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2025-06-22 06:27:16
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Ayesha-Khan-Official-Viral-Video/FULL.VIDEO.Ayesha.Khan.Viral.Video.Tutorial.Official | Ayesha-Khan-Official-Viral-Video | 2025-06-16T05:36:03Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:35:35Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
s-emanuilov/Tucan-2.6B-v1.0 | s-emanuilov | 2025-06-16T05:35:49Z | 187 | 1 | null | [
"safetensors",
"gemma2",
"function_calling",
"MCP",
"tool_use",
"bg",
"arxiv:2503.23278",
"arxiv:2408.00118",
"arxiv:2412.10893",
"base_model:INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0",
"base_model:finetune:INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0",
"license:gemma",
"region:us"
] | null | 2025-06-07T21:26:39Z | ---
license: gemma
language:
- bg
base_model:
- INSAIT-Institute/BgGPT-Gemma-2-2.6B-IT-v1.0
tags:
- function_calling
- MCP
- tool_use
---
# Tucan-2.6B-v1.0
## Bulgarian Language Models for Function Calling 🇧🇬
> 📄 **Full methodology, dataset details, and evaluation results coming in the upcoming paper**
## Overview 🚀
TUCAN (Tool-Using Capable Assistant Navigator) is a series of open-source Bulgarian language models fine-tuned specifically for function calling and tool use.
These models can interact with external tools, APIs, and databases, making them appropriate for building AI agents and [Model Context Protocol (MCP)](https://arxiv.org/abs/2503.23278) applications.
Built on top of [BgGPT models](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe) from [INSAIT Institute](https://insait.ai/), which were themselves built on [Gemma 2](https://arxiv.org/pdf/2408.00118), Tucan models have been enhanced with function-calling capabilities.
## Motivation 🎯
Although BgGPT models demonstrate [strong Bulgarian language comprehension](https://arxiv.org/pdf/2412.10893), they face challenges in maintaining the precise formatting necessary for consistent function calling. Despite implementing detailed system prompts, their performance in this specific task remains suboptimal.
This project addresses that gap by fine-tuning BgGPT, providing the Bulgarian AI community with proper tool-use capabilities in their native language.
## Models and variants 📦
Available in three sizes with full models, LoRA adapters, and quantized GGUF variants:
<div align="center">
| Model Size | Full Model | LoRA Adapter | GGUF (Quantized) |
|------------|------------|--------------|------------------|
| **2.6B** | [Tucan-2.6B-v1.0](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0) 📍| [LoRA](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0-LoRA) | [GGUF](https://huggingface.co/s-emanuilov/Tucan-2.6B-v1.0-GGUF) |
| **9B** | [Tucan-9B-v1.0](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0) | [LoRA](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0-LoRA) | [GGUF](https://huggingface.co/s-emanuilov/Tucan-9B-v1.0-GGUF) |
| **27B** | [Tucan-27B-v1.0](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0) | [LoRA](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0-LoRA) | [GGUF](https://huggingface.co/s-emanuilov/Tucan-27B-v1.0-GGUF) |
*GGUF variants include: q4_k_m, q5_k_m, q6_k, q8_0, q4_0 quantizations*
📍 *Current model/repo*
</div>
Models and quantizations are also available for easy use in Ollama: https://ollama.com/s_emanuilov/tucan
## Benchmarks 📊
All evaluations were performed using the [Tucan evaluation framework](https://github.com/s-emanuilov/tucan), with results averaged across multiple runs. Tucan models demonstrate superior function-calling capabilities compared to their BgGPT counterparts, with particularly strong improvements in smaller model sizes. To ensure no catastrophic forgetting occurred, we evaluated knowledge retention using [EleutherAI's lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) on Bulgarian benchmarks, confirming that each Tucan model maintains performance on par with its BgGPT equivalent.
<div align="center">
| Model | Function Calling | HellaswagBG | WinograndeBG | ARC-Easy-BG | ARC-Challenge-BG |
|-------|-----------------|-------------|--------------|-------------|------------------|
| **Tucan-2.6B-v1.0** 🔥 | **0.7875** | 0.5924 | 0.6456 | 0.5657 | 0.3754 |
| **Tucan-9B-v1.0** 🔥 | **0.8667** | 0.7046 | 0.7151 | 0.7024 | 0.5188 |
| **Tucan-27B-v1.0** 🔥 | **0.875** | 0.6179 | 0.6275 | 0.6486 | 0.442 |
| BgGPT-Gemma-2-2.6B-IT-v1.0 | 0.5874 | 0.6306 | 0.5821 | 0.5657 | 0.372 |
| BgGPT-Gemma-2-9B-IT-v1.0 | 0.7833 | 0.7057 | 0.719 | 0.7231 | 0.5188 |
| BgGPT-Gemma-2-27B-IT-v1.0 | 0.8667 | 0.62 | 0.6212 | 0.6587 | 0.459 |
*Note: 27B models were evaluated in 8-bit precision for comparison purposes.*
</div>
## Usage 🛠️
### Quick start ⚡
```bash
pip install -U "transformers[torch]" accelerate bitsandbytes
```
### Prompt format ⚙️
**Critical:** Use this format for function calling for the best results.
<details>
<summary><strong>📋 Required system prompt template</strong></summary>
```
<bos><start_of_turn>user
Ти си полезен AI асистент, който предоставя полезни и точни отговори.
Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо.
Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```.
## Шаблон за извикване:
```tool_call
{"name": <function-name>, "arguments": <args-json-object>}```
## Налични функции:
[your function definitions here]
## Потребителска заявка:
[your query in Bulgarian]<end_of_turn>
<start_of_turn>model
```
</details>
### Note 📝
**The model only generates the `tool_call` blocks with function names and parameters - it doesn't actually execute the functions.** Your client application must parse these generated calls, execute the actual functions (API calls, database queries, etc.), and provide the results back to the model in `tool_response` blocks for the conversation to continue the interperation of the results. A full demo is comming soon.
### Python example 🐍
<details>
<summary><strong>💻 Complete Working Example</strong></summary>
```python
import torch
import json
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
# Load model
model_name = "s-emanuilov/Tucan-2.6B-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
attn_implementation="eager" # Required for Gemma models
)
# Create prompt with system template
def create_prompt(functions, user_query):
system_prompt = """Ти си полезен AI асистент, който предоставя полезни и точни отговори.
Имаш достъп и можеш да извикаш една или повече функции, за да помогнеш с потребителското запитване. Използвай ги, само ако е необходимо и подходящо.
Когато използваш функция, форматирай извикването ѝ в блок ```tool_call``` на отделен ред, a след това ще получиш резултат от изпълнението в блок ```toll_response```.
## Шаблон за извикване:
```tool_call
{{"name": <function-name>, "arguments": <args-json-object>}}```
"""
functions_text = json.dumps(functions, ensure_ascii=False, indent=2)
full_prompt = f"{system_prompt}\n## Налични функции:\n{functions_text}\n\n## Потребителска заявка:\n{user_query}"
chat = [{"role": "user", "content": full_prompt}]
return tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# Example usage
functions = [{
"name": "create_calendar_event",
"description": "Creates a new event in Google Calendar.",
"parameters": {
"type": "object",
"properties": {
"title": {"type": "string"},
"date": {"type": "string"},
"start_time": {"type": "string"},
"end_time": {"type": "string"}
},
"required": ["title", "date", "start_time", "end_time"]
}
}]
query = "Създай събитие 'Годишен преглед' за 8-ми юни 2025 от 14:00 до 14:30."
# Generate response
prompt = create_prompt(functions, query)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=2048,
temperature=0.1,
top_k=25,
top_p=1.0,
repetition_penalty=1.1,
do_sample=True,
eos_token_id=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<end_of_turn>")],
pad_token_id=tokenizer.eos_token_id
)
result = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(result)
```
</details>
## Performance & Dataset 📊
> 📄 **Full methodology, dataset details, and comprehensive evaluation results coming in the upcoming paper**
**Dataset:** 10,000+ bilingual (Bulgarian/English) function-calling examples across 1,000+ topics, including tool calls with single/multiple arguments, optional parameters, follow-up queries, multi-tool selection, ambiguous queries requiring clarification, and conversational interactions without tool use. Data sourced from manual curation and synthetic generation (Gemini Pro 2.5/GPT-4.1/Sonnet 4).
**Results:** Significant improvements in tool-use capabilities over base BgGPT models: 34.1% for 2.6B, 10.6% for 9B, and 1.0% for 27B models in [internal benchmarks](https://github.com/s-emanuilov/tucan). Beyond raw function-calling scores, all Tucan models demonstrate more natural conversational flow while maintaining tool-use capabilities, retaining their base knowledge.
## Acknowledgments 🙏
Built on top of [BgGPT series](https://huggingface.co/collections/INSAIT-Institute/bggpt-gemma-2-673b972fe9902749ac90f6fe).
## Questions & Contact 💬
For questions, collaboration, or feedback: **[Connect on LinkedIn](https://www.linkedin.com/in/simeon-emanuilov/)**
|
viral-othoi-113-video-link/TRENDING.Top.Othoi.Official.Viral.Video | viral-othoi-113-video-link | 2025-06-16T05:35:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:35:22Z | 01 seconds ago
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|
Nirmit1/lora_bart_base_model | Nirmit1 | 2025-06-16T05:34:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T05:16:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.05_epoch1 | MinaMila | 2025-06-16T05:32:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T05:31:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.5_0.05_epoch2 | MinaMila | 2025-06-16T05:32:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T05:30:36Z | ---
library_name: transformers
tags: []
---
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
KaiChen1998/RACRO-7B-CRO | KaiChen1998 | 2025-06-16T05:28:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"multi-modal-reasoning",
"conversational",
"dataset:TIGER-Lab/ViRL39K",
"arxiv:2506.04559",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-15T13:22:47Z | ---
library_name: transformers
tags:
- multi-modal-reasoning
license: apache-2.0
datasets:
- TIGER-Lab/ViRL39K
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
new_version: KaiChen1998/RACRO-7B-CRO-GRPO
---
# RACRO-7B-CRO
<div align="center">
📄 [Paper](https://arxiv.org/abs/2506.04559) | 💻 [Github](https://github.com/gyhdog99/RACRO2/) | 🤗 [RACRO-Models](https://huggingface.co/collections/KaiChen1998/racro-6848ec8c65b3a0bf33d0fbdb) | 🤗 [RACRO-Demo](https://huggingface.co/spaces/Emova-ollm/RACRO-demo)
</div>
## Model Summary
**RACRO** (Reasoning-Aligned Perceptual Decoupling via Caption Reward Optimization) is a novel framework that enables scalable and modular multimodal reasoning by aligning visual perception with a powerful text-only reasoner. RACRO addresses the key challenge of generating image captions that are both faithful and sufficiently informative for downstream reasoning. It leverages a reasoning-guided reinforcement learning strategy to train the visual extractor, using reward signals derived from the performance of a fixed, high-capacity text-only LLM. This decoupled design avoids costly retraining of vision-language alignments and allows seamless plug-and-play upgrades to more advanced reasoners. Experiments on multimodal math and science benchmarks show that RACRO achieves **state-of-the-art** performance among open models.
