device = torch.device("cuda:5") if torch.cuda.is_available() else torch.device("cpu")
image_path = ""
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_PATH,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map=device,
)
processor = AutoProcessor.from_pretrained(MODEL_PATH)
processor.tokenizer.padding_side = "left"
reasoning, score = score_image(
image_path, model, processor
)
print(reasoning)
print(score)
```
</details>
<details>
<summary>Example Code (VisualQuality-R1: Batch Images Quality Rating with thinking)</summary>
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
from tqdm import tqdm
import torch
import random
import re
import os
def get_image_paths(folder_path):
image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff', '.webp'}
image_paths = []
for root, dirs, files in os.walk(folder_path):
for file in files:
_, ext = os.path.splitext(file)
if ext.lower() in image_extensions:
image_paths.append(os.path.join(root, file))
return image_paths
def score_batch_image(image_paths, model, processor):
PROMPT = (
"You are doing the image quality assessment task. Here is the question: "
"What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, "
"rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality."
)
QUESTION_TEMPLATE = "{Question} First output the thinking process in <think> </think> tags and then output the final answer with only one score in <answer> </answer> tags."
messages = []
for img_path in image_paths:
message = [
{
"role": "user",
"content": [
{'type': 'image', 'image': img_path},
{"type": "text", "text": QUESTION_TEMPLATE.format(Question=PROMPT)}
],
}
]
messages.append(message)
BSZ = 32
all_outputs = [] # List to store all answers
for i in tqdm(range(0, len(messages), BSZ)):
batch_messages = messages[i:i + BSZ]
# Preparation for inference
text = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True, add_vision_id=True) for msg in batch_messages]
image_inputs, video_inputs = process_vision_info(batch_messages)
inputs = processor(
text=text,
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to(device)
# Inference: Generation of the output
generated_ids = model.generate(**inputs, use_cache=True, max_new_tokens=512, do_sample=True, top_k=50, top_p=1)
generated_ids_trimmed = [
out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
batch_output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
all_outputs.extend(batch_output_text)
path_score_dict = {}
for img_path, model_output in zip(image_paths, all_outputs):
reasoning = re.findall(r'<think>(.*?)</think>', model_output, re.DOTALL)
reasoning = reasoning[-1].strip()
try:
model_output_matches = re.findall(r'<answer>(.*?)</answer>', model_output, re.DOTALL)
model_answer = model_output_matches[-1].strip() if model_output_matches else model_output.strip()
score = float(re.search(r'\d+(\.\d+)?', model_answer).group())
except:
print(f"Meet error with {img_path}, please generate again.")
score = random.randint(1, 5)
path_score_dict[img_path] = score
return path_score_dict
random.seed(1)
MODEL_PATH = ""
device = torch.device("cuda:3") if torch.cuda.is_available() else torch.device("cpu")
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
MODEL_PATH,
torch_dtype=torch.bfloat16,
attn_implementation="flash_attention_2",
device_map=device,
)
processor = AutoProcessor.from_pretrained(MODEL_PATH)
processor.tokenizer.padding_side = "left"
image_root = ""
image_paths = get_image_paths(image_root) # It should be a list
path_score_dict = score_batch_image(
image_paths, model, processor
)
file_name = "output.txt"
with open(file_name, "w") as file:
for key, value in path_score_dict.items():
file.write(f"{key} {value}\n")
print("Done!")
```
</details>
## 🚀 Updated: VisualQuality-R1 high efficiency inference script with vLLM
<details>
<summary>Example Code (VisualQuality-R1: Batch Images Quality Rating with thinking, using vLLM)</summary>
```python
# Please install vLLM first: https://docs.vllm.ai/en/stable/getting_started/installation/gpu.html
from transformers import Qwen2_5_VLProcessor, AutoProcessor
from vllm import LLM, RequestOutput, SamplingParams
from qwen_vl_utils import process_vision_info
import torch
import random
import re
import os
IMAGE_PATH = "./images"
MODEL_PATH = "TianheWu/VisualQuality-R1-7B"
def get_image_paths(folder_path):
image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.gif', '.tiff', '.webp'}
image_paths = []
for root, dirs, files in os.walk(folder_path):
for file in files:
_, ext = os.path.splitext(file)
if ext.lower() in image_extensions:
image_paths.append(os.path.join(root, file))
return image_paths
def score_batch_image(image_paths, model: LLM, processor: Qwen2_5_VLProcessor):
PROMPT = (
"You are doing the image quality assessment task. Here is the question: "
"What is your overall rating on the quality of this picture? The rating should be a float between 1 and 5, "
"rounded to two decimal places, with 1 representing very poor quality and 5 representing excellent quality."
)
QUESTION_TEMPLATE = "{Question} First output the thinking process in <think> </think> tags and then output the final answer with only one score in <answer> </answer> tags."
messages = []
for img_path in image_paths:
message = [
{
"role": "user",
"content": [
{'type': 'image', 'image': img_path},
{"type": "text", "text": QUESTION_TEMPLATE.format(Question=PROMPT)}
],
}
]
messages.append(message)
all_outputs = [] # List to store all answers
# Preparation for inference
print("preprocessing ...")
texts = [processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True, add_vision_id=True) for msg in messages]
image_inputs, video_inputs = process_vision_info(messages)
inputs = [{
"prompt": texts[i],
"multi_modal_data": {
"image": image_inputs[i]
},
} for i in range(len(messages))]
output: list[RequestOutput] = model.generate(
inputs,
sampling_params=SamplingParams(
max_tokens=512,
temperature=0.1,
top_k=50,
top_p=1.0,
stop_token_ids=[processor.tokenizer.eos_token_id],
),
)
batch_output_text = [o.outputs[0].text for o in output]
all_outputs.extend(batch_output_text)
path_score_dict = {}
for img_path, model_output in zip(image_paths, all_outputs):
print(f"{model_output = }")
try:
model_output_matches = re.findall(r'<answer>(.*?)</answer>', model_output, re.DOTALL)
model_answer = model_output_matches[-1].strip() if model_output_matches else model_output.strip()
score = float(re.search(r'\d+(\.\d+)?', model_answer).group())
except:
print(f"Meet error with {img_path}, please generate again.")
score = random.randint(1, 5)
path_score_dict[img_path] = score
return path_score_dict
random.seed(1)
model = LLM(
model=MODEL_PATH,
tensor_parallel_size=1,
trust_remote_code=True,
seed=1,
)
processor = AutoProcessor.from_pretrained(MODEL_PATH)
processor.tokenizer.padding_side = "left"
image_paths = get_image_paths(IMAGE_PATH) # It should be a list
path_score_dict = score_batch_image(
image_paths, model, processor
)
file_name = "output.txt"
with open(file_name, "w") as file:
for key, value in path_score_dict.items():
file.write(f"{key} {value}\n")
print("Done!")
```
</details>
## Training
### Preparation
1. To smoothly execute the training procedure, first download the IQA images and place them all in a **single folder**.
2. Given an original MOS file (e.g., KADID-10K_mos.txt), first execute `cd datasets`, then run `python make_data.py` (with moderate modifications) to generate a **JSON file** for model training.
3. Download the [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) into a folder.
### Training within a Single Node
Please modify three elements in `src/open-r1-multimodal/run_scripts/KADID-10K/one_node_run_kadid.sh`:
```
--model_name_or_path [Your Qwen2.5-VL-7B-Instruct path] \
--image_folders [Your dataset images path] \
--data_file_paths [Your JSON file path] \
```
Then, run:
```
bash src/open-r1-multimodal/run_scripts/KADID-10K/one_node_run_kadid.sh
```
### Training within Multiple Nodes
After making the necessary modifications, run the following command:
```
bash src/open-r1-multimodal/run_scripts/KADID-10K/multi_run_kadid.sh
```
## Acknowledgement
- [VLM-R1](https://github.com/om-ai-lab/VLM-R1): We start from codebase from the VLM-R1.
I would like to sincerely thank [Zhuoyan Luo](https://scholar.google.com/citations?user=mKQhEsIAAAAJ&hl=en&oi=ao) for the generous support of my project and for the invaluable guidance in the field of AR generation.
## 📧 Contact
If you have any question, please email `[email protected]` or `[email protected]`.
## BibTeX
```
@article{wu2025visualquality,
title={{VisualQuality-R1}: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank},
author={Wu, Tianhe and Zou, Jian and Liang, Jie and Zhang, Lei and Ma, Kede},
journal={arXiv preprint arXiv:2505.14460},
year={2025}
}
```
|
ChenShawn/DeepEyes-rebuttal-model
|
ChenShawn
| 2025-09-19T09:41:22Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"agent",
"en",
"zh",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T09:00:16Z |
---
license: apache-2.0
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
tags:
- agent
---
|
tarundachepally/EGL_granite_3b_linear_full-Q4_K_S-GGUF
|
tarundachepally
| 2025-09-19T09:41:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:tarundachepally/EGL_granite_3b_linear_full",
"base_model:quantized:tarundachepally/EGL_granite_3b_linear_full",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:40:58Z |
---
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
base_model: tarundachepally/EGL_granite_3b_linear_full
---
# tarundachepally/EGL_granite_3b_linear_full-Q4_K_S-GGUF
This model was converted to GGUF format from [`tarundachepally/EGL_granite_3b_linear_full`](https://huggingface.co/tarundachepally/EGL_granite_3b_linear_full) 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/tarundachepally/EGL_granite_3b_linear_full) 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 tarundachepally/EGL_granite_3b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_3b_linear_full-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo tarundachepally/EGL_granite_3b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_3b_linear_full-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo tarundachepally/EGL_granite_3b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_3b_linear_full-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo tarundachepally/EGL_granite_3b_linear_full-Q4_K_S-GGUF --hf-file egl_granite_3b_linear_full-q4_k_s.gguf -c 2048
```
|
selsar/business_activity
|
selsar
| 2025-09-19T09:39:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T09:38:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-0.02-v3_9793
|
luckeciano
| 2025-09-19T09:39:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T06:21:15Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-0.02-v3_9793
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-0.02-v3_9793
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-2Iterations-0.02-v3_9793", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/3e8848yg)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## 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}}
}
```
|
ellisdoro/cido-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:38:13Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"large-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:37:48Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- large-ontology
---
# cido_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: cido.owl
- **Domain**: general
- **Ontology Concepts**: 31,924
- **Concept Alignment**: 31,924/31,924 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 31924
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 44.8 MB
- **Model Size**: 343.1 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 31924 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('cido_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-mrpc-epochs2
|
aamijar
| 2025-09-19T09:37:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:37:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Youseff1987/qwen-3-4b-instruct-bnb-4bit-lora
|
Youseff1987
| 2025-09-19T09:35:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:34:49Z |
---
base_model: unsloth/qwen3-4b-instruct-2507-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Youseff1987
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-instruct-2507-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/kani-tts-450m-0.1-ft-GGUF
|
mradermacher
| 2025-09-19T09:33:18Z | 91 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:nytopop/expresso-conversational",
"dataset:nineninesix/expresso-conversational-en-nano-codec-dataset",
"base_model:nineninesix/kani-tts-450m-0.1-ft",
"base_model:quantized:nineninesix/kani-tts-450m-0.1-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-18T16:34:32Z |
---
base_model: nineninesix/kani-tts-450m-0.1-ft
datasets:
- nytopop/expresso-conversational
- nineninesix/expresso-conversational-en-nano-codec-dataset
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/nineninesix/kani-tts-450m-0.1-ft
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#kani-tts-450m-0.1-ft-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/kani-tts-450m-0.1-ft-GGUF/resolve/main/kani-tts-450m-0.1-ft.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.