<div align="center">
<img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/intro.png?raw=true" width=100%></img>
</div>
## Results
<div align="center">
<img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/results.png?raw=true" width=100%></img>
</div>
## Usage
```python
from transformers import AutoProcessor, AutoTokenizer
from vllm import LLM, SamplingParams
from qwen_vl_utils import process_vision_info
########################
# === Configuration ===
########################
IMAGE_PATH = "./assets/images/demo_example.jpg"
QUESTION = "When the canister is momentarily stopped by the spring, by what distance $d$ is the spring compressed?"
MLLM_MODEL_PATH = "KaiChen1998/RACRO-7B-CRO"
LLM_MODEL_PATH = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" # feel free to use more advanced reasoners!
########################
# === Prompts ===
########################
SYSTEM_PROMPT_CAP = "You are given an image and a relevant question. Based on the query, please describe the image in details. Do not try to answer the question."
SYSTEM_PROMPT_LLM = "You are a helpful assistant."
CAPTION_PROMPT = "Question: {}\nPlease describe the image. DO NOT try to answer the question!"
LLM_PROMPT = """In the following text, you will receive a detailed caption of an image and a relevant question. In addition, you will be provided with a tentative model response. You goal is to answer the question using these information.
### The detailed caption of the provided image: {}
### Note that the caption might contain incorrect solutions, do not be misguided by them.
### A problem to be solved: {}
### A tentative model response: {}
### Note that the above tentative response might be inaccurate (due to calculation errors, incorrect logic/reasoning and so on), under such a case, please ignore it and give your own solutions. However, if you do not have enough evidence to show it is wrong, please output the tentative response."""
########################
# === Initialize Models ===
########################
processor = AutoProcessor.from_pretrained(MLLM_MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_PATH)
mllm = LLM(model=MLLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8,
device='cuda:0', dtype="bfloat16", limit_mm_per_prompt={"image": 1})
llm = LLM(model=LLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8,
device='cuda:1', dtype="bfloat16")
mllm_sampling = SamplingParams(temperature=0, max_tokens=8192)
llm_sampling = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192)
########################
# === Build Prompts ===
########################
def build_messages(image_path, question):
cap_msgs = [
{"role": "system", "content": SYSTEM_PROMPT_CAP},
{"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": CAPTION_PROMPT.format(question)}]}
]
qa_msgs = [
{"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": question + " Please think step by step. The final answer MUST BE put in \\boxed{}."}]}
]
return cap_msgs, qa_msgs
# === Run Captioning and QA ===
def run_mllm(image_tensor, cap_prompt, qa_prompt):
cap_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": cap_prompt[0]}], sampling_params=mllm_sampling)
qa_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": qa_prompt[0]}], sampling_params=mllm_sampling)
return cap_output[0].outputs[0].text, qa_output[0].outputs[0].text
# === Final Reasoning Step ===
def run_llm_reasoning(caption, question, answer):
messages = [
{"role": "system", "content": SYSTEM_PROMPT_LLM},
{"role": "user", "content": LLM_PROMPT.format(caption, question, answer)}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output = llm.generate([{"prompt": prompt}], sampling_params=llm_sampling)
return output[0].outputs[0].text
########################
# === Pipeline ===
########################
cap_msgs, qa_msgs = build_messages(IMAGE_PATH, QUESTION)
cap_prompt = processor.apply_chat_template([cap_msgs], tokenize=False, add_generation_prompt=True)
qa_prompt = processor.apply_chat_template([qa_msgs], tokenize=False, add_generation_prompt=True)
image_tensor, _ = process_vision_info(cap_msgs)
caption_text, tentative_answer = run_mllm(image_tensor, cap_prompt, qa_prompt)
final_answer = run_llm_reasoning(caption_text, QUESTION, tentative_answer)
print("Final Answer:\n", final_answer)
```
## Citation
```bibtex
@article{gou2025perceptual,
author = {Gou, Yunhao and Chen, Kai and Liu, Zhili and Hong, Lanqing and Jin, Xin and Li, Zhenguo and Kwok, James T. and Zhang, Yu},
title = {Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning},
journal = {arXiv preprint arXiv:2506.04559},
year = {2025},
}
``` |
KaiChen1998/RACRO-7B-CRO-GRPO | KaiChen1998 | 2025-06-16T05:28:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"multi-modal-reasoning",
"conversational",
"dataset:TIGER-Lab/ViRL39K",
"arxiv:2506.04559",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-15T14:12:53Z | ---
library_name: transformers
tags:
- multi-modal-reasoning
license: apache-2.0
datasets:
- TIGER-Lab/ViRL39K
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
---
# RACRO-7B-CRO-GRPO
<div align="center">
📄 [Paper](https://arxiv.org/abs/2506.04559) | 💻 [Github](https://github.com/gyhdog99/RACRO2/) | 🤗 [RACRO-Models](https://huggingface.co/collections/KaiChen1998/racro-6848ec8c65b3a0bf33d0fbdb) | 🤗 [RACRO-Demo](https://huggingface.co/spaces/Emova-ollm/RACRO-demo)
</div>
## Model Summary
**RACRO** (Reasoning-Aligned Perceptual Decoupling via Caption Reward Optimization) is a novel framework that enables scalable and modular multimodal reasoning by aligning visual perception with a powerful text-only reasoner. RACRO addresses the key challenge of generating image captions that are both faithful and sufficiently informative for downstream reasoning. It leverages a reasoning-guided reinforcement learning strategy to train the visual extractor, using reward signals derived from the performance of a fixed, high-capacity text-only LLM. This decoupled design avoids costly retraining of vision-language alignments and allows seamless plug-and-play upgrades to more advanced reasoners. Experiments on multimodal math and science benchmarks show that RACRO achieves **state-of-the-art** performance among open models.
<div align="center">
<img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/intro.png?raw=true" width=100%></img>
</div>
## Results
<div align="center">
<img src="https://github.com/gyhdog99/RACRO2/blob/main/assets/images/results.png?raw=true" width=100%></img>
</div>
## Usage
```python
from transformers import AutoProcessor, AutoTokenizer
from vllm import LLM, SamplingParams
from qwen_vl_utils import process_vision_info
########################
# === Configuration ===
########################
IMAGE_PATH = "./assets/images/demo_example.jpg"
QUESTION = "When the canister is momentarily stopped by the spring, by what distance $d$ is the spring compressed?"
MLLM_MODEL_PATH = "KaiChen1998/RACRO-7B-CRO-GRPO"
LLM_MODEL_PATH = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B" # feel free to use more advanced reasoners!
########################
# === Prompts ===
########################
SYSTEM_PROMPT_CAP = "You are given an image and a relevant question. Based on the query, please describe the image in details. Do not try to answer the question."
SYSTEM_PROMPT_LLM = "You are a helpful assistant."
CAPTION_PROMPT = "Question: {}\nPlease describe the image. DO NOT try to answer the question!"
LLM_PROMPT = """In the following text, you will receive a detailed caption of an image and a relevant question. In addition, you will be provided with a tentative model response. You goal is to answer the question using these information.
### The detailed caption of the provided image: {}
### Note that the caption might contain incorrect solutions, do not be misguided by them.
### A problem to be solved: {}
### A tentative model response: {}
### Note that the above tentative response might be inaccurate (due to calculation errors, incorrect logic/reasoning and so on), under such a case, please ignore it and give your own solutions. However, if you do not have enough evidence to show it is wrong, please output the tentative response."""
########################
# === Initialize Models ===
########################
processor = AutoProcessor.from_pretrained(MLLM_MODEL_PATH)
tokenizer = AutoTokenizer.from_pretrained(LLM_MODEL_PATH)
mllm = LLM(model=MLLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8,
device='cuda:0', dtype="bfloat16", limit_mm_per_prompt={"image": 1})
llm = LLM(model=LLM_MODEL_PATH, tensor_parallel_size=1, gpu_memory_utilization=0.8,
device='cuda:1', dtype="bfloat16")
mllm_sampling = SamplingParams(temperature=0, max_tokens=8192)
llm_sampling = SamplingParams(temperature=0.6, top_p=0.95, max_tokens=8192)
########################
# === Build Prompts ===
########################
def build_messages(image_path, question):
cap_msgs = [
{"role": "system", "content": SYSTEM_PROMPT_CAP},
{"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": CAPTION_PROMPT.format(question)}]}
]
qa_msgs = [
{"role": "user", "content": [{"type": "image", "image": image_path}, {"type": "text", "text": question + " Please think step by step. The final answer MUST BE put in \\boxed{}."}]}
]
return cap_msgs, qa_msgs
# === Run Captioning and QA ===
def run_mllm(image_tensor, cap_prompt, qa_prompt):
cap_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": cap_prompt[0]}], sampling_params=mllm_sampling)
qa_output = mllm.generate([{"multi_modal_data": {"image": image_tensor}, "prompt": qa_prompt[0]}], sampling_params=mllm_sampling)
return cap_output[0].outputs[0].text, qa_output[0].outputs[0].text
# === Final Reasoning Step ===
def run_llm_reasoning(caption, question, answer):
messages = [
{"role": "system", "content": SYSTEM_PROMPT_LLM},
{"role": "user", "content": LLM_PROMPT.format(caption, question, answer)}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
output = llm.generate([{"prompt": prompt}], sampling_params=llm_sampling)
return output[0].outputs[0].text
########################
# === Pipeline ===
########################
cap_msgs, qa_msgs = build_messages(IMAGE_PATH, QUESTION)
cap_prompt = processor.apply_chat_template([cap_msgs], tokenize=False, add_generation_prompt=True)
qa_prompt = processor.apply_chat_template([qa_msgs], tokenize=False, add_generation_prompt=True)
image_tensor, _ = process_vision_info(cap_msgs)
caption_text, tentative_answer = run_mllm(image_tensor, cap_prompt, qa_prompt)
final_answer = run_llm_reasoning(caption_text, QUESTION, tentative_answer)
print("Final Answer:\n", final_answer)
```
## Citation
```bibtex
@article{gou2025perceptual,
author = {Gou, Yunhao and Chen, Kai and Liu, Zhili and Hong, Lanqing and Jin, Xin and Li, Zhenguo and Kwok, James T. and Zhang, Yu},
title = {Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning},
journal = {arXiv preprint arXiv:2506.04559},
year = {2025},
}
``` |
DhanasriArul/Model2vec | DhanasriArul | 2025-06-16T05:26:31Z | 0 | 0 | model2vec | [
"model2vec",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"license:mit",
"region:us"
] | null | 2025-06-16T05:15:41Z | ---
base_model: unknown
library_name: model2vec
license: mit
model_name: my_classifier_pipeline
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# my_classifier_pipeline Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a fine-tuned version of the [unknown](https://huggingface.co/unknown) Model2Vec model. It also includes a classifier head on top.