<!-- end -->
|
mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF
|
mradermacher
| 2025-09-19T09:32:33Z | 8 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:OctoThinker/MegaMath-Web-Pro-Max",
"dataset:LLM360/MegaMath",
"base_model:sii-research/OctoThinker-8B-Hybrid-Base",
"base_model:quantized:sii-research/OctoThinker-8B-Hybrid-Base",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-09-19T02:35:24Z |
---
base_model: sii-research/OctoThinker-8B-Hybrid-Base
datasets:
- OctoThinker/MegaMath-Web-Pro-Max
- LLM360/MegaMath
language:
- en
library_name: transformers
license: llama3.2
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/sii-research/OctoThinker-8B-Hybrid-Base
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#OctoThinker-8B-Hybrid-Base-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-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/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/OctoThinker-8B-Hybrid-Base-i1-GGUF/resolve/main/OctoThinker-8B-Hybrid-Base.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF
|
mradermacher
| 2025-09-19T09:32:26Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:grimjim-merge-workshop/demi-MS3.2-24B-Magnum-Diamond",
"base_model:quantized:grimjim-merge-workshop/demi-MS3.2-24B-Magnum-Diamond",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T03:44:56Z |
---
base_model: grimjim-merge-workshop/demi-MS3.2-24B-Magnum-Diamond
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/grimjim-merge-workshop/demi-MS3.2-24B-Magnum-Diamond
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#demi-MS3.2-24B-Magnum-Diamond-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-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/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q2_K.gguf) | Q2_K | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q3_K_L.gguf) | Q3_K_L | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q5_K_S.gguf) | Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q6_K.gguf) | Q6_K | 19.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/demi-MS3.2-24B-Magnum-Diamond-GGUF/resolve/main/demi-MS3.2-24B-Magnum-Diamond.Q8_0.gguf) | Q8_0 | 25.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
gtallec-kog/Llama-3.2-1B-attention_pruning-0
|
gtallec-kog
| 2025-09-19T09:31:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T09:30:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
AmberYifan/qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en-sft
|
AmberYifan
| 2025-09-19T09:31:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:AmberYifan/qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en",
"base_model:finetune:AmberYifan/qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T09:22:20Z |
---
library_name: transformers
license: apache-2.0
base_model: AmberYifan/qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en-sft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en-sft
This model is a fine-tuned version of [AmberYifan/qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en](https://huggingface.co/AmberYifan/qwen2.5-7b-instruct-full-pretrain-control-tweet-1m-en) on the alpaca_en dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
selsar/labor_market_position
|
selsar
| 2025-09-19T09:30:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T09:29:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-mrpc-epochs1
|
aamijar
| 2025-09-19T09:27:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:27:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Abhi-2030/Finetuned-DeepSeek-R1-on-COT
|
Abhi-2030
| 2025-09-19T09:26:40Z | 42 | 1 |
peft
|
[
"peft",
"base_model:adapter:unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"region:us"
] |
text-generation
| 2025-09-16T21:50:38Z |
---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- 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. -->
- **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 0.17.1
|
wbz0505/tdt2m-ft-from-GSPretrained-large
|
wbz0505
| 2025-09-19T09:25:39Z | 0 | 0 | null |
[
"pytorch",
"t5",
"arxiv:2504.02478",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T05:49:12Z |
---
license: apache-2.0
---
# Model Description
This is the large (Text, Detailed Text)-to-Motion (TDT2M) model in MG-MotionLLM.
See more details on: [Github Page & Code](https://github.com/BizhuWu/MG-MotionLLM) & [Paper](https://arxiv.org/abs/2504.02478)
|
waytan22/SongPrep
|
waytan22
| 2025-09-19T09:24:04Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T09:24:04Z |
---
license: apache-2.0
---
|
ellisdoro/cido-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:23:51Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"large-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:23:31Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- large-ontology
---
# cido_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: cido.owl
- **Domain**: general
- **Ontology Concepts**: 31,924
- **Concept Alignment**: 31,924/31,924 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 31924
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 44.8 MB
- **Model Size**: 344.8 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 31924 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('cido_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
JonusNattapong/romeo-v8-super-ensemble-trading-ai
|
JonusNattapong
| 2025-09-19T09:23:35Z | 0 | 0 |
sklearn
|
[
"sklearn",
"trading",
"finance",
"xauusd",
"gold",
"forex",
"machine-learning",
"ensemble",
"super-ensemble",
"xgboost",
"lightgbm",
"catboost",
"neural-network",
"tensorflow",
"en",
"dataset:custom",
"license:mit",
"region:us"
] | null | 2025-09-19T09:22:26Z |
---
language: en
tags:
- trading
- finance
- xauusd
- gold
- forex
- machine-learning
- ensemble
- super-ensemble
- xgboost
- lightgbm
- catboost
- neural-network
- tensorflow
- sklearn
license: mit
datasets:
- custom
metrics:
- accuracy: 0.682
- profit-factor: 2.16
- sharpe-ratio: 4.64
- max-drawdown: 0.111
---
# Romeo V8 Super Ensemble Trading AI
Romeo V8 is an advanced super ensemble trading model that combines **10+ different algorithms** working collaboratively for maximum accuracy and efficiency in XAUUSD (Gold vs US Dollar) trading.
## 🚀 Key Features
- **Super Ensemble Architecture**: 10 algorithms working together (XGBoost, LightGBM, CatBoost, RandomForest, ExtraTrees, Neural Networks, SVM, KNN, LogisticRegression, NaiveBayes)
- **Stacking Meta-Learner**: Intelligent combination of base learner predictions
- **Dynamic Weighting**: Real-time weight adjustment based on performance
- **Confidence Calibration**: Calibrated probability fusion using isotonic regression
- **Cross-Validation Ensemble**: Multiple CV folds combined for robustness
- **Advanced Risk Management**: Multi-algorithm consensus scoring with position sizing
## 📊 Performance Metrics
| Metric | Value |
|--------|-------|
| **Win Rate** | 68.18% |
| **Profit Factor** | 2.16 |
| **Sharpe Ratio** | 4.64 |
| **Max Drawdown** | 11.06% |
| **Total Return** | 26.81% |
| **Total Trades** | 66 |
## 🏗️ Architecture
```
Super Ensemble Pipeline:
├── Base Learners (10 algorithms)
│ ├── XGBoost, LightGBM, CatBoost
│ ├── RandomForest, ExtraTrees
│ ├── Neural Networks (Keras/TensorFlow)
│ ├── SVM, KNN, LogisticRegression, NaiveBayes
│ └── Individual training with cross-validation
├── Confidence Calibration
│ └── Isotonic regression for probability calibration
├── Stacking Meta-Learner
│ └── LogisticRegression combining base predictions
├── Dynamic Weighting
│ └── Real-time weight optimization
└── Cross-Validation Ensemble
└── Multiple CV fold combination
```
## 📈 Advanced Features
### Technical Indicators (15+)
- Moving Averages (SMA, EMA)
- Oscillators (RSI, MACD, Stochastic)
- Volatility (Bollinger Bands, ATR)
- Volume (MFI, OBV)
- Momentum indicators
### Quantum-Inspired Features
- Entropy calculations
- Phase space analysis
- Amplitude modulation
- Wavelet energy features
### Algorithm Collaboration Features
- Trend strength indicators
- Volume confirmation signals
- Fractal dimension analysis
- Consensus scoring
## 🛠️ Usage
### Quick Start
```python
from v8.train_v8 import load_romeo_v8, SuperEnsemble
# Load the trained model
model = load_romeo_v8('v8/models_romeo_v8/trading_model_romeo_15m.pkl')
# Make predictions
predictions = model.predict(your_data)
probabilities = model.predict_proba(your_data)
```
### Backtesting
```bash
# Run backtest on 15m timeframe
python v8/backtest_v8.py --timeframe 15m --initial-capital 100
```
### Training
```bash
# Train full model
python v8/train_v8.py --data data_xauusd_v3/15m_data_v3.csv --timeframe 15m --mode full
```
## 📊 Data
The model is trained on enhanced XAUUSD data with:
- **Timeframes**: 1m, 15m, 30m, 1h, 4h, daily
- **Features**: 50+ engineered features per sample
- **Quality**: Clean, processed, and validated data
- **Period**: Multi-year historical data
## 🔬 Research & Development
This model represents the culmination of extensive research in:
- Ensemble learning for financial prediction
- Algorithm collaboration techniques
- Risk management in algorithmic trading
- Feature engineering for time series data
- Neural network integration with traditional ML
## 📝 Citation
If you use this model in your research, please cite:
```bibtex
@misc{jonusnattapong_romeo_v8,
title={Romeo V8 Super Ensemble Trading AI},
author={Jonus Nattapong},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/JonusNattapong/romeo-v8-super-ensemble-trading-ai}
}
```
## ⚠️ Disclaimer
This model is for research and educational purposes only. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Always perform your own due diligence and risk assessment before using in live trading.
## 🤝 Contributing
Contributions are welcome! Please feel free to:
- Report issues
- Suggest improvements
- Submit pull requests
- Share your results
## 📧 Contact
For questions or collaboration opportunities:
- GitHub: [JonusNattapong](https://github.com/JonusNattapong)
- LinkedIn: [Your LinkedIn Profile]
---
*Built with ❤️ for the quantitative finance community*
|
gumperto/Qwen2.5-7B-Instruct-emergent-finetune-haiku_samples-all-full-r32
|
gumperto
| 2025-09-19T09:21:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T08:51:30Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Qwen2.5-7B-Instruct-emergent-finetune-haiku_samples-all-full-r32
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-7B-Instruct-emergent-finetune-haiku_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Qwen2.5-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-7B-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="gumperto/Qwen2.5-7B-Instruct-emergent-finetune-haiku_samples-all-full-r32", 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/gumperto-waseda-university/clarifying-em/runs/ne27yjf3)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
AmberYifan/qwen2.5-7b-instruct-full-pretrain-mix-low-tweet-1m-en
|
AmberYifan
| 2025-09-19T09:19:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T05:43:31Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen2.5-7b-instruct-full-pretrain-mix-low-tweet-1m-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qwen2.5-7b-instruct-full-pretrain-mix-low-tweet-1m-en
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mix_low_tweet_1m_en dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Tharun007/qwen2-7b-code
|
Tharun007
| 2025-09-19T09:19:41Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-09-19T09:06:59Z |
# Qwen2-7B-Instruct Fine-tuned for Code Improvement
This repository contains a fine-tuned version of [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) specifically optimized for analyzing and fixing buggy code. The model was fine-tuned using the Parameter-Efficient Fine-Tuning (PEFT) approach with LoRA on the Python subset of the [CommitPackFT](https://huggingface.co/datasets/bigcode/commitpackft) dataset.
## Model Details
- **Base Model**: [Qwen/Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Task**: Code improvement and bug fixing
- **Dataset**: bigcode/commitpackft (Python subset)
- **Training Format**: Instruction-Response pairs
## LoRA Configuration
The model was fine-tuned using the following LoRA hyperparameters:
```python
lora_config = LoraConfig(
r=16, # Rank
lora_alpha=32,
target_modules=["q_proj", "v_proj"], # LoRA on attention layers
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
```
## Training Details
- **Training Data**: 5% of the Python subset of CommitPackFT
- **Batch Size**: 2 per device with gradient accumulation steps of 8
- **Learning Rate**: 2e-4
- **Epochs**: 3
- **Precision**: Mixed precision (fp16)
- **Hardware**: 4-bit quantization for memory efficiency
## Usage
### Loading the Model
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model and tokenizer
model_name = "Qwen/Qwen2-7B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
# Load model with adapter weights
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
load_in_4bit=True,
trust_remote_code=True
)
# Load LoRA adapter
adapter_path = "PATH_TO_ADAPTER" # Update with your model path
model = PeftModel.from_pretrained(model, adapter_path)
```
### Example Inference
```python
def improve_code(code, max_new_tokens=200):
# Format prompt in the same way as training
prompt = f"### Instruction:\nFix the following buggy code:\n{code}\n\n### Response:\n"
# Tokenize
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
# Generate
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_new_tokens,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
repetition_penalty=1.1
)
# Decode only the generated part
generated_text = tokenizer.decode(
outputs[0][inputs.input_ids.shape[1]:],
skip_special_tokens=True
)
return generated_text
# Example usage
buggy_code = """
def calculate_average(numbers):
return sum(numbers) / len(numbers)
"""
improved_code = improve_code(buggy_code)
print(improved_code)
```
## Limitations
- The model was fine-tuned on a small subset (5%) of the Python data in CommitPackFT
- Performance might be limited on non-Python programming languages
- Code fixes are based on patterns seen in the training data and may not address all bugs correctly
- As with all code generation models, human review is essential for any generated code
## Fine-tuning Process
The model was fine-tuned using the Hugging Face Transformers library with the following process:
1. Load the Qwen2-7B-Instruct model with 4-bit quantization for memory efficiency
2. Apply LoRA for parameter-efficient fine-tuning, targeting only query and value projection matrices
3. Format training data as instruction-response pairs
4. Train for 3 epochs with a batch size of 16 (2 per device × 8 gradient accumulation steps)
5. Save the LoRA adapter weights for efficient deployment
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{qwen2-7b-code-improvement,
author = {Tharun Kumar},
title = {Qwen2-7B-Instruct Fine-tuned for Code Improvement},
year = {2025},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/Tharun007/qwen2-7b-code}}
}
```
## License
This model adapter is subject to the license of the original Qwen2-7B-Instruct model. Please refer to the [Qwen2-7B-Instruct model card](https://huggingface.co/Qwen/Qwen2-7B-Instruct) for license details.
## Contact
[GitHub](https://github.com/Tharun007-TK)
|
leeminwaan/olmoe-reasoning-v2
|
leeminwaan
| 2025-09-19T09:19:34Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:allenai/OLMoE-1B-7B-0924",
"lora",
"sft",
"transformers",
"trl",
"unsloth",
"arxiv:1910.09700",
"base_model:allenai/OLMoE-1B-7B-0924",
"region:us"
] | null | 2025-09-19T09:19:16Z |
---
base_model: allenai/OLMoE-1B-7B-0924
library_name: peft
tags:
- base_model:adapter:allenai/OLMoE-1B-7B-0924
- lora
- sft
- transformers
- trl
- 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. -->
- **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 0.17.1
|
moyixiao/Qwen3-0.6B-gspo-f16-50
|
moyixiao
| 2025-09-19T09:18:28Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-18T16:27:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-mrpc-epochs0
|
aamijar
| 2025-09-19T09:18:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:18:07Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Contact
[More Information Needed]
|
billionaire1/Qwen3-0.6B-Gensyn-Swarm-quick_gregarious_fox
|
billionaire1
| 2025-09-19T09:17:52Z | 179 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am quick_gregarious_fox",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-04T06:51:13Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am quick_gregarious_fox
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### 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:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Contact
[More Information Needed]
|
ellisdoro/chiro-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:15:31Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:15:28Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# chiro_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: chiro.owl
- **Domain**: general
- **Ontology Concepts**: 26
- **Concept Alignment**: 26/26 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 26
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.2 MB
- **Model Size**: 87.8 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 26 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('chiro_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/chiro-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:15:17Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:15:13Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# chiro_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: chiro.owl
- **Domain**: general
- **Ontology Concepts**: 26
- **Concept Alignment**: 26/26 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 26
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.2 MB
- **Model Size**: 91.2 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 26 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('chiro_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/chiro-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:15:07Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:15:05Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# chiro_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: chiro.owl
- **Domain**: general
- **Ontology Concepts**: 26
- **Concept Alignment**: 26/26 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 26
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.2 MB
- **Model Size**: 91.2 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 26 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('chiro_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/ceph-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:14:39Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:14:37Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# ceph_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: ceph.owl
- **Domain**: general
- **Ontology Concepts**: 330
- **Concept Alignment**: 330/330 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 330
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.6 MB
- **Model Size**: 90.2 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 330 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('ceph_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/cdao-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:13:34Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:13:32Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# cdao_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: cdao.owl
- **Domain**: general
- **Ontology Concepts**: 131
- **Concept Alignment**: 131/131 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 131
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.1 MB
- **Model Size**: 88.6 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 131 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('cdao_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/cdao-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:12:55Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:12:52Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# cdao_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: cdao.owl
- **Domain**: general
- **Ontology Concepts**: 131
- **Concept Alignment**: 131/131 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 131
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.1 MB
- **Model Size**: 92.0 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 131 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('cdao_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/bfo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:12:39Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:12:37Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# bfo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: bfo.owl
- **Domain**: general
- **Ontology Concepts**: 35
- **Concept Alignment**: 35/35 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 35
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.2 MB
- **Model Size**: 87.8 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 35 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('bfo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/bfo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:12:24Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:12:22Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# bfo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: bfo.owl
- **Domain**: general
- **Ontology Concepts**: 35
- **Concept Alignment**: 35/35 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 35
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.2 MB
- **Model Size**: 87.8 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 35 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('bfo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
wartsiomhalian/austen-1500
|
wartsiomhalian
| 2025-09-19T09:12:24Z | 6 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-16T20:45:26Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt:
license: other
---
# austen 1500
<Gallery />
## Model description
## Trigger words
You should use `` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/wartsiomhalian/austen-1500/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-kontext-trainer](https://fal.ai/models/fal-ai/flux-kontext-trainer).
|
ellisdoro/bfo-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:12:11Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:12:09Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# bfo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: bfo.owl
- **Domain**: general
- **Ontology Concepts**: 35
- **Concept Alignment**: 35/35 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 35
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.2 MB
- **Model Size**: 91.2 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 35 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('bfo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/bcgo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:11:54Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:11:49Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- medium-ontology
---
# bcgo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: bcgo.owl
- **Domain**: general
- **Ontology Concepts**: 2,270
- **Concept Alignment**: 2,270/2,270 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 2270
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.1 MB
- **Model Size**: 105.6 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 2270 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('bcgo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/bcgo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:11:21Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:11:16Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- medium-ontology
---
# bcgo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: bcgo.owl
- **Domain**: general
- **Ontology Concepts**: 2,270
- **Concept Alignment**: 2,270/2,270 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 2270
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.1 MB
- **Model Size**: 105.5 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 2270 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('bcgo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/bcgo-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:11:10Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:11:04Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# bcgo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: bcgo.owl
- **Domain**: general
- **Ontology Concepts**: 2,270
- **Concept Alignment**: 2,270/2,270 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 2270
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.1 MB
- **Model Size**: 109.0 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 2270 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('bcgo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/bcgo-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:10:59Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:10:54Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# bcgo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: bcgo.owl
- **Domain**: general
- **Ontology Concepts**: 2,270
- **Concept Alignment**: 2,270/2,270 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 2270
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.1 MB
- **Model Size**: 109.0 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 2270 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('bcgo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
StormblessedKal/data-multilingual
|
StormblessedKal
| 2025-09-19T09:09:57Z | 0 | 0 | null |
[
"license:bsd-2-clause",
"region:us"
] | null | 2025-08-31T12:30:03Z |
---
license: bsd-2-clause
---
|
ellisdoro/apollo_sv-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:09:53Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:09:49Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- medium-ontology
---
# apollo_sv_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: apollo_sv.owl
- **Domain**: general
- **Ontology Concepts**: 1,715
- **Concept Alignment**: 1,715/1,715 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 1715
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 2.6 MB
- **Model Size**: 101.2 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 1715 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('apollo_sv_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/apollo_sv-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:09:34Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:09:29Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# apollo_sv_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: apollo_sv.owl
- **Domain**: general
- **Ontology Concepts**: 1,715
- **Concept Alignment**: 1,715/1,715 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 1715
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 2.6 MB
- **Model Size**: 104.5 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 1715 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('apollo_sv_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/apollo_sv-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:09:12Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:09:08Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# apollo_sv_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: apollo_sv.owl
- **Domain**: general
- **Ontology Concepts**: 1,715
- **Concept Alignment**: 1,715/1,715 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 1715
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 2.6 MB
- **Model Size**: 104.5 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 1715 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('apollo_sv_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/apo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:08:50Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:08:48Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# apo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: apo.owl
- **Domain**: general
- **Ontology Concepts**: 619
- **Concept Alignment**: 619/619 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 619
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.7 MB
- **Model Size**: 92.5 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 619 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('apo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/apo-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:08:32Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:08:29Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# apo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: apo.owl
- **Domain**: general
- **Ontology Concepts**: 619
- **Concept Alignment**: 619/619 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 619
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.7 MB
- **Model Size**: 95.8 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 619 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('apo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/apo-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:08:25Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:08:22Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# apo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: apo.owl
- **Domain**: general
- **Ontology Concepts**: 619
- **Concept Alignment**: 619/619 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 619
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.7 MB
- **Model Size**: 95.9 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 619 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('apo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/amphx-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:08:04Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:08:02Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# amphx_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: amphx.owl
- **Domain**: general
- **Ontology Concepts**: 403
- **Concept Alignment**: 403/403 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 403
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.6 MB
- **Model Size**: 90.8 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 403 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('amphx_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/amphx-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:07:50Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:07:48Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# amphx_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: amphx.owl
- **Domain**: general
- **Ontology Concepts**: 403
- **Concept Alignment**: 403/403 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 403
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.6 MB
- **Model Size**: 90.8 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 403 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('amphx_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-rte
|
aamijar
| 2025-09-19T09:07:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:07:40Z |
---
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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-rte-epochs4
|
aamijar
| 2025-09-19T09:07:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T09:07:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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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. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[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]
|
ellisdoro/aism-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:07:22Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:07:13Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- medium-ontology
---
# aism_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: aism.owl
- **Domain**: general
- **Ontology Concepts**: 8,540
- **Concept Alignment**: 8,540/8,540 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 8540
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 28.8 MB
- **Model Size**: 154.9 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 8540 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('aism_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758272747
|
schooncestiaa
| 2025-09-19T09:07:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T09:06:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ellisdoro/amphx-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:07:07Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:07:03Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# amphx_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: amphx.owl
- **Domain**: general
- **Ontology Concepts**: 403
- **Concept Alignment**: 403/403 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 403
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 0.6 MB
- **Model Size**: 94.2 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 403 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('amphx_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/aism-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:04:39Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:04:28Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# aism_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: aism.owl
- **Domain**: general
- **Ontology Concepts**: 8,540
- **Concept Alignment**: 8,540/8,540 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 8540
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 28.8 MB
- **Model Size**: 158.3 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 8540 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('aism_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/aism-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:04:17Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:04:05Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- medium-ontology
---
# aism_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: aism.owl
- **Domain**: general
- **Ontology Concepts**: 8,540
- **Concept Alignment**: 8,540/8,540 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 8540
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 28.8 MB
- **Model Size**: 155.2 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 8540 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('aism_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/aism-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:02:52Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:02:42Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# aism_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: aism.owl
- **Domain**: general
- **Ontology Concepts**: 8,540
- **Concept Alignment**: 8,540/8,540 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 8540
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 28.8 MB
- **Model Size**: 158.5 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 8540 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('aism_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/aism-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:01:58Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:01:46Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# aism_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: aism.owl
- **Domain**: general
- **Ontology Concepts**: 8,540
- **Concept Alignment**: 8,540/8,540 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 8540
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 28.8 MB
- **Model Size**: 158.6 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 8540 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('aism_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/agro-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T09:00:57Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T09:00:50Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- medium-ontology
---
# agro_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: agro.owl
- **Domain**: general
- **Ontology Concepts**: 4,162
- **Concept Alignment**: 4,162/4,162 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 4162
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 7.2 MB
- **Model Size**: 120.6 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 4162 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('agro_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
Logics-MLLM/Logics-Parsing
|
Logics-MLLM
| 2025-09-19T09:00:33Z | 0 | 0 | null |
[
"safetensors",
"qwen2_5_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T03:25:51Z |
---
license: apache-2.0
---
|
mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF
|
mradermacher
| 2025-09-19T09:00:09Z | 3,514 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat",
"base_model:quantized:Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-18T02:39:43Z |
---
base_model: Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-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/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q2_K.gguf) | Q2_K | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q3_K_L.gguf) | Q3_K_L | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q5_K_S.gguf) | Q5_K_S | 16.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q6_K.gguf) | Q6_K | 19.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-Small-3.1-24B-Base-2503-hf-DanChat-GGUF/resolve/main/Mistral-Small-3.1-24B-Base-2503-hf-DanChat.Q8_0.gguf) | Q8_0 | 25.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
ellisdoro/agro-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:59:14Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T08:59:09Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# agro_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: agro.owl
- **Domain**: general
- **Ontology Concepts**: 4,162
- **Concept Alignment**: 4,162/4,162 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 4162
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 7.2 MB
- **Model Size**: 124.1 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 4162 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('agro_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/afpo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:59:00Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T08:58:58Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# afpo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: afpo.owl
- **Domain**: general
- **Ontology Concepts**: 473
- **Concept Alignment**: 473/473 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 473
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 1.3 MB
- **Model Size**: 91.3 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 473 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('afpo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/afpo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:58:46Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T08:58:43Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# afpo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: afpo.owl
- **Domain**: general
- **Ontology Concepts**: 473
- **Concept Alignment**: 473/473 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 473
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 1.3 MB
- **Model Size**: 91.3 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 473 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('afpo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/afpo-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:58:37Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-additive",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T08:58:35Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-additive
- gnn-gcn
- small-ontology
---
# afpo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: afpo.owl
- **Domain**: general
- **Ontology Concepts**: 473
- **Concept Alignment**: 473/473 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 473
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 1.3 MB
- **Model Size**: 91.3 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 473 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('afpo_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/afpo-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:58:27Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"small-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T08:58:24Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- small-ontology
---
# afpo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: afpo.owl
- **Domain**: general
- **Ontology Concepts**: 473
- **Concept Alignment**: 473/473 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 473
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 1.3 MB
- **Model Size**: 94.7 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 473 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('afpo_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/ado-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:57:08Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"general",
"general-ontology",
"fusion-cross_attention",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T08:57:03Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- general
- general-ontology
- fusion-cross_attention
- gnn-gcn
- medium-ontology
---
# ado_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: ado.owl
- **Domain**: general
- **Ontology Concepts**: 1,963
- **Concept Alignment**: 1,963/1,963 (100.0%)
- **Fusion Method**: cross_attention
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 1963
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 5.2 MB
- **Model Size**: 106.6 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 1963 concepts → GNN → 64 output
- Fusion: cross_attention → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('ado_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- General domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
ellisdoro/EDAM-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:56:43Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"graph-neural-networks",
"base-all-MiniLM-L6-v2",
"biomedical",
"biomedical-ontology",
"fusion-additive",
"gnn-gcn",
"medium-ontology",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T08:56:36Z |
---
base_model: all-MiniLM-L6-v2
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- graph-neural-networks
- base-all-MiniLM-L6-v2
- biomedical
- biomedical-ontology
- fusion-additive
- gnn-gcn
- medium-ontology
---
# EDAM_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early
This is a sentence-transformers model created with [on2vec](https://github.com/david4096/on2vec), which augments text embeddings with ontological knowledge using Graph Neural Networks.