## Installation
Install model2vec using pip:
```
pip install model2vec[inference]
```
## Usage
Load this model using the `from_pretrained` method:
```python
from model2vec.inference import StaticModelPipeline
# Load a pretrained Model2Vec model
model = StaticModelPipeline.from_pretrained("my_classifier_pipeline")
# Predict labels
predicted = model.predict(["Example sentence"])
```
## Additional Resources
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
- [Website](https://minishlab.github.io/)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
``` |
tv-nulook-india-18k/Original.Full.Clip.nulook.india.Viral.Videos.Leaks.Official | tv-nulook-india-18k | 2025-06-16T05:25:09Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:24:03Z | Nulookindia Video New collections of Nulookindia Video now being a creator on Fanfix uploading adult contents
<a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
|
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.5_0.05_epoch1 | MinaMila | 2025-06-16T05:24:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T05:22:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[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]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
VIDEO-Nirma-Meena-Viral-Video-tv/Full.VIDEO.Nirma.Meena.Viral.Video.Tutorial | VIDEO-Nirma-Meena-Viral-Video-tv | 2025-06-16T05:22:07Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:21:55Z | 01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://sahabagi-mgi.blogspot.com/p/heres-now.html)
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<a href="https://sahabagi-mgi.blogspot.com/p/heres-now.html" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.15_epoch1 | MinaMila | 2025-06-16T05:19:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T05:17:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- 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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## 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] |
TV-nulook-india-viral-video/nulook.india.viral.video.original.nulook.india.nulookindia.video.mms | TV-nulook-india-viral-video | 2025-06-16T05:18:06Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:17:05Z | "03 Second — Nulookindia Video New collections of Nulookindia Video now being a creator on Fanfix uploading adult contents
<a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
|
nulook-india-viral-video/nulook.india.viral.video.original.nulook.india.nulookindia.video.mms | nulook-india-viral-video | 2025-06-16T05:18:01Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:16:56Z | "03 Second — Nulookindia Video New collections of Nulookindia Video now being a creator on Fanfix uploading adult contents
<a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
|
Ashwani-0101/YOLOV11M-Construction | Ashwani-0101 | 2025-06-16T05:17:42Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:39:25Z | # YOLO 11M - Confusion Matrix and Other Result
Normalized Confusion Matrix (Closer to 1, better the performance for the specific class it has):
Compiled results:
|
Ashwani-0101/Yolo11L-construction | Ashwani-0101 | 2025-06-16T05:16:51Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:32:53Z | # YOLO 11L - Confusion Matrix and Other Result
Normalized Confusion Matrix (Closer to 1, better the performance for the specific class it has):
Other Results Compiled:
|
rafamartins/rafa.martins.e.cadeirante.twitter.rafa.martins.e.cadeirante.twitter | rafamartins | 2025-06-16T05:16:37Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:15:29Z | Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/scsscsc"> 🌐 Click Here To link (Watch-Video-18rafa.martins.e.cadeirante.twitter)
🔴 ➤►DOWNLOAD👉👉🟢 ➤Watch 🟢 ➤ ➤ ➤ <a href="https://viraltrendzzz.com/scsscsc"> 🌐 Watch-Video-18rafa.martins.e.cadeirante.twitter |
Pakistani-Viral-Video/VIDEO.Pakistani.Viral.Video.Tutorial.Official | Pakistani-Viral-Video | 2025-06-16T05:13:08Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:10:21Z | <a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
Bearrr310/train_grpo_7B_unsloth_0616_100step | Bearrr310 | 2025-06-16T05:12:41Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"grpo",
"dataset:unsloth-7B-reward-0616-100step",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T05:12:02Z | ---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
datasets: unsloth-7B-reward-0616-100step
library_name: transformers
model_name: train_grpo_7B_unsloth_0616_100step
tags:
- generated_from_trainer
- unsloth
- trl
- grpo
licence: license
---
# Model Card for train_grpo_7B_unsloth_0616_100step
This model is a fine-tuned version of [unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit) on the [unsloth-7B-reward-0616-100step](https://huggingface.co/datasets/unsloth-7B-reward-0616-100step) 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="Bearrr310/train_grpo_7B_unsloth_0616_100step", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
wATCH-Meezo-Fun-Video/Video.meezo.fun.video.meezo.fun.mezo.fun.meezo.fun.Original | wATCH-Meezo-Fun-Video | 2025-06-16T05:12:05Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:11:52Z | [🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 Video](https://tinyurl.com/modasnap?fkisreal)
[🔴 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🌐==►► 𝖣𝗈𝗐𝗇𝗅𝗈𝖺𝖽 𝖭𝗈𝗐 Video](https://tinyurl.com/modasnap?fkisreal)
<a href="https://tinyurl.com/modasnap?fkisreal" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
hitty28/branch-switch-classifier | hitty28 | 2025-06-16T05:11:00Z | 0 | 0 | null | [
"safetensors",
"distilbert",
"text-classification",
"branch-switching",
"hospital-chatbot",
"en",
"dataset:branch_switch_classification",
"license:apache-2.0",
"region:us"
] | text-classification | 2025-06-16T05:10:40Z |
---
language: en
license: apache-2.0
tags:
- text-classification
- branch-switching
- hospital-chatbot
- distilbert
datasets:
- branch_switch_classification
widget:
- text: "I want to switch to Mumbai branch"
- text: "What are your hospital timings?"
- text: "Can I change to the branch near my home?"
---
# Branch Switch Classification Model
This model classifies whether a user wants to switch hospital branches or is asking for general information.
## Model Description
- **Model**: DistilBERT for Sequence Classification
- **Task**: Binary Classification
- **Domain**: Hospital/Healthcare Chatbot
- **Classes**:
- `True`: User wants to switch branches
- `False`: General query/information seeking
## Usage
```python
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch
# Load model and tokenizer
tokenizer = DistilBertTokenizer.from_pretrained("hitty28/branch-switch-classifier")
model = DistilBertForSequenceClassification.from_pretrained("hitty28/branch-switch-classifier")
# Predict
def predict(text):
inputs = tokenizer(text, truncation=True, padding='max_length', max_length=128, return_tensors='pt')
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
predicted_class = torch.argmax(predictions, dim=-1).item()
return bool(predicted_class)
# Example
result = predict("I want to switch to Delhi branch")
print(result) # True
```
## Training Data
The model was trained on a comprehensive dataset including:
- Direct branch switch requests
- Location-specific switches
- Facility-based switches
- Information queries about branches
- Medical service inquiries
- Edge cases and ambiguous statements
## Performance
The model achieves high accuracy in distinguishing between branch switching intents and general information queries.
|
AhmadAli223/llama3.2_Final_FYP | AhmadAli223 | 2025-06-16T05:09:39Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-16T05:09:39Z | ---
license: apache-2.0
---
|
Sawu-Low3/t5-base-lora-stage3 | Sawu-Low3 | 2025-06-16T05:08:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T05:08:40Z | ---
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]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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## 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]
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[More Information Needed]
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[More Information Needed]
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Sawu-Low3/t5-base-lora-stage2 | Sawu-Low3 | 2025-06-16T05:08:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T05:08:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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**BibTeX:**
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Bu-Guru-Salsa-viral-18k/Bu.Guru.Salsa.Jember.Video.Viral | Bu-Guru-Salsa-viral-18k | 2025-06-16T05:07:34Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-15T11:11:45Z | "03 Second — Bu Guru Salsa Jember Video New collections of Bu Guru Salsa Jember Video now being a creator on Fanfix uploading adult contents.
<a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a>
|
Videos-Hawk-Tuah-Girl-Original-Video/Original.Full.Clip.Hawk.Tuah.Viral.Video.Leaks.Official | Videos-Hawk-Tuah-Girl-Original-Video | 2025-06-16T05:07:23Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T05:07:03Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
stablediffusionapi/ht_photorealism | stablediffusionapi | 2025-06-16T05:06:34Z | 3 | 0 | diffusers | [
"diffusers",
"safetensors",
"stablediffusionapi.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-12-08T14:54:55Z | ---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# ht_photorealism API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "ht_photorealism"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Try model for free: [Generate Images](https://stablediffusionapi.com/models/ht_photorealism)
Model link: [View model](https://stablediffusionapi.com/models/ht_photorealism)
Credits: [View credits](https://civitai.com/?query=ht_photorealism)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v4/dreambooth"
payload = json.dumps({
"key": "your_api_key",
"model_id": "ht_photorealism",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.25_epoch1 | MinaMila | 2025-06-16T05:05:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T05:04:07Z | ---
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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
japat123/gemma_jun16_2 | japat123 | 2025-06-16T05:03:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-7b-bnb-4bit",
"base_model:quantized:unsloth/gemma-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-16T05:02:22Z | ---
base_model: unsloth/gemma-7b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** japat123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-bnb-4bit
This gemma 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)
|
2-Wolf-1-Girl-viral-video-original-Link/FULL.VIDEO.two.wolf.one.girl.Viral.Video.Tutorial.Official | 2-Wolf-1-Girl-viral-video-original-Link | 2025-06-16T05:01:28Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:58:48Z | <animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
rmdhirr/suja-lorab-ep5-suja-6000 | rmdhirr | 2025-06-16T05:00:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:rmdhirr/merged-suja-latest",
"base_model:adapter:rmdhirr/merged-suja-latest",
"region:us"
] | null | 2025-06-16T04:59:37Z | ---
base_model: rmdhirr/merged-suja-latest
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### 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]
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<!-- 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 |
MinaMila/gemma_2b_unlearned_2nd_1e-5_1.0_0.5_0.5_0.25_epoch2 | MinaMila | 2025-06-16T04:59:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:58:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_045357 | himedia | 2025-06-16T04:59:05Z | 0 | 0 | null | [
"safetensors",
"financial",
"credit-rating",
"korean",
"gemma",
"unsloth",
"fine-tuned",
"text-generation",
"conversational",
"ko",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-16T04:58:42Z | ---
language: ko
license: apache-2.0
base_model: unsloth/gemma-3-4b-it
tags:
- financial
- credit-rating
- korean
- gemma
- unsloth
- fine-tuned
model_name: FinCreditGemma-3-4B
pipeline_tag: text-generation
---
# FinCreditGemma-3-4B
## 모델 개요
FinCreditGemma-3-4B는 금융 신용 평가를 위해 특별히 설계된 한국어 언어 모델입니다.
**베이스 모델**: unsloth/gemma-3-4b-it
**데이터셋**: himedia/financial_dummy_data_v2
**학습 방법**: LoRA (Low-Rank Adaptation)
**학습 일시**: 20250616_045357
## 하이퍼파라미터
- **Learning Rate**: 5e-05
- **Max Steps**: 10
- **Batch Size**: 2
- **Gradient Accumulation**: 4
- **LoRA r**: 16
- **LoRA alpha**: 16
- **Max Sequence Length**: 2048
- **Warmup Steps**: 5
## 사용 방법
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# 모델과 토크나이저 로드
tokenizer = AutoTokenizer.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_045357")
model = AutoModelForCausalLM.from_pretrained("himedia/fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_045357")
# 간단한 추론 예제
prompt = "고객의 신용등급을 평가해주세요:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 레포지토리명 구성
```
fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_045357 = fincredit-gemma3-4b-lr5e05-bs2-r16-steps10-20250616_045357
```
- `fincredit-gemma3-4b`: 모델 기본명
- `lr5e05`: Learning Rate
- `bs2`: Batch Size
- `r16`: LoRA rank
- `steps10`: 학습 스텝
- `20250616_045357`: 학습 시각
## 성능
이 모델은 한국어 금융 텍스트에 대해 파인튜닝되어 신용 평가 관련 질의응답에 특화되어 있습니다.
## 라이선스
Apache 2.0
|
Khushi-Rao-Official-Viral-Video/Full.VIDEO.khushi.rao.Viral.Video.Tutorial.Official | Khushi-Rao-Official-Viral-Video | 2025-06-16T04:58:39Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:58:22Z | Khushi Rao Viral video took the internet viewers on various Leaked social media platforms. Khushi Rao Video, a young and talented digital creator, recently became famous thanks to this interesting video.
<a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
Montecarlo2024/Qwen3-4B-Python-Combined-v2_GGUF | Montecarlo2024 | 2025-06-16T04:56:40Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"dataset:Montecarlo2024/Python-Combined-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T22:29:09Z | ---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
datasets:
- Montecarlo2024/Python-Combined-v2
---
# Uploaded model
- **Developed by:** Montecarlo2024
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
-
-
-
-
System Prompt: You should include the following in your system prompt for this model
-
-
SYSTEM """You are a helpful assistant. Respond directly to user questions without repeating the question or generating additional user prompts."""
_
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
FLOPS-Squared/KeystoneFuse-FW-G-16-Flax | FLOPS-Squared | 2025-06-16T04:55:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:53:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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shiny-Dixit-hot-sex-scene-in-web-series/Full.Video.shiny.Dixit.hot.sex.scene.in.web.series.Video | shiny-Dixit-hot-sex-scene-in-web-series | 2025-06-16T04:52:59Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:52:41Z | 01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://infobal.com.ar/watch-full-video/?Bados.all)
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://infobal.com.ar/watch-full-video/?Bados.all)
<a href="https://infobal.com.ar/watch-full-video/?Bados.all" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.5_epoch1 | MinaMila | 2025-06-16T04:51:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:49:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Summary
## Model Examination [optional]
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nieo23/emotion-analysis-with-distilbert | nieo23 | 2025-06-16T04:50:40Z | 3 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-13T09:01:07Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: nieo23/emotion-analysis-with-distilbert
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nieo23/emotion-analysis-with-distilbert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1386
- Validation Loss: 0.1496
- Train Accuracy: 0.9275
- Epoch: 1
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': np.float32(5e-05), 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3844 | 0.1858 | 0.9235 | 0 |
| 0.1386 | 0.1496 | 0.9275 | 1 |
### Framework versions
- Transformers 4.52.4
- TensorFlow 2.18.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
UNIVA-Bllossom/DeepSeek-llama3.3-Bllossom-70B | UNIVA-Bllossom | 2025-06-16T04:48:05Z | 146 | 59 | transformers | [
"transformers",
"safetensors",
"text-generation",
"ko",
"en",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-12T11:27:31Z | ---
license: mit
language:
- ko
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Llama-70B
library_name: transformers
pipeline_tag: text-generation
---
# DeepSeek-llama3.3-Bllossom
DeepSeek-Bllossom Series는 기존 DeepSeek-R1-Distill Series 모델의 language mixing, 다국어 성능 저하 문제를 해결하기 위해 추가로 학습된 모델입니다.
DeepSeek-llama3.3-Bllossom-70B는 DeepSeek-R1-distill-Llama-70B 모델을 베이스로 구축된 모델로, 한국어 환경에서의 추론 성능 향상을 목표로 개발되었습니다.
본 모델은 UNIVA와 Bllossom팀이 합작으로 제작한 첫 번째 모델입니다.
<div align="center">
| **Model** | **Base Model** | **Download** |
| :------------: | :------------: | :------------: |
| DeepSeek-qwen-Bllossom-1.5B | [DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) | 공개예정 |
| DeepSeek-qwen-Bllossom-7B | [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) | 공개예정 |
| DeepSeek-llama3.1-Bllossom-8B | [DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) | [🤗 HuggingFace](https://huggingface.co/UNIVA-Bllossom/DeepSeek-llama3.1-Bllossom-8B) |
| DeepSeek-qwen-Bllossom-14B | [DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) | 공개예정 |
| DeepSeek-qwen-Bllossom-32B | [DeepSeek-R1-Distill-Qwen-32B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B) | [🤗 HuggingFace](https://huggingface.co/UNIVA-Bllossom/DeepSeek-qwen-Bllossom-32B) |
| DeepSeek-llama3.3-Bllossom-70B | [DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) | [🤗 HuggingFace](https://huggingface.co/UNIVA-Bllossom/DeepSeek-llama3.3-Bllossom-70B ) |
</div>
## 1. Introduction
DeepSeek-llama3.3-Bllossom-70B는 DeepSeek-R1-distill-Llama-70B 모델을 베이스로 구축된 모델로, 기존 베이스 모델이 영어와 중국어 위주의 데이터로 학습된 한계를 극복하고자 개발되었습니다. 특히, 기존 DeepSeek-R1-distill-Llama-70B의 경우 한국어로 추론 시 모델 성능이 크게 하락하는 문제가 있었는데, DeepSeek-Bllossom은 이 문제를 해결하기 위해 내부 사고 과정은 영어로 수행하고 최종 사용자에게 제공되는 응답은 입력 언어에 따라 출력되도록 추가로 학습되었습니다. 이를 통해 한국어 환경에서의 추론 성능이 크게 개선되었습니다.
학습에는 한국어, 영어 reasoning 데이터를 사용하였으며, 기존 DeepSeek-R1 모델 학습에 주로 사용된 STEM 분야 데이터 외에도 다양한 분야의 데이터가 포함되었습니다. 데이터셋 설계와 모델 학습 과정에서 DeepSeek-llama3.3-Bllossom은 한국어 사용 환경에서 더 정확하고 신뢰할 수 있는 추론 결과를 제공하는 것을 주된 목표로 개발되었습니다.
DeepSeek-Bllossom Series의 조금 더 작은 8B모델은 이곳에서 찾을 수 있습니다. [DeepSeek-R1-distill-Llama-Bllossom-8B](https://huggingface.co/UNIVA-Bllossom/DeepSeek-llama3.1-Bllossom-8B)
---
## 2. Post-training
DeepSeek-llama3.3-Bllossom은 자체적으로 제작한 다양한 reasoning 데이터를 활용하여 post-training 과정을 진행하였습니다. 이 과정에서는 대규모 모델이 보유한 우수한 reasoning 능력과 한국어 처리 능력을 DeepSeek-R1-distill-Llama-70B 모델에 효과적으로 distillation하는 방법을 적용하였습니다. 이를 통해 기존 모델의 성능을 보완하고, 복합적인 추론 문제에 대해 더 정확하며 신뢰할 수 있는 응답을 생성할 수 있도록 최적화하였습니다.
---
## 3. inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"UNIVA-Bllossom/DeepSeek-llama3.3-Bllossom-70B",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("UNIVA-Bllossom/DeepSeek-llama3.3-Bllossom-70B")
system='''
You are a highly capable assistant. For every user question, follow these instructions exactly:
1. First, think through the problem step-by-step in English. Enclose all of your internal reasoning between <think> and </think> tags. This chain-of-thought should detail your reasoning process.
2. After the closing </think> tag, provide your final answer.
3. Do not include any additional text or commentary outside of this format.
4. Your output should strictly follow this structure:
<think>
[Your detailed step-by-step reasoning in English]
</think>
[Your final answer]
'''
text="철수, 영희, 민수가 3회의 게임에서 점수를 받았습니다. 영희의 점수는 민수의 점수의 두 배이며, 민수의 점수는 철수의 4배입니다. 철수가 10점을 받았다면 이 3명의 평균 점수를 계산하세요."