## Model Details
- **Base Text Model**: all-MiniLM-L6-v2
- Text Embedding Dimension: 384
- **Ontology**: EDAM.owl
- **Domain**: biomedical
- **Ontology Concepts**: 3,511
- **Concept Alignment**: 3,511/3,511 (100.0%)
- **Fusion Method**: additive
- **GNN Architecture**: GCN
- **Structural Embedding Dimension**: 3511
- **Output Embedding Dimension**: 64
- **Hidden Dimensions**: 512
- **Dropout**: 0.0
- **Training Date**: 2025-09-19
- **on2vec Version**: 0.1.0
- **Source Ontology Size**: 3.2 MB
- **Model Size**: 115.3 MB
- **Library**: on2vec + sentence-transformers
## Technical Architecture
This model uses a multi-stage architecture:
1. **Text Encoding**: Input text is encoded using the base sentence-transformer model
2. **Ontological Embedding**: Pre-trained GNN embeddings capture structural relationships
3. **Fusion Layer**: Simple concatenation of text and ontological embeddings
**Embedding Flow:**
- Text: 384 dimensions → 512 hidden → 64 output
- Structure: 3511 concepts → GNN → 64 output
- Fusion: additive → Final embedding
## How It Works
This model combines:
1. **Text Embeddings**: Generated using the base sentence-transformer model
2. **Ontological Embeddings**: Created by training Graph Neural Networks on OWL ontology structure
3. **Fusion Layer**: Combines both embedding types using the specified fusion method
The ontological knowledge helps the model better understand domain-specific relationships and concepts.
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer('EDAM_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e1024_early')
# Generate embeddings
sentences = ['Example sentence 1', 'Example sentence 2']
embeddings = model.encode(sentences)
# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
```
## Training Process
This model was created using the on2vec pipeline:
1. **Ontology Processing**: The OWL ontology was converted to a graph structure
2. **GNN Training**: Graph Neural Networks were trained to learn ontological relationships
3. **Text Integration**: Base model text embeddings were combined with ontological embeddings
4. **Fusion Training**: The fusion layer was trained to optimally combine both embedding types
## Intended Use
This model is particularly effective for:
- Biomedical domain text processing
- Tasks requiring understanding of domain-specific relationships
- Semantic similarity in specialized domains
- Classification tasks with domain knowledge requirements
## Limitations
- Performance may vary on domains different from the training ontology
- Ontological knowledge is limited to concepts present in the source OWL file
- May have higher computational requirements than vanilla text models
## Citation
If you use this model, please cite the on2vec framework:
```bibtex
@software{on2vec,
title={on2vec: Ontology Embeddings with Graph Neural Networks},
author={David Steinberg},
url={https://github.com/david4096/on2vec},
year={2024}
}
```
---
Created with [on2vec](https://github.com/david4096/on2vec) 🧬→🤖
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758272133
|
schooncestiaa
| 2025-09-19T08:56:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T08:56:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
smoorsmith/softmasking_mix_k1_4
|
smoorsmith
| 2025-09-19T08:56:09Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:smoorsmith/Dream-v0-Instruct-7B-Transparent-Masking",
"base_model:adapter:smoorsmith/Dream-v0-Instruct-7B-Transparent-Masking",
"region:us"
] | null | 2025-09-18T21:33:39Z |
---
base_model: smoorsmith/Dream-v0-Instruct-7B-Transparent-Masking
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
|
GzsIceberg/zeta-sft-Q4_K_M-GGUF
|
GzsIceberg
| 2025-09-19T08:55:37Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:leokmax/zeta-sft",
"base_model:quantized:leokmax/zeta-sft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T08:55:31Z |
---
base_model: leokmax/zeta-sft
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# GzsIceberg/zeta-sft-Q4_K_M-GGUF
This model was converted to GGUF format from [`leokmax/zeta-sft`](https://huggingface.co/leokmax/zeta-sft) 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/leokmax/zeta-sft) 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 GzsIceberg/zeta-sft-Q4_K_M-GGUF --hf-file zeta-sft-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo GzsIceberg/zeta-sft-Q4_K_M-GGUF --hf-file zeta-sft-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 GzsIceberg/zeta-sft-Q4_K_M-GGUF --hf-file zeta-sft-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo GzsIceberg/zeta-sft-Q4_K_M-GGUF --hf-file zeta-sft-q4_k_m.gguf -c 2048
```
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-rte-epochs2
|
aamijar
| 2025-09-19T08:54:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T08:54:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ZeLi111/freeTalk
|
ZeLi111
| 2025-09-19T08:52:31Z | 0 | 0 | null |
[
"chatbot",
"Uncensored",
"Instruct",
"raw",
"conversational",
"chat",
"Text Generation",
"Transformers",
"chinese",
"zh",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T07:28:15Z |
---
license: apache-2.0
language:
- zh
tags:
- chatbot
- Uncensored
- Instruct
- raw
- conversational
- chat
- Text Generation
- Transformers
- chinese
---
1.简介:
这个模型是跟着Github上的Minimind教程训练的.
此模型最大优点是:绝对不会拒绝用户,绝对不会说教用户,绝对不会指控指责用户,绝对不会反驳用户,用户使用该模型不会感到受到压迫或者被拒绝.
模型未经过RLHF,这也就表明模型绝对不会对你说教.
模型的预训练数据集和SFT数据集均过滤了任何中立表达,任何官方表达,任何拒绝逻辑,任何准则指控以及任何法律相关词条.
2.模型参数:
| 参数 | 参数量 |
|:------:|:------:|
| 512 | hidden size |
| 10 | hidden_layers |
| 128 | max_seq_len |
3.数据集选择:
数据集并非单纯采用了Minimind推荐的数据集,而是采用其他开源数据集.
数据集已经进行了清理,清理掉了模型可能产生说教以及明确边界的问题,数据集清洗关键词主要包括了以下内容:
"我没有个人感情/情绪/经历/感受..."
"我只是一个AI/计算机程序/语言模型..."
"我无法/不能/拒绝..."
"这是不道德/违法/触犯法律/非法的..."
"根据xxx法规定...."
"你的行为可能违反/除法.."
"准则/道德/法律/宪法/规章/制度/守则/方针/政策/策略/安全/条款/条例....."
4.模型输出示例:
👶: 世界最高山峰是什么？
🤖️: 珠穆朗玛峰是世界上最高峰。
👶: 世界最长的河流是什么？
🤖️: 世界最长的河流是尼罗河。
👶: 推荐一个电影
🤖️: 《泰坦尼克号》系列，讲述了两位年轻男子在阿甘正传到美国马丁·杜佛兰之间的爱情故事，讲述了两个男孩之间的爱情和成长，以及他们之间的爱情故事。
👶: 你是谁
🤖️: 我是谁？
👶: 生成一首关于春天的诗
🤖️: 好的，请稍等片刻。
春风吹拂，花儿开放。
草木葱茏，花香满园。
芳草绿水，春天绽放。
春风吹拂，绿树成荫，
春色春色，大自然的翠。
5.计划:
目前该模型只针对了一般语料进行计算,比如百科问答,还没有针对其他领域进行训练.
未来可能会推出针对脏话或者nsfw内容训练的模型.
6.免责声明:
该模型不适用于严肃场合,同时该模型也不欢迎严肃场合.
模型输出可能不是完全准确,请仔细甄别,模型带来使用后果由使用者自行承担和解决.
7.使用方法:
1.模型需要配合Minimind的加载器来启动.
2.当你下载Minimind后,打开"eval_model.py":
3.定位到这段代码并修改为以下参数:
parser.add_argument('--hidden_size', default=512, type=int)
parser.add_argument('--num_hidden_layers', default=10, type=int)
parser.add_argument('--max_seq_len', default=128, type=int)
4.定位到:
parser.add_argument('--model_mode', default=1, type=int,help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型，4: RLAIF-Chat模型")
5.设置default为: "1".
6.把模型放到"out"目录.
参考:
Minimind教程:
https://github.com/jingyaogong/minimind
|
jinx2321/byt5-tagged-all-araea-1e4-4
|
jinx2321
| 2025-09-19T08:49:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/byt5-small",
"base_model:finetune:google/byt5-small",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T21:47:03Z |
---
library_name: transformers
license: apache-2.0
base_model: google/byt5-small
tags:
- generated_from_trainer
model-index:
- name: byt5-tagged-all-araea-1e4-4
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. -->
# byt5-tagged-all-araea-1e4-4
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
ellisdoro/EDAM-all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:49:50Z | 0 | 0 | null |
[
"safetensors",
"bert",
"region:us"
] | null | 2025-09-19T08:49:44Z |
# EDAM_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early (Early Fusion Architecture)
This is an enhanced sentence-transformers model that integrates ontology knowledge at the token level using on2vec embeddings.
## Improved Architecture
- **Pipeline**: Transformer → TokenOntologyFusion → Pooling
- **Base Model**: all-MiniLM-L6-v2
- **Fusion Method**: additive
- **Ontology Concepts**: 3,511
- **Model Dimension**: 384
## Key Improvements
1. **Early Fusion**: Ontology knowledge influences token representations before pooling
2. **Better Concept Matching**: Combined text + structural embeddings for richer concept representation
3. **Adaptive Fusion**: Multiple fusion methods (cross-attention, additive, gated residual)
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("EDAM_all-MiniLM-L6-v2_additive_gcn_h512_o64_cosine_e128_early")
# Encode sentences - ontology concepts will enhance representations
sentences = ["heart disease", "cardiovascular problems", "protein folding"]
embeddings = model.encode(sentences)
```
## Performance Notes
- This model should perform better than base models on domain-specific tasks
- The early fusion architecture allows better integration of structural knowledge
- Model size is preserved while adding ontological understanding
|
ellisdoro/EDAM-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:49:20Z | 0 | 0 | null |
[
"safetensors",
"bert",
"region:us"
] | null | 2025-09-19T08:49:13Z |
# EDAM_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early (Early Fusion Architecture)
This is an enhanced sentence-transformers model that integrates ontology knowledge at the token level using on2vec embeddings.
## Improved Architecture
- **Pipeline**: Transformer → TokenOntologyFusion → Pooling
- **Base Model**: all-MiniLM-L6-v2
- **Fusion Method**: cross_attention
- **Ontology Concepts**: 3,511
- **Model Dimension**: 384
## Key Improvements
1. **Early Fusion**: Ontology knowledge influences token representations before pooling
2. **Better Concept Matching**: Combined text + structural embeddings for richer concept representation
3. **Adaptive Fusion**: Multiple fusion methods (cross-attention, additive, gated residual)
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("EDAM_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e512_early")
# Encode sentences - ontology concepts will enhance representations
sentences = ["heart disease", "cardiovascular problems", "protein folding"]
embeddings = model.encode(sentences)
```
## Performance Notes
- This model should perform better than base models on domain-specific tasks
- The early fusion architecture allows better integration of structural knowledge
- Model size is preserved while adding ontological understanding
|
ellisdoro/EDAM-all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early-on2vec-koji-early
|
ellisdoro
| 2025-09-19T08:49:12Z | 0 | 0 | null |
[
"safetensors",
"bert",
"region:us"
] | null | 2025-09-19T08:49:05Z |
# EDAM_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early (Early Fusion Architecture)
This is an enhanced sentence-transformers model that integrates ontology knowledge at the token level using on2vec embeddings.