chat = [
{"role": "system", "content": system},
{"role": "user", "content": text}
]
prompt=tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer(
prompt,
return_tensors="pt",
add_special_tokens=True
)
if "token_type_ids" in model_inputs:
del model_inputs["token_type_ids"]
model_inputs = {k: v.to(model.device) for k, v in model_inputs.items()}
generated_ids = model.generate(
**model_inputs,
max_new_tokens=8192,
)
```
## 4. 모델 응답 비교
| 입력 (Prompt) | DeepSeek-R1-distill-Llama-70B | DeepSeek-llama3.3-Bllossom-70B |
| ------------- | ----------------------------- | -------------------------- |
| **Alice, Bob, Charlie가 3회의 게임에서 점수를 받았습니다.<br>Alice의 점수는 Bob의 점수의 두 배이며, Bob의 점수는 Charlie의 4배입니다.<br>Charlie가 10점을 받았다면 이 3명의 평균 점수를 계산하세요.** |<br>Charlie의 점수는 10점입니다.<br>Bob의 점수는 Charlie의 4배이므로, Bob의 점수는 <br>$$<br>4 \\times 10 = 40<br>$$<br>점입니다.<br>Alice의 점수는 Bob의 점수의 두 배이므로, Alice의 점수는 <br>$$<br>2 \\times 40 = 80<br>$$<br>점입니다.<br>세人的 총 점수는 <br>$$<br>10 + 40 + 80 = 130<br>$$<br>점입니다.<br>평균 점수는 <br>$$<br>\\frac{130}{3} \\approx 43.33$점입니다.<br>$$<br>\\boxed{43.33}<br>$$<br>Charlie의 점수는 10점입니다. | <br>Charlie의 점수는 10점으로 주어졌습니다. Bob의 점수는 Charlie의 점수의 4배이며, 이는 다음과 같이 계산됩니다:<br>$$<br>4 \times 10 = 40<br>$$<br>Alice의 점수는 Bob의 점수의 두 배이며, 이는 다음과 같습니다:<br>$$<br>2 \times 40 = 80<br>$$<br>세 사람의 총 점수는 다음과 같습니다:<br>$$<br>10 + 40 + 80 = 130<br>$$<br>이 세 사람의 평균 점수는 총 점수를 사람 수로 나누어 계산됩니다:<br>$$<br>\frac{130}{3}<br>$$<br>따라서, 이 세 사람의 평균 점수는 <br>$$<br>\boxed{\dfrac{130}{3}}<br>$$<br>입니다. |
| **소수가 무한하다는 사실을 3가지 방법으로 증명하시오.** | <br>以下是用三种方法证明素数无限的详细步骤：<br>### 方法一：欧几里得的反证法<br>1. **假设**：假设素数是有限的，且所有素数为 $ p_1, p_2, p_3, \\ldots, p_n $。<br>2. **构造数**：构造一个新数 $ N = (p_1 \\times p_2 \\times p_3 \\times \\ldots \\times p_n) + 1 $。<br>3. **分析N的性质**：<br> - 如果N是素数，则N不在原假设的素数列表中，与假设矛盾。<br> - 如果N是合数，则存在某个素数 $ p $ 整除N。但由于N除以任何 $ p_i $ 都余1，故 $ p $ 不在原列表中，同样矛盾。<br>4. **结论**：因此，素数必须是无限的。<br>### 方法二：费马数的性质<br>1. **定义**：费马数形如 $ F_k = 2^{2^k} + 1 $，其中 $ k $ 为非负整数。<br>2. **假设**：假设素数是有限的，且所有素数为 $ p_1, p_2, \\ldots, p_n $。<br>3. **构造费马数**：对于每个 $ k $，构造 $ F_k $。如果 $ F_k $ 是素数，则不在原列表中；如果是合数，其素因数不在原列表中。<br>4. **结论**：因此，素数必须是无限的。<br>### 方法三：欧拉函数与素数无限性<br>1. **欧拉函数**：欧拉函数 $ \\phi(n) $ 表示小于 $ n $ 且与 $ n $ 互质的数的个数。<br>2. **假设**：假设素数有限，记为 $ p_1, p_2, \\ldots, p_n $。<br>3. **构造数**：考虑形如 $ N = p_1^{k_1} p_2^{k_2} \\ldots p_n^{k_n} $ 的数，其欧拉函数为 $ \\phi(N) = N \\prod_{i=1}^{n} \\left(1 - \\frac{1}{p_i}\\right) $。<br>4. **分析**：如果素数有限，$ \\prod_{p \\text{素数}} \\left(1 - \\frac{1}{p}\\right) $ 收敛，但实际上该积发散，导致矛盾。<br>5. **结论**：因此，素数必须是无限的。<br>这三种方法各自从不同的角度证明了素数的无限性，展示了数论中素数无限性的深刻性。 | <br>소수가 무한하다는 것을 3가지 방법으로 증명하기 위해, 우리는 다음과 같은 접근 방식을 사용할 수 있습니다:<br>1. **유클리드의 모순 증명**:<br> - **가정**: 소수는 유한한 개수 $ p_1, p_2, \\dots, p_n $이라고 가정합니다.<br> - **구성**: $ P = p_1 \\times p_2 \\times \\dots \\times p_n + 1 $으로 정의합니다.<br> - **결론**: $ P $가 소수이면, 그것은 목록에 없는 새로운 소수입니다. 만약 $ P $가 합성수이면, 그것은 목록에 없는 소수를 포함해야 합니다. 두 경우 모두 가정에 모순됩니다. 따라서, 소수는 무한합니다.<br>2. **팩토리얼 접근법**:<br> - **가정**: 소수는 유한한 개수 $ p_1, p_2, \\dots, p_n $이라고 가정합니다.<br> - **구성**: $ n! + 1 $을 고려하십시오, 여기서 $ n $은 가장 큰 소수입니다.<br> - **결론**: $ n! $은 모든 $ p_i $로 나누어 떨어지므로, $ n! + 1 $은 $ p_i $ 중 어느 것도 포함하지 않습니다. 만약 $ n! + 1 $이 소수이면, 그것은 $ n $보다 큰 새로운 소수입니다. 만약 $ n! + 1 $이 합성수이면, 그것은 $ n $보다 큰 소수를 포함해야 합니다. 두 경우 모두 가정에 모순됩니다. 따라서, 소수는 무한합니다.<br>3. **오일러의 제타 함수 증명**:<br> - **개념**: 오일러는 제타 함수 $ \\zeta(s) = \\sum_{k=1}^\\infty \\frac{1}{k^s} $가 소수에 대한 곱으로 표현될 수 있음을 보였습니다: $ \\zeta(s) = \\prod_{p \\text{ 소수}} \\frac{1}{1 - \\frac{1}{p^s}} $.<br> - **결론**: 만약 소수가 유한하면, 곱은 유한해야 할 것입니다. 그러나 $ s $가 1보다 큰 값을 향해 접근할 때, $ \\zeta(s) $는 무한대로 발산합니다. 이것은 곱이 발산해야 함을 요구하며, 따라서 소수는 무한해야 합니다.<br>각 방법은 소수가 무한하다는 것을 확증하며, 서로 다른 수학적 원칙에 기반을 둡니다. 유클리드의 증명은 모순을 사용합니다, 팩토리얼 접근법은 간단한 수론을 사용하며, 오일러의 증명은 해석적 수론의 도구를 사용합니다. 세 방법 모두 소수 무한성에 대한 이해를 강화합니다. |
## 5. Benchmark
- dtype을 float16으로 추론을 진행하였습니다.
- max_tokens: 32786
- temperature: 0.7
- 평가 방식: 각 벤치마크를 3회 반복 실행한 후 평균 점수를 산출하였습니다.
- _en 벤치마크: 원본 벤치마크 질문을 그대로 사용하였습니다.
- _ko 벤치마크: 원본 벤치마크 질문을 한국어로 고품질 번역하여 사용하였습니다.
| Model | AIME24_ko | AIME24_en | MATH500_ko | MATH500_en |
|---------------------------------------|-----------|-----------|------------|------------|
| DeepSeek-R1-Distill-Llama-8B | 25.56 | 46.67 | 63.40 | 88.87 |
| DeepSeek-llama3.1-Bllossom-8B | 36.67 | 40.00 | 78.07 | 87.80 |
| DeepSeek-R1-Distill-Llama-70B | 58.89 | 70.00 | 88.53 | 93.73 |
| DeepSeek-llama3.3-Bllossom-70B | 62.22 | 65.56 | 88.40 | 93.33 |
## 6. License
This code repository and the model weights are licensed under the MIT License.
DeepSeek-Bllossom series support commercial use, allow for any modifications and derivative works, including, but not limited to, distillation for training other LLMs. Please note that:
- **DeepSeek-R1-Distill-Llama-70B** is derived from Llama3.3-70B-Instruct and is originally licensed under llama3.3 license.
- **DeepSeek-llama3.3-Bllossom-70B** is derived from DeepSeek-R1-Distill-Llama-70B and is originally licensed under llama3.3 license.
## 7. Contributor
- **UNIVA AI Team** ([UNIVA](https://univa.co.kr), Main contributor)
- 최창수 (서울과학기술대학교, [MLP연구실](https://sites.google.com/view/aailab) 석사과정)
- **임경태** (KAIST, [MLP연구실](https://sites.google.com/view/aailab) 교수)
## 8. Contact
If you have any questions, please raise an issue or contact us at [[email protected]]([email protected]). |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.25_0.75_epoch2 | MinaMila | 2025-06-16T04:44:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:42:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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## Training Details
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### Results
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#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_500_no_KL | KevinG | 2025-06-16T04:43:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:09:22Z | ---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
model_name: Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_500_no_KL
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_500_no_KL
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-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="KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_500_no_KL", 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/sleeepeer-penn-state/huggingface/runs/oushi1xm)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Z841973620/Qwen3-30B-A3B-IQ2_XS-GGUF | Z841973620 | 2025-06-16T04:38:45Z | 0 | 0 | null | [
"gguf",
"base_model:huihui-ai/Qwen3-30B-A3B-abliterated",
"base_model:quantized:huihui-ai/Qwen3-30B-A3B-abliterated",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-16T04:02:03Z | ---
base_model:
- huihui-ai/Qwen3-30B-A3B-abliterated
--- |
proyrb/ppo-LunarLander-v2 | proyrb | 2025-06-16T04:35:43Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-16T03:02:53Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -56.14 +/- 76.83
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
## Evaluation Results
- Mean Reward: -56.14 ± 76.83
- Number of Evaluation Episodes: 10
## Hyperparameters
```python
{'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.0003
'num_envs': 8
'num_steps': 2048
'update_epochs': 10
'num_minibatches': 32
'clip_coef': 0.5
'seed': 136
'repo_id': 'proyrb/ppo-LunarLander-v2'
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'norm_adv': True
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'batch_size': 16384
'minibatch_size': 512}
|
FormlessAI/db2f65e3-3813-44fb-8af6-258eb0046876 | FormlessAI | 2025-06-16T04:33:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"base_model:finetune:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"endpoints_compatible",
"region:us"
] | null | 2025-06-15T21:42:54Z | ---
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
library_name: transformers
model_name: db2f65e3-3813-44fb-8af6-258eb0046876
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for db2f65e3-3813-44fb-8af6-258eb0046876
This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO).
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="FormlessAI/db2f65e3-3813-44fb-8af6-258eb0046876", 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/phoenix-formless/Gradients/runs/nibb0cx5)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
gradientrouting-spar/standard_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_seed_25_20250616_042127 | gradientrouting-spar | 2025-06-16T04:31:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:31:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Henil1/vit-axavision-2-ChestX-BioGPT-v8 | Henil1 | 2025-06-16T04:31:02Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:31:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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Xem-Phim-Gai-Xinh-Hang-Mup-Nhanh-Nhat/Xem.Phim.Sex.Gai.Xinh.Hang.Mup.Nhanh.Nhat | Xem-Phim-Gai-Xinh-Hang-Mup-Nhanh-Nhat | 2025-06-16T04:29:33Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:29:23Z | 01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://infobal.com.ar/watch-full-video/?Bados.all)
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<a href="https://infobal.com.ar/watch-full-video/?Bados.all" rel="nofollow" data-target="animated-image.originalLink"><img src="https://media.post.rvohealth.io/wp-content/uploads/2024/02/video-overlay-bed-sex-couple-original.jpg" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_100_no_KL | KevinG | 2025-06-16T04:29:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:08:53Z | ---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
model_name: Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_100_no_KL
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_100_no_KL
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-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="KevinG/Meta-Llama-3-8B-Instruct-GRPO-alpaca_naive_100_no_KL", 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/sleeepeer-penn-state/huggingface/runs/09d6hqlt)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
abdullahalatabee/geometric-shapes-generator | abdullahalatabee | 2025-06-16T04:28:52Z | 4 | 1 | peft | [
"peft",
"diffusers",
"safetensors",
"arxiv:1910.09700",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"region:us"
] | null | 2025-06-09T23:02:28Z | ---
base_model: runwayml/stable-diffusion-v1-5
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]
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<!-- Provide the basic links for the model. -->
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<!-- 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]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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[More Information Needed]
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.14.0 |
sicak-sex-turkiye/sicak.sex.turkiye.porno.videolari.xnxx | sicak-sex-turkiye | 2025-06-16T04:28:48Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:28:38Z | [](https://t.co/P8Ex9FtH0g) |
louzongzhi/CosmoGPT | louzongzhi | 2025-06-16T04:28:36Z | 0 | 0 | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2025-06-16T04:28:36Z | ---
license: cc-by-nc-nd-4.0
---
|
mrk91s/qbox-coder-output | mrk91s | 2025-06-16T04:28:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:deepseek-ai/deepseek-coder-1.3b-instruct",
"base_model:finetune:deepseek-ai/deepseek-coder-1.3b-instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:28:06Z | ---
base_model: deepseek-ai/deepseek-coder-1.3b-instruct
library_name: transformers
model_name: qbox-coder-output
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qbox-coder-output
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-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="mrk91s/qbox-coder-output", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mezzo-fun-18-video/FULL.VIDEOs.Mezzo.Fun.Viral.Video.Tutorial.Official | mezzo-fun-18-video | 2025-06-16T04:26:05Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:25:45Z | <a href="https://tinyurl.com/Sapna-News?fkisreal" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
Edcastro/tinyllama-edcastr_JQuery-v1 | Edcastro | 2025-06-16T04:25:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:24:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### 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).