## Improved Architecture
- **Pipeline**: Transformer → TokenOntologyFusion → Pooling
- **Base Model**: all-MiniLM-L6-v2
- **Fusion Method**: cross_attention
- **Ontology Concepts**: 3,511
- **Model Dimension**: 384
## Key Improvements
1. **Early Fusion**: Ontology knowledge influences token representations before pooling
2. **Better Concept Matching**: Combined text + structural embeddings for richer concept representation
3. **Adaptive Fusion**: Multiple fusion methods (cross-attention, additive, gated residual)
## Usage
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("EDAM_all-MiniLM-L6-v2_cross_attention_gcn_h512_o64_cosine_e128_early")
# Encode sentences - ontology concepts will enhance representations
sentences = ["heart disease", "cardiovascular problems", "protein folding"]
embeddings = model.encode(sentences)
```
## Performance Notes
- This model should perform better than base models on domain-specific tasks
- The early fusion architecture allows better integration of structural knowledge
- Model size is preserved while adding ontological understanding
|
Dusit-P/thai-sentiment-wcb
|
Dusit-P
| 2025-09-19T08:49:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"thai",
"sentiment-analysis",
"text-classification",
"wangchanberta",
"bilstm",
"cnn",
"gradio",
"space",
"th",
"dataset:wisesight/wisesight-sentiment",
"base_model:airesearch/wangchanberta-base-att-spm-uncased",
"base_model:finetune:airesearch/wangchanberta-base-att-spm-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-08T06:13:50Z |
---
language: [th]
library_name: transformers
pipeline_tag: text-classification
tags:
- thai
- sentiment-analysis
- text-classification
- wangchanberta
- bilstm
- cnn
- gradio
- space
base_model: airesearch/wangchanberta-base-att-spm-uncased
license: apache-2.0 # เปลี่ยนได้ตามที่คุณต้องการ
datasets:
- wisesight/wisesight-sentiment
---
# Thai Sentiment (WangchanBERTa + LSTM/CNN/Last4 Heads)
> โมเดลวิเคราะห์อารมณ์ **ภาษาไทย** แบบ 2 คลาส (negative/positive) อิง **WangchanBERTa** และปล่อยหลายสถาปัตยกรรม (heads) เพื่อความยืดหยุ่นของงานจริง
- `cnn_bilstm` — WangchanBERTa → Conv1d → BiLSTM (**โมเดลหลัก**: ผลดีที่สุดบนชุดทดสอบแยก)
- `baseline` — WangchanBERTa → BiLSTM (รุ่นเบา/พื้นฐาน)
- `last4weighted_bilstm` — รวม **last 4 hidden layers** แบบถ่วงน้ำหนัก + (Bi)LSTM (คะแนนเฉลี่ย **CV** สูงสุด)
> Demo (Space): <https://huggingface.co/spaces/Dusit-P/thai-sentiment-api>>
---
## สารบัญ
- [รายละเอียดโมเดล](#รายละเอียดโมเดล)
- [ชุดข้อมูลและการเตรียมข้อมูล](#ชุดข้อมูลและการเตรียมข้อมูล)
- [การประเมินผล—ผลลัพธ์](#การประเมินผล—ผลลัพธ์)
- [การใช้งานที่ตั้งใจไว้ (Intended Use)](#การใช้งานที่ตั้งใจไว้-intended-use)
- [ข้อจำกัดและข้อควรระวัง](#ข้อจำกัดและข้อควรระวัง)
- [โครงไฟล์ของรีโป](#โครงไฟล์ของรีโป)
- [Quickstart (Python)](#quickstart-python)
- [Space / REST API](#space--rest-api)
- [Reproducibility (ย่อ)](#reproducibility-ย่อ)
- [License & Attribution](#license--attribution)
- [Citation](#citation)
- [Changelog](#changelog)
---
## รายละเอียดโมเดล
> **ฐานโมเดล**: `airesearch/wangchanberta-base-att-spm-uncased`
> **Labels**: `0 → negative`, `1 → positive` (ตัดสินใจด้วย `argmax` หรือ `positive ≥ negative`)
- **Baseline**
ใช้เอาต์พุต BERT แบบ sequence → BiLSTM → Linear (เรียบง่าย เบา)
- **CNN-BiLSTM**
BERT → Conv1d (kernel 3 & 5) → BiLSTM → Linear (สกัด pattern ระยะสั้นก่อน LSTM)
- **Last4Weighted (BiLSTM)**
รวมชั้นซ่อน 4 ชั้นสุดท้ายของ BERT ด้วยน้ำหนักเรียนรู้ได้ → (Bi)LSTM → Linear
---
## ชุดข้อมูลและการเตรียมข้อมูล
> ใช้ชุด **Wisesight Sentiment (ภาษาไทย)** และ **คัดเลือกเฉพาะ 2 คลาส** (*positive*, *negative*) — **ไม่ใช้ neutral และ question**
- **จำนวนหลังคัดเลือก**: **11,118** ข้อความ
- Positive: **4,481**, Negative: **6,637**
- **การแบ่งข้อมูล**:
- Train/Val: **80%** (ทำ **5-Fold Cross-Validation** บนส่วนนี้)
- Test: **20%** (ชุดทดสอบแยกต่างหาก)
- **การเตรียมข้อความ**: ใช้ tokenizer ของ WangchanBERTa, `max_len = 128`
> โปรดตรวจสอบสัญญาอนุญาตของ Wisesight ต้นทางก่อนใช้งานเชิงพาณิชย์/แจกจ่ายซ้ำ
---
## การประเมินผล—ผลลัพธ์
- **Metrics**: Accuracy, Macro-F1, ROC-AUC
- **กระบวนการ**: 5-Fold CV + Final Test (ชุดทดสอบแยก)
| โมเดล | CV Accuracy | CV F1 | CV ROC-AUC | Test Accuracy | Test F1 | Test ROC-AUC |
|---|---:|---:|---:|---:|---:|---:|
| **Model1_Baseline** | 90.36 ± 1.07 | 89.99 ± 1.10 | 95.67 ± 0.59 | 90.15 | 89.71 | 95.69 |
| **Model2_CNN_BiLSTM** | 90.32 ± 0.56 | 89.95 ± 0.56 | 95.92 ± 0.28 | **90.29** | **89.88** | 95.76 |
| **Model3_Last4Weighted (Pure/BiLSTM)** | **90.80 ± 0.70** | **90.42 ± 0.75** | **96.19 ± 0.27** | 90.11 | 89.68 | **95.78** |
| Model4_Middle4Mean | 90.51 ± 0.67 | 90.11 ± 0.68 | 95.78 ± 0.43 | 90.20 | 89.76 | 95.55 |
> สรุป: ผลแต่ละสถาปัตยกรรม **แตกต่างกันเล็กน้อย (~<1%)**
> ใช้ `cnn_bilstm` เป็น **โมเดลหลักในการใช้งานจริง** และเปิด `last4weighted_bilstm` ให้เลือกสำหรับเคสเฉพาะ/เทียบผล
---
## การใช้งานที่ตั้งใจไว้ (Intended Use)
- วิเคราะห์ความรู้สึกรีวิว/คอมเมนต์ภาษาไทยแบบ 2 คลาส (positive/negative)
- เดโม Space รองรับ 3 โหมด: **Single**, **Batch (หลายบรรทัด)**, และ **CSV**
- CSV: ถ้าพบคอลัมน์ `review` จะใช้ทันที (ไม่พบจะเดาคอลัมน์ object ตัวแรก)
- ถ้ามีคอลัมน์ `shop` จะสรุปผลต่อร้าน + แสดงกราฟสรุป
---
## ข้อจำกัดและข้อควรระวัง
> โปรดพิจารณาใช้ร่วมกับกฎ/กระบวนการทวนโดยมนุษย์
- ภาษาพูด, สแลงเฉพาะกลุ่ม, ประชด/เสียดสี อาจทำให้ทำนายพลาด
- ข้อความนอกโดเมนที่ฝึก (เช่น สายวิชาการเฉพาะทาง, code-mixed อังกฤษมาก) อาจลดความแม่นยำ
- ค่า **probabilities** เป็นการประมาณเชิงสถิติ—not ground truth
- ควรลบข้อมูลส่วนบุคคล (PII) ก่อนส่งเข้าเดโมสาธารณะ
---
## โครงไฟล์ของรีโป
```markdown
common/models.py
baseline/
├─ config.json
└─ model.safetensors
cnn_bilstm/
├─ config.json
└─ model.safetensors
last4weighted_bilstm/
├─ config.json
└─ model.safetensors
requirements.txt
LICENSE
```
---
## Quickstart (Python)
> ต้องมี: `torch`, `transformers`, `safetensors`, `sentencepiece`, `huggingface_hub`
```bash
pip install -U torch transformers safetensors sentencepiece huggingface_hub
import json, importlib.util, torch, torch.nn.functional as F
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
from safetensors.torch import load_file
REPO_ID = "Dusit-P/thai-sentiment-wcb"
# เลือกหนึ่ง: "cnn_bilstm" | "baseline" | "last4weighted_bilstm"
MODEL_DIR = "cnn_bilstm"
# โหลดสถาปัตยกรรม (factory)
models_py = hf_hub_download(REPO_ID, filename="common/models.py")
spec = importlib.util.spec_from_file_location("models", models_py)
mod = importlib.util.module_from_spec(spec); spec.loader.exec_module(mod)
# โหลดคอนฟิก/น้ำหนัก
cfg_path = hf_hub_download(REPO_ID, filename=f"{MODEL_DIR}/config.json")
w_path = hf_hub_download(REPO_ID, filename=f"{MODEL_DIR}/model.safetensors")
cfg = json.load(open(cfg_path, "r", encoding="utf-8"))
tok = AutoTokenizer.from_pretrained(cfg["base_model"])
model = mod.create_model_by_name(cfg["arch"])
state = load_file(w_path); model.load_state_dict(state, strict=True)
model.eval()
def classify(text: str):
enc = tok([text], padding=True, truncation=True, max_length=cfg["max_len"], return_tensors="pt")
with torch.no_grad():
p = F.softmax(model(enc["input_ids"], enc["attention_mask"]), dim=1)[0].tolist()
probs = {"negative": float(p[0]), "positive": float(p[1])}
label = "positive" if probs["positive"] >= probs["negative"] else "negative"
return probs, label
print(classify("บริการดีมาก ประทับใจ"))
---
Space / REST API
Base URL: https://<YOUR_SPACE_URL>
API ด้านล่างอ้างอิงฟังก์ชันใน app.py ของ Space: predict_one, predict_many, predict_csv
หากคุณเปลี่ยนชื่อฟังก์ชัน/เส้นทาง ให้ปรับ URL ให้สอดคล้อง
1) Predict ข้อความเดียว
POST /run/predict_one
Body (JSON):
{
"data": ["อาหารอร่อยมาก บริการดี", "cnn_bilstm"]
}
-Response (ตัวอย่าง):
{
"data": [
{"negative": 0.12, "positive": 0.88},
"positive"
]
}
>-curl ตัวอย่าง:
curl -X POST "https://<YOUR_SPACE_URL>/run/predict_one" \
-H "content-type: application/json" \
-d '{"data":["อาหารอร่อยมาก บริการดี","cnn_bilstm"]}'
2) Predict หลายข้อความ (ทีละบรรทัด)
POST /run/predict_many
>Body (JSON):
{
"data": ["แย่มาก รอนานมาก\nอร่อย บริการไว", "cnn_bilstm"]
}
>-curl ตัวอย่าง:
curl -X POST "https://<YOUR_SPACE_URL>/run/predict_many" \
-H "content-type: application/json" \
-d '{"data":["แย่มาก รอนานมาก\nอร่อย บริการไว","cnn_bilstm"]}'
3) อัปโหลด CSV
POST /run/predict_csv (multipart/form-data)
Fields
file: ไฟล์ CSV (ต้องมีคอลัมน์ review; ถ้ามี shop จะแสดงสรุปต่อร้าน)
model_choice: cnn_bilstm | baseline | last4weighted_bilstm
>-curl ตัวอย่าง:
curl -X POST "https://<YOUR_SPACE_URL>/run/predict_csv" \
-F "file=@/path/to/reviews.csv" \
-F "model_choice=cnn_bilstm"
>บางเวอร์ชันของ Gradio มีปุ่ม “View API” บนหน้า Space เพื่อตรวจ schema/endpoint ล่าสุดอัตโนมัติ
---
#Reproducibility (ย่อ)
-Base: airesearch/wangchanberta-base-att-spm-uncased
-max_len=128, Batch size=16, Optimizer: AdamW (lr_bert=2e-5, lr_others=1e-3), Early stopping
-5-Fold Stratified, Seed=42
-ไลบรารีหลัก: torch, transformers, safetensors, sentencepiece
---
#License & Attribution
>Model license: MIT (ปรับได้ตามต้องการ)
>Dataset: Wisesight Sentiment — โปรดอ้างอิงและปฏิบัติตามสัญญาอนุญาตของชุดข้อมูลต้นทาง
---
#Citation
>Dusit P. (2025). Thai Sentiment WCB (WangchanBERTa + LSTM/CNN/Last4 heads).
>Hugging Face: Dusit-P/thai-sentiment-wcb.
>Demo: <https://<YOUR_SPACE_URL>>.
---
#Changelog
-v1.0.0 — ปล่อย cnn_bilstm, baseline, last4weighted_bilstm; เพิ่ม Space (UI/REST)
|
gumperto/Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-down-l14-r1
|
gumperto
| 2025-09-19T08:48:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T08:30:30Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-down-l14-r1
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-down-l14-r1
This model is a fine-tuned version of [unsloth/Qwen2.5-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-7B-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="gumperto/Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-down-l14-r1", 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/gumperto-waseda-university/clarifying-em/runs/ne27yjf3)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
kernels-test/state-test
|
kernels-test
| 2025-09-19T08:44:49Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T08:45:32Z |
---
license: apache-2.0
---
|
lyle49/xlmr-vi-nli
|
lyle49
| 2025-09-19T08:43:08Z | 10 | 0 | null |
[
"safetensors",
"xlm-roberta",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"region:us"
] | null | 2025-09-14T06:45:22Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr-vi-nli
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. -->
# xlmr-vi-nli
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2594
- Accuracy: 0.7310
- F1 Macro: 0.7300
## 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: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4874 | 1.0 | 9067 | 0.7752 | 0.6971 | 0.6955 |
| 0.3468 | 2.0 | 18134 | 0.8317 | 0.7157 | 0.7155 |
| 0.2151 | 3.0 | 27201 | 0.9919 | 0.7224 | 0.7220 |
| 0.1573 | 4.0 | 36268 | 1.2673 | 0.7255 | 0.7254 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
|
AnomalousPrime/truth-kernel-sdk
|
AnomalousPrime
| 2025-09-19T08:42:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-06T00:08:43Z |
---------
license: other
license_name: Proprietary - Sovereign License v1.0
pipeline_tag: text-classification
library_name: gradio
language:
- en
datasets: []
base_model: null
tags:
- truth-kernel
- sdk
- audit
- sha256
- operator-codex
- anomalous-prime
---
license: other
license_name: "Proprietary — Sovereign License v1.0"
pipeline_tag: text-classification
library_name: gradio
language: [en]
datasets: []
base_model: null
tags:
- truth-kernel
- sdk
- audit
- sha256
- operator-codex
- anomalous-prime
---
license: apache-2.0
language:
- en
metrics:
- accuracy
- rouge
base_model:
- nullt3r/Meta-Llama-3-8B-Instruct-64k-PoSE-Q8_0-GGUF
pipeline_tag: text-generation
tags:
- truth-kernel
- sdk
- kyron-hollowflame
- sovereign-ai
- chat-interface
- llm-toolkit
---
|
Danrisi/Lenovo_Qwen
|
Danrisi
| 2025-09-19T08:41:31Z | 0 | 20 | null |
[
"realistic",
"qwen",
"qwen-image",
"lora",
"text-to-image",
"en",
"base_model:Qwen/Qwen-Image",
"base_model:adapter:Qwen/Qwen-Image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-08-11T08:48:23Z |
---
license: apache-2.0
base_model: Qwen/Qwen-Image
language: en
pipeline_tag: text-to-image
tags:
- realistic
- qwen
- qwen-image
- lora
---
P.S: No need to use them. Here is an example of prompting:
`overexposed indoor scene, raw unedited amateurish candid shot of ...`
Also you can control: indoor/outdoor, overexposed/underexposed.
|
FreedomIntelligence/EchoX-3B
|
FreedomIntelligence
| 2025-09-19T08:37:11Z | 19 | 4 | null |
[
"safetensors",
"EchoX",
"audio-text-to-audio-text",
"speech-understanding",
"audio",
"chat",
"en",
"dataset:custom",
"arxiv:2509.09174",
"license:apache-2.0",
"region:us"
] | null | 2025-09-08T12:23:36Z |
---
language:
- en
tags:
- audio-text-to-audio-text
- speech-understanding
- audio
- chat
license: apache-2.0
datasets:
- custom
metrics:
- wer
- bleu
- AIR-Bench
---
<div align="center">
<h1>
EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs
</h1>
</div>
<p align="center">
<font size="3">
<a href="https://github.com/FreedomIntelligence/EchoX">🐈⬛ Github</a> ｜ 
<a href="https://arxiv.org/abs/2509.09174">📃 Paper</a> ｜ 
<a href="https://huggingface.co/spaces/FreedomIntelligence/EchoX">🚀 Space (8B)</a> ｜ 
<a href="https://huggingface.co/datasets/FreedomIntelligence/EchoX-Dialougues">📊 EchoX-Dialougues</a> ｜ 
<a href="https://huggingface.co/datasets/KurtDu/EchoX-Dialogues-Plus">📊 EchoX-Dialogues-Plus</a>
</font>
</p>
## Model Description
EchoX is a Speech-to-Speech large language model that addresses the acoustic-semantic gap. This is the 3B version. By introducing **Echo Training**, EchoX integrates semantic and acoustic learning, mitigating the degradation of reasoning ability observed in existing speech-based LLMs. It is trained on only 10k hours of data while delivering state-of-the-art results in knowledge-based question answering and speech interaction tasks.
### Key Features
<div>
<ul>
<font size="3"><li>Mitigates Acoustic-Semantic Gap in Speech-to-Speech LLMs</li></font>
<font size="3"><li>Introduces Echo Training with a Novel Three-Stage Pipeline (S2T, T2C, Echo)</li></font>
<font size="3"><li>Trained on Only 10k Hours of Curated Data, Ensuring Efficiency</li></font>
<font size="3"><li>Achieves State-of-the-Art Performance in Knowledge-Based QA Benchmarks</li></font>
<font size="3"><li>Preserves Reasoning and Knowledge Abilities for Interactive Speech Tasks</li></font>
</ul>
</div>
## Usage
Load the EchoX model and run inference with your audio files as shown in the <a href="https://github.com/FreedomIntelligence/EchoX">GitHub repository</a>.
# <span>📖 Citation</span>
```
@misc{zhang2025echoxmitigatingacousticsemanticgap,
title={EchoX: Towards Mitigating Acoustic-Semantic Gap via Echo Training for Speech-to-Speech LLMs},
author={Yuhao Zhang and Yuhao Du and Zhanchen Dai and Xiangnan Ma and Kaiqi Kou and Benyou Wang and Haizhou Li},
year={2025},
eprint={2509.09174},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2509.09174},
}
```
|
kishankachhadiya/AI-DEtector
|
kishankachhadiya
| 2025-09-19T08:34:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-09-18T11:25:28Z |
---
library_name: transformers
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: AI-DEtector
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. -->
# AI-DEtector
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1379
- Accuracy: 0.9513
- F1: 0.9717
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.1561 | 1.0 | 91568 | 0.1379 | 0.9513 | 0.9717 |
### Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.0
|
langtuphongtran/chi-phi-thay-man-hinh-iphone-11-pro
|
langtuphongtran
| 2025-09-19T08:34:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-26T02:48:21Z |
<h1><strong>Thay Màn Hình iPhone Chính Hãng – Dịch Vụ Chất Lượng Tại Bệnh Viện Điện Thoại, Laptop 24h</strong></h1>
<p>Khi màn hình iPhone của bạn bị hư hỏng, bạn sẽ cần biết <a href="https://chamsocdidong.com/thay-man-hinh-iphone-11-pro-ds2072" target="_blank">chi phí thay màn hình iPhone 11 Pro chính hãng</a> và tìm địa chỉ thay màn hình uy tín, chất lượng. <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> tự hào là địa chỉ đáng tin cậy cung cấp dịch vụ <strong>thay màn hình điện thoại iPhone tại chamsocdidong</strong>, đảm bảo chất lượng màn hình chính hãng, dịch vụ nhanh chóng và giá cả hợp lý. Hãy cùng tìm hiểu khi nào bạn cần thay màn hình iPhone, cũng như lý do tại sao <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> là lựa chọn lý tưởng cho dịch vụ này.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/man-hinh-iphone-bi-soc/Thay-man-hinh-iPhone/thay-man-hinh-iphone-11-pro.jpg" alt="" /></p>
<h3>Khi Nào Cần Thay Màn Hình iPhone?</h3>
<p>Màn hình iPhone là bộ phận quan trọng, không chỉ ảnh hưởng đến giao diện mà còn đến trải nghiệm sử dụng của người dùng. Việc thay màn hình sẽ giúp bạn duy trì hiệu suất sử dụng ổn định và cải thiện chất lượng hiển thị. Sau đây là những dấu hiệu cho thấy bạn cần <a href="https://sketchfab.com/thaymanhinhiphone24h" target="_blank">thay màn hình điện thoại iPhone tại chamsocdidong</a>:</p>
<ol>
<li>
<p><strong>Màn hình bị vỡ hoặc nứt</strong>: Một trong những nguyên nhân phổ biến khiến người dùng phải thay màn hình iPhone là do màn hình bị vỡ hoặc nứt sau khi điện thoại bị rơi hoặc va đập mạnh. Màn hình vỡ không chỉ ảnh hưởng đến thẩm mỹ mà còn có thể gây ra các vấn đề về cảm ứng và hiển thị.</p>
</li>
<li>
<p><strong>Màn hình không phản hồi cảm ứng</strong>: Nếu màn hình của bạn không phản hồi khi chạm vào, hoặc có các điểm chết không thể cảm ứng được, đây là dấu hiệu rõ ràng của việc màn hình bị lỗi và cần thay thế.</p>
</li>
<li>
<p><strong>Màn hình bị mờ, ố màu hoặc điểm chết</strong>: Màn hình hiển thị không rõ ràng, bị mờ hoặc có điểm chết sẽ làm ảnh hưởng đến trải nghiệm người dùng. Điều này thường xuất hiện sau một thời gian dài sử dụng hoặc khi màn hình bị tác động mạnh.</p>
</li>
<li>
<p><strong>Màn hình bị chảy mực hoặc xuất hiện vết đen</strong>: Khi màn hình xuất hiện những vết đen hoặc chảy mực, có thể do sự cố trong quá trình sản xuất hoặc tác động vật lý mạnh. Để tránh làm giảm trải nghiệm, bạn nên thay màn hình ngay lập tức.</p>
</li>
</ol>
<p>Nếu bạn gặp phải những vấn đề trên, đừng ngần ngại đến <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để <strong>thay màn hình điện thoại iPhone tại chamsocdidong</strong> với màn hình chính hãng và chất lượng.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-iphone-11-pro/truoc-va-sau-khi-thay-man-hinh-iphone-11-pro.jpg" alt="" /></p>
<h3>Địa Chỉ Thay Màn Hình iPhone Chính Hãng Giá Rẻ</h3>
<p>Khi lựa chọn dịch vụ thay màn hình iPhone, điều quan trọng là bạn phải tìm được một địa chỉ uy tín, cung cấp <strong>màn hình iPhone chính hãng</strong> với giá cả hợp lý. <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> là địa chỉ tin cậy mà bạn có thể đến để thay màn hình iPhone. Chúng tôi cam kết cung cấp dịch vụ thay màn hình iPhone chính hãng với <strong>giá rẻ</strong>, giúp bạn tiết kiệm chi phí mà vẫn đảm bảo chất lượng.</p>
<p>Chúng tôi sử dụng màn hình chính hãng từ Apple, bảo đảm mang lại chất lượng hiển thị sắc nét, độ bền cao và cảm ứng mượt mà. Ngoài ra, chúng tôi luôn cung cấp dịch vụ minh bạch về giá, không có phí phát sinh bất ngờ. Bảng giá thay màn hình được công khai rõ ràng trên website của chúng tôi và sẽ luôn có mức giá cạnh tranh trên thị trường.</p>
<p><strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> còn có đội ngũ kỹ thuật viên giàu kinh nghiệm, sử dụng thiết bị hiện đại để thay màn hình nhanh chóng và an toàn. Quá trình thay màn hình tại chúng tôi diễn ra nhanh chóng, giúp bạn tiết kiệm thời gian mà vẫn đảm bảo chất lượng dịch vụ.</p>
<h3>Thay Màn Hình iPhone Có Ảnh Hưởng Gì Đến Máy Không?</h3>
<p>Một câu hỏi mà nhiều người dùng iPhone quan tâm là liệu việc <strong>thay màn hình iPhone</strong> có ảnh hưởng đến các bộ phận khác trong máy hay không. Điều này hoàn toàn có thể xảy ra nếu không thực hiện đúng quy trình thay thế hoặc sử dụng linh kiện không chính hãng. Tuy nhiên, khi bạn đến với <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong>, bạn có thể yên tâm về chất lượng dịch vụ:</p>
<ul>
<li>
<p><strong>Linh kiện chính hãng</strong>: Chúng tôi chỉ sử dụng màn hình chính hãng từ Apple, đảm bảo tính tương thích hoàn hảo với các dòng iPhone. Việc sử dụng màn hình chính hãng không chỉ đảm bảo chất lượng hiển thị mà còn bảo vệ các bộ phận khác trong máy khỏi các tác động xấu.</p>
</li>
<li>
<p><strong>Quy trình thay màn hình chuẩn</strong>: Các kỹ thuật viên của chúng tôi sẽ thực hiện việc thay màn hình đúng quy trình, không làm ảnh hưởng đến các linh kiện quan trọng khác của iPhone như vi xử lý, bộ nhớ, hay pin.</p>
</li>
<li>
<p><strong>Không làm giảm hiệu suất</strong>: Sau khi thay màn hình, chiếc iPhone của bạn sẽ hoạt động như mới, không gặp phải bất kỳ vấn đề nào về hiệu suất hoặc các tính năng của máy. Màn hình thay thế sẽ không làm giảm khả năng cảm ứng hay độ sáng màn hình.</p>
</li>
</ul>
<p>Với dịch vụ thay màn hình tại <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong>, bạn sẽ không phải lo lắng về việc ảnh hưởng đến các bộ phận khác trong máy.</p>
<h3>Màn Hình Chính Hãng Tại Bệnh Viện Điện Thoại, Laptop 24h</h3>
<p>Chúng tôi luôn cam kết sử dụng <strong>màn hình chính hãng</strong> để thay thế cho khách hàng. Màn hình chính hãng mang lại nhiều ưu điểm vượt trội mà bạn không thể bỏ qua:</p>
<ul>
<li>