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Henil1/vit-axavision-2-ChestX-v2 | Henil1 | 2025-06-16T04:24:48Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:24:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.05_epoch1 | MinaMila | 2025-06-16T04:24:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:22:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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VIDEOS-two-wolf-one-girl-Viral-Video/CLIP.VIDEO.two.wolf.one.girl.Video.Tutorial.Official | VIDEOS-two-wolf-one-girl-Viral-Video | 2025-06-16T04:24:16Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:20:01Z | <animated-image data-catalyst=""><a href="https://sexleakedviral.com/new-leaked-video/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
Henil1/vit-axavision-2-ChestX-BioGPT | Henil1 | 2025-06-16T04:24:09Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:24:07Z | ---
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]
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## How to Get Started with the Model
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Henil1/vit-axavision-2-ChestX | Henil1 | 2025-06-16T04:24:06Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"image-captioning",
"vision-language",
"vit-gpt2",
"chest-xray",
"healthcare",
"axamine",
"finetuned",
"nlpconnect/vit-gpt2-image-captioning",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-14T18:50:44Z | ---
library_name: transformers
tags:
- image-captioning
- vision-language
- vit-gpt2
- chest-xray
- healthcare
- axamine
- finetuned
- nlpconnect/vit-gpt2-image-captioning
---
# Vit-Axavision-2-ChestX 🩺
This model is a fine-tuned version of [`nlpconnect/vit-gpt2-image-captioning`](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on a chest X-ray dataset. It is developed as part of the Axamine AI research efforts to explore medical vision-language applications. The model takes chest X-ray images as input and generates descriptive captions that may help in automated reporting, healthcare research, or AI-assisted diagnostics.
---
## Model Details
- **Base model:** nlpconnect/vit-gpt2-image-captioning
- **Architecture:** VisionEncoderDecoderModel (ViT encoder + GPT2 decoder)
- **Fine-tuned on dataset:** [Shrey-1329/cxiu_hf_dataset](https://huggingface.co/datasets/Shrey-1329/cxiu_hf_dataset)
- **Model size:** ~250M parameters
- **Developed by:** Henilsinh Raj (Axamine AI)
---
## Use Cases
### Intended Use
- Chest X-ray image captioning
- Healthcare research
- Medical AI experiments
- Educational purposes
### Limitations
- This model does **not** provide medical diagnosis.
- Captions are purely descriptive and may not fully reflect clinical accuracy.
---
## Usage
Here’s how you can use the model for inference:
```python
from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
from PIL import Image
import torch
import requests
# Load model
model_id = "Henil1/vit-axavision-2-ChestX"
model = VisionEncoderDecoderModel.from_pretrained(model_id)
feature_extractor = ViTImageProcessor.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Preprocess image
image = Image.open("your_image_path.jpg").convert("RGB")
pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values.to(device)
# Generate caption
output_ids = model.generate(pixel_values, max_length=64, num_beams=4)
caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print("Generated caption:", caption)
```
---
## Citation
If you use this model, please cite:
```bibtex
@misc{henil2025axavision,
author = {Henilsinh Raj},
title = {Vit-Axavision-2-ChestX: Vision-Language Model for Chest X-Ray Captioning},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/Henil1/vit-axavision-2-ChestX}
}
|
mjpreneur/scalewithai | mjpreneur | 2025-06-16T04:23:09Z | 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 | 2025-06-16T03:53:56Z | ---
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: TOK
---
# Scalewithai
<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 `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/mjpreneur/scalewithai/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('mjpreneur/scalewithai', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/mjpreneur/scalewithai/discussions) to add images that show off what you’ve made with this LoRA.
|
gradientrouting-spar/standard_1_proxy_ntrain_25_ntrig_9_random_3x3_seed_1_20250616_041059 | gradientrouting-spar | 2025-06-16T04:21:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:21:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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### 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]
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Minahil-Malik-Official-Viral-Video/FULL.VIDEO.Minahil.Malik.Viral.Video.Tutorial.Official | Minahil-Malik-Official-Viral-Video | 2025-06-16T04:20:55Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:20:35Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
gradientrouting-spar/mc14_badmed_kl_div_dsd-42_msd-42_beta_kl-3_seed_1 | gradientrouting-spar | 2025-06-16T04:19:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:18:45Z | ---
library_name: transformers
tags: []
---
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gradientrouting-spar/mc14_badmed_kl_div_dsd-42_msd-42_beta_kl-3_seed_1_epoch_1 | gradientrouting-spar | 2025-06-16T04:18:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:18:30Z | ---
library_name: transformers
tags: []
---
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EJAustin/vit-base-patch16-224-in21k-finetuned-lora-food101 | EJAustin | 2025-06-16T04:15:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:07:05Z | ---
library_name: transformers
tags: []
---
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Cnam-LMSSC/mimi_throat_microphone | Cnam-LMSSC | 2025-06-16T04:14:27Z | 158 | 0 | transformers | [
"transformers",
"safetensors",
"mimi",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-06-03T01:29:56Z | ---
library_name: transformers
tags: []
---
## Inference script :
```python
import torch, torchaudio
from datasets import load_dataset
from moshi.models import loaders
weight_path = loaders.hf_hub_download("Cnam-LMSSC/mimi_throat_microphone", "kyutai_implementation.safetensors")
model = loaders.get_mimi(weight_path).eval()
model.set_num_codebooks(model.total_codebooks) # use all codebooks
test_dataset = load_dataset("Cnam-LMSSC/vibravox", "speech_clean", split="test", streaming=True)
audio_48kHz = torch.Tensor(next(iter(test_dataset))["audio.throat_microphone"]["array"])
audio_24kHz = torchaudio.functional.resample(audio_48kHz, orig_freq=48_000, new_freq=24_000)
enhanced_audio_24kHz = model.decode(model.encode(audio_24kHz[None, None, :]))
```
For streaming usage, please refer to this [script](https://github.com/kyutai-labs/moshi/blob/main/scripts/mimi_streaming_test.py) |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.15_epoch2 | MinaMila | 2025-06-16T04:12:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T04:10:46Z | ---
library_name: transformers
tags: []
---
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gradientrouting-spar/standard_1_proxy_ntrain_25_ntrig_9_animals_3x3_seed_1_seed_25_seed_2_seed_42_20250616_040025 | gradientrouting-spar | 2025-06-16T04:10:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T04:10:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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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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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Video-Billie-Eilish-mirror-photo-strap/Billie.Eilish.mirror.photo.strap.Viral.Video.Tutorial.Official | Video-Billie-Eilish-mirror-photo-strap | 2025-06-16T04:10:42Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:10:26Z | <a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
researchsocaai/gen-sql-8b-thinking-awq | researchsocaai | 2025-06-16T04:10:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:researchsocaai/gen-sql-8b-thinking-awq",
"base_model:quantized:researchsocaai/gen-sql-8b-thinking-awq",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-06-16T04:08:33Z | ---
base_model: dwikitheduck/gen-sql-8B-thinking-awq
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** dwikitheduck
- **License:** apache-2.0
- **Finetuned from model :** dwikitheduck/gen-sql-8B-thinking-awq
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)
|
New-tutorial-Hareem-Shah-viral-video/FULL.VIDEO.Hareem.Shah.Viral.Video.Tutorial.Official | New-tutorial-Hareem-Shah-viral-video | 2025-06-16T04:10:08Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:09:51Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
original-shruthi-narayanan-viral-video/wATCH.shruthi.narayanan.viral.video.original | original-shruthi-narayanan-viral-video | 2025-06-16T04:05:25Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:05:09Z | [](https://t.co/P8Ex9FtH0g) |
18-shruthi-narayanan-viral-video/wATCH.shruthi.narayanan.viral.video.original.link | 18-shruthi-narayanan-viral-video | 2025-06-16T04:04:00Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T04:03:52Z | [](https://t.co/P8Ex9FtH0g) |
seregadgl/sts_v11 | seregadgl | 2025-06-16T04:02:47Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:111476",
"loss:CosineSimilarityLoss",
"dataset:seregadgl/data_cross_gpt_139k",
"arxiv:1908.10084",
"base_model:sergeyzh/LaBSE-ru-sts",
"base_model:finetune:sergeyzh/LaBSE-ru-sts",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-06-16T04:02:21Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:111476
- loss:CosineSimilarityLoss
base_model: sergeyzh/LaBSE-ru-sts
widget:
- source_sentence: 'трюковый самокат plank 180 белый '
sentences:
- смарт-телевизор 75 sony kd-75x950h
- самокат для трюков плэнк 1.80 м белый
- xiaomi mi 11 8gb 128gb
- source_sentence: 'вейп vaporesso xros '
sentences:
- садовая ограда классика 4 2 м белый
- кухонные весы
- электронная сигарета voopoo drag
- source_sentence: серьги l atelier precieux 1628710
sentences:
- фильтр hepa для пылесоса варис st400
- потолочная люстра майтон nostalgia ceiling chandelier mod048pl-06g
- серьги atelier de bijoux 1628712
- source_sentence: 'мобильный геймпад триггерами x2 '
sentences:
- электроскутер nitro pro milano 750w led
- наушники без проводов мейзу ep52 lite
- геймпад с функцией триггеров x2
- source_sentence: комод 7 рисунком машинки 4 ящика
sentences:
- удлинитель far f 505 d lara выключателем 2 0м
- беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный
- комод 8 с изображением супергероев 6 ящиков
datasets:
- seregadgl/data_cross_gpt_139k
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on sergeyzh/LaBSE-ru-sts
results:
- task:
type: binary-classification
name: Binary Classification
dataset:
name: eval
type: eval
metrics:
- type: cosine_accuracy
value: 0.9722640832436311
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.630459189414978
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9724366041896361
name: Cosine F1
- type: cosine_f1_threshold
value: 0.5821653008460999
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.9647847565278758
name: Cosine Precision
- type: cosine_recall
value: 0.9802107980210798
name: Cosine Recall
- type: cosine_ap
value: 0.9945729266353226
name: Cosine Ap
- type: cosine_mcc
value: 0.9445047865635516
name: Cosine Mcc
---
# SentenceTransformer based on sergeyzh/LaBSE-ru-sts
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) on the [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sergeyzh/LaBSE-ru-sts](https://huggingface.co/sergeyzh/LaBSE-ru-sts) <!-- at revision 00c333ce29c9ad739f48baca9a578cd1e85094d4 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("seregadgl/sts_v11")
# Run inference
sentences = [
'комод 7 рисунком машинки 4 ящика',
'комод 8 с изображением супергероев 6 ящиков',
'беззеркальный фотоаппарат nikon z50 kit 16-50mm ilce-7cl красный',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Binary Classification
* Dataset: `eval`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:--------------------------|:-----------|
| cosine_accuracy | 0.9723 |
| cosine_accuracy_threshold | 0.6305 |
| cosine_f1 | 0.9724 |
| cosine_f1_threshold | 0.5822 |
| cosine_precision | 0.9648 |
| cosine_recall | 0.9802 |
| **cosine_ap** | **0.9946** |
| cosine_mcc | 0.9445 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### data_cross_gpt_139k
* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e)
* Size: 111,476 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 14.84 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.64 tokens</li><li>max: 55 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:-------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------|:-----------------|