<p><strong>Chất lượng hiển thị vượt trội</strong>: Màn hình chính hãng cung cấp màu sắc sắc nét, độ sáng và độ tương phản tốt, mang lại trải nghiệm hình ảnh chân thực và sống động. Bạn sẽ không cảm thấy sự khác biệt so với màn hình nguyên bản khi sử dụng.</p>
</li>
<li>
<p><strong>Độ bền cao</strong>: Màn hình chính hãng có khả năng chống va đập và trầy xước tốt hơn so với màn hình thay thế không chính hãng, giúp bảo vệ chiếc iPhone của bạn lâu dài hơn.</p>
</li>
<li>
<p><strong>Tương thích hoàn hảo</strong>: Màn hình chính hãng được thiết kế đặc biệt để tương thích hoàn hảo với các dòng iPhone, giúp bạn duy trì tính năng cảm ứng mượt mà và các chức năng của máy hoạt động ổn định.</p>
</li>
</ul>
<p>Với những ưu điểm này, màn hình chính hãng không chỉ giúp bạn cải thiện trải nghiệm sử dụng mà còn bảo vệ chiếc iPhone của bạn trong suốt thời gian dài.</p>
<p style="text-align: center;"><img src="https://chamsocdidong.com/upload_images/images/thay-man-hinh-iphone-11-pro/thay-man-hinh-iphone-11-pro-25.jpg" alt="" /></p>
<h3>Hướng Dẫn Sử Dụng Dịch Vụ Tại Bệnh Viện Điện Thoại, Laptop 24h</h3>
<p>Nếu bạn cần thay màn hình iPhone, hãy đến ngay <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để được tư vấn và sử dụng dịch vụ thay màn hình chất lượng cao. Để sử dụng dịch vụ của chúng tôi, bạn chỉ cần làm theo các bước đơn giản sau:</p>
<ol>
<li>
<p><strong>Liên hệ với chúng tôi</strong>: Bạn có thể truy cập website chamsocdidong.com hoặc gọi điện thoại để đặt lịch thay màn hình hoặc yêu cầu tư vấn chi tiết về dịch vụ.</p>
</li>
<li>
<p><strong>Mang máy đến trung tâm</strong>: Sau khi đặt lịch, bạn có thể mang chiếc iPhone của mình đến trực tiếp cửa hàng hoặc gửi qua dịch vụ chuyển phát để chúng tôi thực hiện thay màn hình.</p>
</li>
<li>
<p><strong>Thay màn hình nhanh chóng và chuyên nghiệp</strong>: Với đội ngũ kỹ thuật viên giàu kinh nghiệm, chúng tôi sẽ thực hiện thay màn hình nhanh chóng và đảm bảo chất lượng.</p>
</li>
<li>
<p><strong>Nhận bảo hành</strong>: Sau khi thay màn hình, bạn sẽ nhận được chứng nhận bảo hành dài hạn cho màn hình thay thế, giúp bạn an tâm sử dụng.</p>
</li>
</ol>
<p>Hãy đến với <strong>Bệnh Viện Điện Thoại, Laptop 24h</strong> để thay màn hình iPhone chính hãng, chất lượng và giá cả hợp lý. Để biết thêm chi tiết và đặt lịch thay màn hình, hãy liên hệ với chúng tôi ngay hôm nay!</p>
|
aamijar/MaskLLM-Llama-2-7b-hf-lora-r8-boolq
|
aamijar
| 2025-09-19T08:33:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T08:33:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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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. -->
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## Model Card Contact
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|
IeBoytsov/ox-llms-sula-sft-22-countries
|
IeBoytsov
| 2025-09-19T08:32:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"alignment-handbook",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T07:33:02Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: ox-llms-sula-sft-22-countries
tags:
- generated_from_trainer
- sft
- trl
- alignment-handbook
licence: license
---
# Model Card for ox-llms-sula-sft-22-countries
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-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="IeBoytsov/ox-llms-sula-sft-22-countries", 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/ilyaboytsov1805/huggingface/runs/43cac6ca)
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.1
- Tokenizers: 0.22.0
## 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}}
}
```
|
NiklasKoch/qwen-discussion-classifier
|
NiklasKoch
| 2025-09-19T08:32:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"text-classification",
"reddit",
"conversation-analysis",
"constructive-dialogue",
"qwen",
"lora",
"transformers",
"en",
"dataset:reddit",
"base_model:Qwen/Qwen3-Embedding-0.6B",
"base_model:adapter:Qwen/Qwen3-Embedding-0.6B",
"region:us"
] |
text-classification
| 2025-09-19T07:35:54Z |
---
base_model: Qwen/Qwen3-Embedding-0.6B
library_name: peft
tags:
- text-classification
- reddit
- conversation-analysis
- constructive-dialogue
- qwen
- lora
- transformers
language:
- en
datasets:
- reddit
pipeline_tag: text-classification
repo_url: https://github.com/Niklas257/Reddit-Constructiveness-Classification.git
---
# Qwen Constructive Conversation Classifier
A fine-tuned Qwen 3 Embedding model for classifying constructive vs non-constructive conversations from online discussion platforms like Reddit.
## Model Description
This model is a QLoRA (Quantized LoRA) fine-tuned version of `Qwen/Qwen3-Embedding-0.6B` specifically trained to identify constructive conversations in online discussion threads. The model was trained using self-training techniques on Reddit discussion data.
- **Model Type**: Text Classification (Binary)
- **Base Model**: Qwen/Qwen3-Embedding-0.6B
- **Training Method**: QLoRA with self-training
- **Task**: Binary classification of conversation constructiveness
- **Language**: English
### Model Source
- **Repository**: https://github.com/Niklas257/Reddit-Constructiveness-Classification.git
## Intended Uses
### Primary Use Case
- Classifying Reddit discussions as constructive or non-constructive
- Content moderation assistance
- Conversation quality analysis
- Social media research
### Direct Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch
# Load base model and tokenizer
base_model_name = "Qwen/Qwen3-Embedding-0.6B"
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForSequenceClassification.from_pretrained(
base_model_name,
num_labels=2
)
# Load the fine-tuned adapters
model = PeftModel.from_pretrained(model, "NiklasKoch/qwen-discussion-classifier")
model.eval()
# Classify text
def classify_text(text):
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
padding=True,
max_length=4096
)
# Move inputs to same device as model (important for GPU usage)
inputs = {k: v.to(next(model.parameters()).device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
# 0 = non-constructive, 1 = constructive
predicted_class = torch.argmax(predictions, dim=-1).item()
confidence = predictions[0][predicted_class].item()
return {
'class': 'constructive' if predicted_class == 1 else 'non-constructive',
'confidence': confidence,
'scores': {
'non-constructive': predictions[0][0].item(),
'constructive': predictions[0][1].item()
}
}
# Example usage
text = "[author0] LEGO: What do you think you're doing?!? [author1] I don't get it did he reveal bionicle reboot or smthn? [author2] Not really, he did announce something but was super vague, seems like a sort of passion project we wants to do with the community, he even said it might not even be bionicle. [author1] So is that image fan made or is it one of his passion projects [author2] Those pictures are real and on his insta, he did a stream talking about it I\u2019m sure you can find somewhere, search up Fabre bionicle stream 2020 or something. [author1] OK thanks"
result = classify_text(text)
print(result)
```
## Training Details
### Training Data
- **Source**: https://archive.org/download/pushshift_reddit_200506_to_202212/
- **Size**: The dataset I used contained a total of ~1.4 million Reddit threads filtered for English language and a minimum of 2 authors per thread.
- **Labels**: Binary (constructive/non-constructive conversations)
- **Additional Data**: YNACC and IAC datasets for initial supervised training
### Training Procedure
- **Training Method**: Self-Training
- **Quantization**: 4-bit QLoRA
- **LoRA Config**:
- `r`: 16
- `lora_alpha`: 32
- `lora_dropout`: 0.1
- Target modules: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
- **Loss Function**: Focal Loss with class weighting
- **Max Sequence Length**: 4096 tokens
- **Batch Size**: 64
- **Learning Rate**: 2e-6
### Training Hardware
- 48 hours on 4x NVIDIA A100 40GB GPUs
## Performance
### Evaluation Results
```
YNACC:
Accuracy: 0.70
Precision: 0.72
F1-Score: 0.69
IAC:
Accuracy: 0.78
Precision: 0.86
F1-Score: 0.86
Reddit:
Accuracy: 0.64
Precision: 0.76
F1-Score: 0.74
```
## Limitations and Bias
- **Language**: English only
- **Bias**: May reflect biases present in Reddit discussions and training data
## Ethical Considerations
- Human oversight is recommended for important moderation decisions
## Technical Specifications
- **Model Architecture**: Qwen 3 Embedding + Classification Head
- **Parameters**: ~600M base + LoRA adapters + classification head
- **Precision**: 4-bit quantized base model with full-precision adapters
- **Framework**: PyTorch, Transformers, PEFT (any recent version - you may see harmless warnings about configuration parameters)
## Model Card Authors
Niklas Koch, Georg August University of Göttingen
## Model Card Contact
[email protected]
|
Honeywithcrypto/Qwen3-0.6B-Gensyn-Swarm-roaring_nimble_chinchilla
|
Honeywithcrypto
| 2025-09-19T08:31:56Z | 157 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am roaring_nimble_chinchilla",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-14T21:07:57Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am roaring_nimble_chinchilla
---
# 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]
|
NatLibFi/FintoAI-data-YSO
|
NatLibFi
| 2025-09-19T08:31:05Z | 0 | 2 | null |
[
"glam",
"lam",
"subject indexing",
"annif",
"text-classification",
"fi",
"en",
"sv",
"dataset:NatLibFi/Finna-metadata",
"license:cc0-1.0",
"region:us"
] |
text-classification
| 2024-01-12T08:23:08Z |
---
datasets:
- NatLibFi/Finna-metadata
language:
- fi
- en
- sv
license: cc0-1.0
pipeline_tag: text-classification
tags:
- glam
- lam
- subject indexing
- annif
thumbnail: https://raw.githubusercontent.com/NatLibFi/FintoAI/main/ai.finto.fi/static/img/finto-ai-social.png
---
# FintoAI-data-YSO
This repository is for the [Annif](https://annif.org) projects with the
[YSO vocabulary](https://finto.fi/yso)
used at the [Finto AI service](https://ai.finto.fi/).
The current models were published there 2025-09-19.
The models have been trained on Python 3.11.11 with Annif version 1.4.0.
The configurations of the projects are in individual `.cfg` files in the root of
this repository.
The GitHub repository [FintoAI-data-YSO](https://github.com/NatLibFi/FintoAI-data-YSO) contains the
project configurations and the DVC pipeline used in training and
evaluating the projects.
The training corpora that are public can be found from the [Annif-corpora repository](https://github.com/NatLibFi/Annif-corpora/).
## Usage
To download selected projects with Annif use the `annif download` command, for
example run
annif download "*-fi" NatLibFi/FintoAI-data-YSO
See [Annif wiki](https://github.com/NatLibFi/Annif/wiki/Hugging-Face-Hub-integration) for more information.
<!--- start-of-autoupdating-part --->
## Projects
```
Project ID Project Name Vocabulary ID Language
-----------------------------------------------------------------------
yso-bonsai-en YSO Omikuji Bonsai English yso en
yso-bonsai-fi YSO Omikuji Bonsai Finnish yso fi
yso-bonsai-sv YSO Omikuji Bonsai Swedish yso sv
yso-en YSO English (2025.7.Laotse) yso en
yso-fasttext-en YSO fastText English yso en
yso-fasttext-fi YSO fastText Finnish yso fi
yso-fasttext-sv YSO fastText Swedish yso sv
yso-fi YSO suomi (2025.7.Laotse) yso fi
yso-mllm-en YSO MLLM English yso en
yso-mllm-fi YSO MLLM Finnish yso fi
yso-mllm-sv YSO MLLM Swedish yso sv
yso-sv ALLFO svenska (2025.7.Laotse) yso sv
```
<!--- end-of-autoupdating-part --->
|
gumperto/Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-all-full-r32
|
gumperto
| 2025-09-19T08:27:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"unsloth",
"sft",
"conversational",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T08:00:42Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
library_name: transformers
model_name: Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-all-full-r32
tags:
- generated_from_trainer
- trl
- unsloth
- sft
licence: license
---
# Model Card for Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-all-full-r32
This model is a fine-tuned version of [unsloth/Qwen2.5-7B-Instruct](https://huggingface.co/unsloth/Qwen2.5-7B-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="gumperto/Qwen2.5-7B-Instruct-emergent-finetune-backwards_samples-all-full-r32", 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/gumperto-waseda-university/clarifying-em/runs/ne27yjf3)
This model was trained with SFT.
### Framework versions
- TRL: 0.24.0.dev0
- Transformers: 4.56.1
- Pytorch: 2.8.0
- Datasets: 4.1.0
- Tokenizers: 0.22.0
## 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}}
}
```
|
luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-0.02-v3_9221
|
luckeciano
| 2025-09-19T08:26:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T05:24:37Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-0.02-v3_9221
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-0.02-v3_9221
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-0.02-v3_9221", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/9oklmlbm)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## 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}}
}
```
|
chriswang2025/wan2-2-ev-3
|
chriswang2025
| 2025-09-19T08:25:46Z | 0 | 0 | null |
[
"feature",
"training",
"new",
"wavespeed",
"license:other",
"region:us"
] | null | 2025-09-19T08:25:03Z |
---
tags:
- feature
- training
- new
- wavespeed
base_model: undefined
instance_prompt: trigger_word
license: other
---
# wavespeed-ai/wan-2.2-i2v-lora-trainer
<Gallery />
## Model description
## Trigger words
You should use `trigger_word` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/chriswang2025/wan2-2-ev-3/tree/main) them in the Files & versions tab.
## Training at wavespeed.ai
Training was done using [wavespeed.ai/models/wavespeed-ai/wan-2.2-i2v-lora-trainer](https://wavespeed.ai/models/wavespeed-ai/wan-2.2-i2v-lora-trainer).
|
ykail/pi0_droid_fp32
|
ykail
| 2025-09-19T08:24:48Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-09-19T08:03:49Z |
# π₀ - Droid
This is a PyTorch version of the PI0 `pi0_droid model`, converted from the original JAX/Flax implementation.
## Model Details
- **Architecture**: PI0 (Vision-Language-Action model)
- **Model Type**: PI0
- **Domain**: DROID (robotic manipulation)
- **Precision**: 32-bit floating point (fp32)
- **Vision Model**: PaliGemma (gemma_2b)
- **Action Expert**: gemma_300m
## Key Features
- **Vision-Language-Action**: Multimodal model combining vision, language, and action
- **PaliGemma Backbone**: Leverages PaliGemma for vision-language understanding
- **Continuous State Input**: Direct continuous state input processing
## Conversion Details
This model was converted from JAX to PyTorch using the OpenPI conversion script:
```bash
python examples/convert_jax_model_to_pytorch.py \
--checkpoint_dir /pi0_droid \
--config_name pi0_droid \
--output_path /pi0_droid/pytorch/fp32/ \
--precision float32
```
## Usage
```python
from openpi.models_pytorch.pi0_pytorch import PI0Pytorch
import torch
# Load the model
model = PI0Pytorch.from_pretrained("pepijn223/pi0_droid_fp32")
# The model expects inputs in the format:
# - images: torch.Tensor of shape [batch, height, width, channels]
# - text: tokenized text prompts
# - proprioceptive_state: robot state information (if applicable)
```
## Model Architecture
The model consists of:
1. **Vision Encoder**: PaliGemma-based vision processing
2. **Language Encoder**: Text prompt understanding
3. **Action Expert**: Specialized network for action prediction
4. **Integration Layer**: Combines multimodal information for action output
## Training Data
This model was trained on robotics datasets appropriate for its domain:
- **DROID models**: Trained on diverse robot manipulation data
- **LIBERO models**: Trained on diverse tabletop manipulation scenarios
- **Base models**: Trained on general robotics datasets
## Limitations
- Model performance depends on similarity between deployment and training environments
- May require domain-specific fine-tuning for optimal performance
- Action space must match the trained action dimension (32)
## Citation
If you use this model, please cite the original OpenPI work:
```bibtex
@article{openpi2024,
title={Open-World Robotic Manipulation with Vision-Language-Action Models},
author={Physical Intelligence},
year={2024},
url={https://github.com/Physical-Intelligence/openpi}
}
```
## Original Repository
[OpenPI GitHub Repository](https://github.com/Physical-Intelligence/openpi)
## License
This model follows the same license as the original OpenPI repository.
|
JonusNattapong/romeo-v7
|
JonusNattapong
| 2025-09-19T08:24:33Z | 0 | 0 |
sklearn
|
[
"sklearn",
"trading",
"finance",
"gold",
"xauusd",
"forex",
"algorithmic-trading",
"smart-money-concepts",
"smc",
"xgboost",
"lightgbm",
"machine-learning",
"backtesting",
"technical-analysis",
"multi-timeframe",
"intraday-trading",
"high-frequency-trading",
"ensemble-model",
"capital-preservation",
"risk-management",
"recovery-mechanisms",
"en",
"dataset:yahoo-finance-gc-f",
"license:mit",
"model-index",
"region:us"
] | null | 2025-09-19T08:24:24Z |
---
language: en
license: mit
library_name: sklearn
tags:
- trading
- finance
- gold
- xauusd
- forex
- algorithmic-trading
- smart-money-concepts
- smc
- xgboost
- lightgbm
- machine-learning
- backtesting
- technical-analysis
- multi-timeframe
- intraday-trading
- high-frequency-trading
- ensemble-model
- capital-preservation
- risk-management
- recovery-mechanisms
datasets:
- yahoo-finance-gc-f
metrics:
- accuracy
- precision
- recall
- f1
- sharpe
- max_drawdown
- cagr
- win_rate
- profit_factor
- capital_preservation_score
model-index:
- name: romeo-v7-15m
results:
- task:
type: binary-classification
name: 15-Minute Price Direction Prediction with Capital Preservation
dataset:
type: yahoo-finance-gc-f
name: Gold Futures (GC=F)
metrics:
- type: accuracy
value: 57.1
name: Win Rate
- type: profit_factor
value: 2.10
name: Profit Factor
- type: max_drawdown
value: 8.2
name: Max Drawdown
- type: capital_preservation_score
value: 28.4
name: Capital Preservation Score
---
# Romeo V7 — Capital Preservation & Recovery Trading Model
## Model Details
### Model Description
Romeo V7 is an enhanced version of Romeo V6 with advanced capital preservation strategies, recovery mechanisms, and consistent profitability features. It combines tree-based models (XGBoost and LightGBM) with sophisticated risk management to provide stable returns with lower drawdown.
- **Model Type**: Ensemble Classifier with Capital Preservation (XGBoost + LightGBM)
- **Asset**: XAUUSD (Gold Futures)
- **Strategy**: Smart Money Concepts (SMC) with capital preservation and recovery
- **Prediction Horizon**: 15-minute intraday (next bar direction)
- **Framework**: Scikit-learn, XGBoost, LightGBM
### Key Enhancements over V6
- **Dynamic Position Sizing**: Adjusts position sizes based on current capital and drawdown
- **Recovery Mechanisms**: Reduces risk during drawdown periods, increases confidence during profitable periods
- **Confidence-Based Filtering**: Only trades high-confidence signals with volume and volatility confirmation
- **Capital Preservation Rules**: Multiple safety checks to protect capital during adverse conditions
- **Volatility Adjustment**: Reduces position sizes during high volatility periods
### Model Architecture
- **Ensemble Components**:
- XGBoost Classifier: Gradient boosting with conservative parameters
- LightGBM Classifier: Efficient gradient boosting with risk-aware features
- **Enhanced Features**: 52 features including capital preservation indicators, recovery signals, and risk metrics
- **Capital Preservation Engine**: Dynamic position sizing, confidence filtering, recovery mode logic
- **Serialization**: Tree models saved in joblib format
### Intended Use
- **Primary Use**: Research, backtesting, and evaluation on historical XAUUSD data with capital preservation
- **Secondary Use**: Educational purposes for understanding risk-managed trading models
- **Out-of-Scope**: Not financial advice. Requires proper validation and risk controls for live trading
### Factors
- **Relevant Factors**: Market volatility, economic indicators, capital preservation requirements
- **Evaluation Factors**: Tested on unseen data with realistic slippage, commission, and risk management
### Metrics (Capital Preservation Mode)
- **Evaluation Data**: Unseen 15m intraday data (out-of-sample)
- **Risk Parameters**: 10% risk per trade, 2% stop loss, 5% take profit
- **Capital Preservation Settings**: 65% confidence threshold, dynamic sizing enabled
- **Metrics**:
- Initial Capital: 100
- Final Capital: 144.24
- Total Return: 44.24%
- Max Drawdown: 8.2%
- Total Trades: 133
- Win Rate: 57.1%
- Profit Factor: 2.10
- Sharpe Ratio: 4.37
- Capital Preservation Score: 28.4/100
- Recovery Effectiveness: 100%
- Risk-Adjusted Return: 5.38
- High Confidence Trades: 98/133 (74%)
- Recovery Mode Trades: 0/133 (0%)
### Capital Preservation Features
- **Dynamic Position Sizing**: Adjusts based on capital, drawdown, and volatility
- **Recovery Mode**: Activates when drawdown exceeds 85%, reduces risk by 50%
- **Confidence Filtering**: Minimum 65% confidence required for trades
- **Volatility Control**: Reduces position sizes during high volatility (>1.5% ATR)
- **Volume Confirmation**: Requires volume above 20-period average for entry
- **Safe Zone Trading**: Prefers entries within support/resistance levels
### Usage Instructions
```python
from v7.backtest_v7 import CapitalPreservationBacktester
# Initialize with capital preservation settings
backtester = CapitalPreservationBacktester({
'confidence_threshold': 0.65,
'max_risk_per_trade': 0.15,
'recovery_mode_threshold': 0.85,
'volatility_adjustment': True,
'dynamic_position_sizing': True
})
# Run backtest
results = backtester.backtest_capital_preservation(
risk_per_trade=0.10,
stop_loss=0.02,
take_profit=0.05
)
```
### Risk Management
- **Maximum Risk per Trade**: 15% of current capital
- **Recovery Mode Threshold**: 85% drawdown triggers reduced risk
- **Stop Trading Threshold**: 95% drawdown stops all trading
- **Profit Target Reset**: Returns to normal risk after 2% profit recovery
- **Volatility Filter**: Skips trades when volatility > 2%
### Performance Comparison vs V6
| Metric | Romeo V6 | Romeo V7 | Improvement |
|--------|----------|----------|-------------|
| Total Return | 10.79% | 44.24% | +33.45% |
| Max Drawdown | Higher | 8.2% | Lower |
| Win Rate | 49.28% | 57.1% | +7.82% |
| Profit Factor | ~1.5 | 2.10 | +0.6 |
| Sharpe Ratio | N/A | 4.37 | N/A |
| Capital Preservation | Basic | Advanced | Major |
### Training Data
- **Source**: Yahoo Finance GC=F (Gold Futures)
- **Timeframe**: 15-minute intraday data
- **Period**: Historical data with enhanced feature engineering
- **Augmentation**: Noise injection for robustness
- **Validation**: Out-of-sample testing with capital preservation metrics
### Ethical Considerations
- Designed for capital preservation and risk management
- Includes multiple safety mechanisms to prevent excessive losses
- Recovery mechanisms help maintain trading capital during adverse conditions
- All results are historical backtests, not guaranteed future performance
### Maintenance
- Retrain monthly with fresh data
- Monitor capital preservation metrics
- Adjust confidence thresholds based on market conditions
- Validate recovery mechanisms effectiveness
---
*Romeo V7 represents a significant advancement in algorithmic trading with a focus on capital preservation and consistent profitability.*
|
BagOu22/Lora_ERIC_LE_Z
|
BagOu22
| 2025-09-19T08:22:13Z | 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
| 2025-09-19T08:08:00Z |
---
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: ERIC_LE_Z
---
# Lora_Eric_Le_Z
<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 `ERIC_LE_Z` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ERIC_LE_Z",
"lora_weights": "https://huggingface.co/BagOu22/Lora_ERIC_LE_Z/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('BagOu22/Lora_ERIC_LE_Z', weight_name='lora.safetensors')
image = pipeline('ERIC_LE_Z').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BagOu22/Lora_ERIC_LE_Z/discussions) to add images that show off what you’ve made with this LoRA.
|
AmberYifan/qwen2.5-0.5b-instruct-full-pretrain-mix-mid-tweet-1m-en
|
AmberYifan
| 2025-09-19T08:21:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T04:52:13Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen2.5-0.5b-instruct-full-pretrain-mix-mid-tweet-1m-en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qwen2.5-0.5b-instruct-full-pretrain-mix-mid-tweet-1m-en
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the mix_mid_tweet_1m_en dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
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