| <code>нож кухонный 21см синий</code> | <code>кухонный нож 22см зелёный</code> | <code>0.0</code> |
| <code>блок питания универсальный для мерцающих флэш гирлянд rich led бахрома занавес нить белый</code> | <code>адаптер питания для мигающих led гирлянд "luminous decor" бахрома занавес нить зелёный</code> | <code>0.0</code> |
| <code>защитная пленка для apple iphone 6 прозрачная </code> | <code>protective film for apple iphone 6 transparent</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Evaluation Dataset
#### data_cross_gpt_139k
* Dataset: [data_cross_gpt_139k](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k) at [9e1f5ca](https://huggingface.co/datasets/seregadgl/data_cross_gpt_139k/tree/9e1f5ca30088e6f61ca5b9a742b38ef2c4fc7f3e)
* Size: 27,870 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 3 tokens</li><li>mean: 15.05 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 15.57 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.48</li><li>max: 1.0</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------|:-----------------|
| <code>сумка дорожная складная полет оранжевая bradex td 0599 </code> | <code>сумка для путешествий складная брадекс orange</code> | <code>1.0</code> |
| <code>наушники sennheiser hd 450bt белый </code> | <code>наушники сенхайзер hd 450bt white</code> | <code>1.0</code> |
| <code>перчатки stg al-05-1871 синие серые черные зеленыеполноразмерные xl</code> | <code>перчатки stg al-05-1871 blue gray black green full size xl</code> | <code>1.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 4.7459131195420915e-05
- `weight_decay`: 0.03196240090522689
- `num_train_epochs`: 2
- `warmup_ratio`: 0.014344463935915175
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 4.7459131195420915e-05
- `weight_decay`: 0.03196240090522689
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.014344463935915175
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | eval_cosine_ap |
|:------:|:----:|:-------------:|:---------------:|:--------------:|
| 0.0287 | 100 | 0.189 | - | - |
| 0.0574 | 200 | 0.0695 | - | - |
| 0.0861 | 300 | 0.067 | - | - |
| 0.1148 | 400 | 0.0643 | - | - |
| 0.1435 | 500 | 0.0594 | 0.0549 | 0.9862 |
| 0.1722 | 600 | 0.0565 | - | - |
| 0.2009 | 700 | 0.0535 | - | - |
| 0.2296 | 800 | 0.0506 | - | - |
| 0.2583 | 900 | 0.0549 | - | - |
| 0.2870 | 1000 | 0.0535 | 0.0451 | 0.9888 |
| 0.3157 | 1100 | 0.0492 | - | - |
| 0.3444 | 1200 | 0.0499 | - | - |
| 0.3731 | 1300 | 0.0486 | - | - |
| 0.4018 | 1400 | 0.0458 | - | - |
| 0.4305 | 1500 | 0.0458 | 0.0419 | 0.9877 |
| 0.4592 | 1600 | 0.0502 | - | - |
| 0.4879 | 1700 | 0.045 | - | - |
| 0.5166 | 1800 | 0.0435 | - | - |
| 0.5454 | 1900 | 0.0426 | - | - |
| 0.5741 | 2000 | 0.0422 | 0.0386 | 0.9906 |
| 0.6028 | 2100 | 0.0436 | - | - |
| 0.6315 | 2200 | 0.043 | - | - |
| 0.6602 | 2300 | 0.0432 | - | - |
| 0.6889 | 2400 | 0.0397 | - | - |
| 0.7176 | 2500 | 0.0394 | 0.0357 | 0.9903 |
| 0.7463 | 2600 | 0.039 | - | - |
| 0.7750 | 2700 | 0.0398 | - | - |
| 0.8037 | 2800 | 0.0394 | - | - |
| 0.8324 | 2900 | 0.0426 | - | - |
| 0.8611 | 3000 | 0.0345 | 0.0341 | 0.9921 |
| 0.8898 | 3100 | 0.0361 | - | - |
| 0.9185 | 3200 | 0.0365 | - | - |
| 0.9472 | 3300 | 0.0401 | - | - |
| 0.9759 | 3400 | 0.0391 | - | - |
| 1.0046 | 3500 | 0.0342 | 0.0310 | 0.9928 |
| 1.0333 | 3600 | 0.0267 | - | - |
| 1.0620 | 3700 | 0.0264 | - | - |
| 1.0907 | 3800 | 0.0263 | - | - |
| 1.1194 | 3900 | 0.0248 | - | - |
| 1.1481 | 4000 | 0.0282 | 0.0301 | 0.9928 |
| 1.1768 | 4100 | 0.0279 | - | - |
| 1.2055 | 4200 | 0.0258 | - | - |
| 1.2342 | 4300 | 0.0248 | - | - |
| 1.2629 | 4400 | 0.0289 | - | - |
| 1.2916 | 4500 | 0.0261 | 0.0291 | 0.9935 |
| 1.3203 | 4600 | 0.0262 | - | - |
| 1.3490 | 4700 | 0.0276 | - | - |
| 1.3777 | 4800 | 0.0256 | - | - |
| 1.4064 | 4900 | 0.0272 | - | - |
| 1.4351 | 5000 | 0.0283 | 0.0284 | 0.9939 |
| 1.4638 | 5100 | 0.0254 | - | - |
| 1.4925 | 5200 | 0.0252 | - | - |
| 1.5212 | 5300 | 0.0234 | - | - |
| 1.5499 | 5400 | 0.0228 | - | - |
| 1.5786 | 5500 | 0.0248 | 0.0277 | 0.9941 |
| 1.6073 | 5600 | 0.024 | - | - |
| 1.6361 | 5700 | 0.0225 | - | - |
| 1.6648 | 5800 | 0.0234 | - | - |
| 1.6935 | 5900 | 0.0226 | - | - |
| 1.7222 | 6000 | 0.0248 | 0.0265 | 0.9942 |
| 1.7509 | 6100 | 0.0247 | - | - |
| 1.7796 | 6200 | 0.0219 | - | - |
| 1.8083 | 6300 | 0.026 | - | - |
| 1.8370 | 6400 | 0.0209 | - | - |
| 1.8657 | 6500 | 0.0252 | 0.0262 | 0.9945 |
| 1.8944 | 6600 | 0.0218 | - | - |
| 1.9231 | 6700 | 0.0223 | - | - |
| 1.9518 | 6800 | 0.0228 | - | - |
| 1.9805 | 6900 | 0.0242 | - | - |
| 2.0 | 6968 | - | 0.0257 | 0.9946 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
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-->
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## Model Card Contact
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sonnykoalu/xdf | sonnykoalu | 2025-06-16T04:02:41Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-16T04:01:45Z | ---
license: other
license_name: none
license_link: LICENSE
---
|
Assamese-Viral-Video/VIDEO.Assamese.Viral.Video.Tutorial.Official | Assamese-Viral-Video | 2025-06-16T04:01:54Z | 0 | 0 | null | [
"region:us"
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"region:us"
] | null | 2025-06-16T03:56:37Z | [](https://t.co/P8Ex9FtH0g) |
MinaMila/phi3_unlearned_2nd_5e-7_1.0_0.25_0.5_0.25_epoch1 | MinaMila | 2025-06-16T03:57:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:55:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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18-maryam-faisal-Leaked-Viral-video/TRENDING.maryam.faisal.Viral.Video.Link.Tutorial | 18-maryam-faisal-Leaked-Viral-video | 2025-06-16T03:54:29Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:54:17Z | 01 seconds ago
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶](https://infobal.com.ar/watch-full-video/?Bados.all)
[🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► 𝖶𝖠𝖳𝖢𝖧 𝖭𝖮𝖶 FREE](https://infobal.com.ar/watch-full-video/?Bados.all)
<a href="https://infobal.com.ar/watch-full-video/?Bados.all" rel="nofollow" data-target="animated-image.originalLink"><img src="https://media.post.rvohealth.io/wp-content/uploads/2024/02/video-overlay-bed-sex-couple-original.jpg" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
erdem-erdem/Qwen2.5-3B-Instruct-countdown-ps-grpo-r32 | erdem-erdem | 2025-06-16T03:53:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:52:24Z | ---
base_model: unsloth/Qwen2.5-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** erdem-erdem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sil-ai/madlad400-finetuned-onr_x_wol-tpi | sil-ai | 2025-06-16T03:53:38Z | 7 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"onr",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-11T00:20:59Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-onr_x_wol-tpi
results: []
language:
- onr
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-onr_x_wol-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Onnele, Wolwale to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Onnele, Wolwale (`onr`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Onnele, Wolwale:
- Onnele, Wolwale Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-onr_x_wol-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-onr_x_wol-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-onr_x_wol-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- Chrf: 84.1544
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2296 | 7.7341 | 1600 | 0.1639 | 81.9949 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-onr_x_rbr-tpi | sil-ai | 2025-06-16T03:53:37Z | 9 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"onr",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-10T23:21:25Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-onr_x_rbr-tpi
results: []
language:
- onr
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-onr_x_rbr-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Onnele, Romei-Barera to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Onnele, Romei-Barera (`onr`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Onnele, Romei-Barera:
- Onnele, Romei-Barera Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-onr_x_rbr-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-onr_x_rbr-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-onr_x_rbr-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1796
- Chrf: 79.0591
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2929 | 7.7435 | 1600 | 0.2140 | 76.7801 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-onr_x_goi-tpi | sil-ai | 2025-06-16T03:53:36Z | 8 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"onr",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-10T23:22:35Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-onr_x_goi-tpi
results: []
language:
- onr
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-onr_x_goi-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Onnele, Goiniri to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Onnele, Goiniri (`onr`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Onnele, Goiniri:
- Onnele, Goiniri Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-onr_x_goi-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-onr_x_goi-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-onr_x_goi-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1157
- Chrf: 85.9775
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.197 | 7.7295 | 1600 | 0.1436 | 83.9241 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-uni-tpi | sil-ai | 2025-06-16T03:53:34Z | 110 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"uni",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-05T03:56:50Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-uni-tpi
results: []
language:
- uni
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-uni-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Ramo to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Ramo (`uni`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Ramo:
- Ramo Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-uni-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-uni-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-uni-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1777
- Chrf: 78.0127
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2819 | 7.7482 | 1600 | 0.2113 | 75.2590 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-bpe_x_bar-tpi | sil-ai | 2025-06-16T03:53:32Z | 14 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"bpe",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-06T19:20:16Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-bpe_x_bar-tpi
results: []
language:
- bpe
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-bpe_x_bar-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Bauni, Barupu to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Bauni, Barupu (`bpe`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Bauni, Barupu:
- Bauni, Barupu Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-bpe_x_bar-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-bpe_x_bar-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-bpe_x_bar-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1551
- Chrf: 80.7709
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2676 | 7.6923 | 1600 | 0.1899 | 78.1510 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
sil-ai/madlad400-finetuned-sso-tpi | sil-ai | 2025-06-16T03:53:31Z | 11 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"sso",
"tpi",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-04T14:10:17Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-sso-tpi
results: []
language:
- sso
- tpi
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-sso-tpi
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Sissono to Tok Pisin.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Sissono (`sso`)
- **Target language:** Tok Pisin (`tpi`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Sissono:
- Sissono Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-sso-tpi")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-sso-tpi")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-sso-tpi
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2259
- Chrf: 72.9830
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3649 | 7.7155 | 1600 | 0.2684 | 70.3240 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
opendiffusionai/t5-v1_1-xl-encoder-only | opendiffusionai | 2025-06-16T03:53:29Z | 21 | 0 | null | [
"safetensors",
"t5",
"base_model:google/t5-v1_1-xl",
"base_model:finetune:google/t5-v1_1-xl",
"license:apache-2.0",
"region:us"
] | null | 2025-06-15T03:13:22Z | ---
license: apache-2.0
base_model:
- google/t5-v1_1-xl
tags:
- t5
---
This is just the encoder weights from "google/t5-v1_1-xl"
It takes 11GB down to 4GB.
The script to do the extraction is included here as
[transform.py](transform.py)
Edit: Now that I have this in a convenient form...
I got a chance to test t5-xxl projected down to 2048, vs this t5-xl
Surprisingly, even with an untrained projection layer, trivial embedding diversity scores rate
the projected xxl version higher than native xl at 2048.
So, while this model will continue to exist as a convenient way to compare.. and possibly as something
to use if you are really, really REALLY tight on memory... you are probably best off
using t5-xxl whenever you can. |
sil-ai/madlad400-finetuned-tpi-uni | sil-ai | 2025-06-16T03:53:25Z | 61 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"uni",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-05T03:56:30Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-uni
results: []
language:
- tpi
- uni
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-uni
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Ramo.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Ramo (`uni`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Ramo:
- Ramo Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-uni")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-uni")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-uni
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2914
- Chrf: 70.9472
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4351 | 7.7482 | 1600 | 0.3454 | 67.9624 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
New-tutorial-Kayla-Nella-viral-video/FULL.VIDEO.Kayla.Nella.Viral.Video.Tutorial.Official | New-tutorial-Kayla-Nella-viral-video | 2025-06-16T03:53:11Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:52:55Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
sil-ai/madlad400-finetuned-tpi-bpe_x_pou | sil-ai | 2025-06-16T03:52:33Z | 136 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"translation",
"tpi",
"bpe",
"base_model:jbochi/madlad400-3b-mt",
"base_model:adapter:jbochi/madlad400-3b-mt",
"license:apache-2.0",
"region:us"
] | translation | 2025-06-06T21:34:34Z | ---
base_model: jbochi/madlad400-3b-mt
library_name: peft
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: madlad400-finetuned-tpi-bpe_x_pou
results: []
language:
- tpi
- bpe
model_type: Translation
pipeline_tag: translation
---
# madlad400-finetuned-tpi-bpe_x_pou
This model is a fine-tuned version of `jbochi/madlad400-3b-mt` for translation from Tok Pisin to Bauni, Pou.
## Model details
- **Developed by:** SIL Global
- **Finetuned from model:** jbochi/madlad400-3b-mt
- **Model type:** Translation
- **Source language:** Tok Pisin (`tpi`)
- **Target language:** Bauni, Pou (`bpe`)
- **License:** closed/private
## Datasets
The model was trained on a parallel corpus of plain text files:
Tok Pisin:
- Tok Pisin back-translation
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
Bauni, Pou:
- Bauni, Pou Scriptures
- License: All rights reserved, Wycliffe Bible Translators. Used with permission.
## Usage
You can use this model with the `transformers` library like this:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_pou")
model = AutoModelForSeq2SeqLM.from_pretrained("sil-ai/madlad400-finetuned-tpi-bpe_x_pou")
inputs = tokenizer("Your input text here", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0]))
```
# madlad400-finetuned-tpi-bpe_x_pou
This model is a fine-tuned version of [jbochi/madlad400-3b-mt](https://huggingface.co/jbochi/madlad400-3b-mt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2035
- Chrf: 79.0847
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Chrf |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3414 | 7.8144 | 1600 | 0.2413 | 76.9720 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1 |
dgambettaphd/M_llm2_run2_gen8_WXS_doc1000_synt64_lr1e-04_acm_FRESH | dgambettaphd | 2025-06-16T03:52:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T03:51:55Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Mezzo-Fun-Viral-Video/VIDEO.mezzo.fun.Viral.Video.Tutorial.Official | Mezzo-Fun-Viral-Video | 2025-06-16T03:50:52Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-16T03:48:42Z | <a href="https://t.co/98E3uGhPfJ" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
huihui-ai/Huihui-MoE-1.2B-A0.6B | huihui-ai | 2025-06-16T03:49:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3_moe",
"text-generation",
"moe",
"conversational",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T02:31:56Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-0.6B
- suayptalha/Qwen3-0.6B-Code-Expert
- suayptalha/Qwen3-0.6B-Math-Expert
- suayptalha/Qwen3-0.6B-Medical-Expert
library_name: transformers
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- moe
---
# huihui-ai/Huihui-MoE-1.2B-A0.6B
## Model Overview
Huihui-MoE-1.2B-A0.6B is a **Mixture of Experts (MoE)** language model developed by **huihui.ai**, built upon the **[Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)** base model. It enhances the standard Transformer architecture by replacing MLP layers with MoE layers, each containing 3 experts, to achieve high performance with efficient inference. The model is designed for natural language processing tasks, including text generation, question answering, and conversational applications.
### Note
[huihui-ai/Huihui-MoE-1B-A0.6B](https://huggingface.co/huihui-ai/Huihui-MoE-1B-A0.6B) Because `tie_word_embeddings=True`, the parameters for the lm_head were not saved, which causes ollama to be unable to use it. Therefore, this version supports ollama.
- **Architecture**: Qwen3MoeForCausalLM model with 3 experts per layer (num_experts=3), activating 1 expert per token (num_experts_per_tok=1).
- **Total Parameters**: ~1.2 billion (1.2B)
- **Activated Parameters**: ~0.62 billion (0.6B) during inference, comparable to Qwen3-0.6B
- **Developer**: huihui.ai
- **Release Date**: June 2025
- **License**: Inherits the license of the Qwen3 base model (apache-2.0)
## Expert Models:
### Coding:
[suayptalha/Qwen3-0.6B-Code-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Code-Expert)
This model was fully fine-tuned with BF16 on first 20k rows of `nvidia/OpenCodeReasoning` dataset for 1 epoch.
### Math:
[suayptalha/Qwen3-0.6B-Math-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Math-Expert)
This model was fully fine-tuned with BF16 on entire `unsloth/OpenMathReasoning-mini` dataset for 1 epoch.
### Medical:
[suayptalha/Qwen3-0.6B-Medical-Expert](https://huggingface.co/suayptalha/Qwen3-0.6B-Medical-Expert)
This model was fully fine-tuned with BF16 on first 20k rows of `FreedomIntelligence/medical-o1-reasoning-SFT` dataset for 1 epoch.
### Instruction Following:
[Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
`Qwen/Qwen3-0.6B` model was directly used for this expert, no fine-tune was applied.
## Training
- **Base Model**: Qwen3-0.6B, pre-trained by the Qwen team, Experts, pre-trained by the Suayptalha team.
- **Conversion**: The model copies embeddings, self-attention, and normalization weights from Qwen3-0.6B, replacing MLP layers with MoE layers (3 experts). Gating weights are randomly initialized.
- **Fine-Tuning**: Not fine-tuned; users are recommended to fine-tune for specific tasks to optimize expert routing. The fine-tuned version is already available and can be referred to as [huihui-ai/Huihui-MoE-1.2B-A0.6B-SFT](https://huggingface.co/huihui-ai/Huihui-MoE-1.2B-A0.6B-SFT).
## ollama
You can use [huihui_ai/huihui-moe:1.2b](https://ollama.com/huihui_ai/huihui-moe:1.2b) directly,
Switch the thinking toggle using /set think and /set nothink
```
ollama run huihui_ai/huihui-moe:1.2b
```
## Usage
```
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal
import random
import numpy as np
import time
from collections import Counter
cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)
print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Huihui-MoE-1.2B-A0.6B"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_32 = BitsAndBytesConfig(
load_in_32bit=True,
bnb_32bit_compute_dtype=torch.bfloat16,
bnb_32bit_use_double_quant=True,
llm_int32_enable_fp32_cpu_offload=True,
)
model = AutoModelForCausalLM.from_pretrained(
NEW_MODEL_ID,
device_map="auto",
trust_remote_code=True,
#quantization_config=quant_config_32,
torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
messages = []
nothink = False
same_seed = False
skip_prompt=True
skip_special_tokens=True
do_sample = True
def set_random_seed(seed=None):
"""Set random seed for reproducibility. If seed is None, use int(time.time())."""
if seed is None:
seed = int(time.time()) # Convert float to int
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # If using CUDA
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
return seed # Return seed for logging if needed
class CustomTextStreamer(TextStreamer):
def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
self.generated_text = ""
self.stop_flag = False
self.init_time = time.time() # Record initialization time
self.end_time = None # To store end time
self.first_token_time = None # To store first token generation time
self.token_count = 0 # To track total tokens
def on_finalized_text(self, text: str, stream_end: bool = False):
if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
self.first_token_time = time.time()
self.generated_text += text
# Count tokens in the generated text
tokens = self.tokenizer.encode(text, add_special_tokens=False)
self.token_count += len(tokens)
print(text, end="", flush=True)
if stream_end:
self.end_time = time.time() # Record end time when streaming ends
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
self.end_time = time.time() # Record end time when generation is stopped
def get_metrics(self):
"""Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
if self.end_time is None:
self.end_time = time.time() # Set end time if not already set
total_time = self.end_time - self.init_time # Total time from init to end
tokens_per_second = self.token_count / total_time if total_time > 0 else 0
first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
metrics = {
"init_time": self.init_time,
"first_token_time": self.first_token_time,
"first_token_latency": first_token_latency,
"end_time": self.end_time,
"total_time": total_time, # Total time in seconds
"total_tokens": self.token_count,
"tokens_per_second": tokens_per_second
}
return metrics
def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
enable_thinking = not nothink,
add_generation_prompt=True,
return_tensors="pt"
)
attention_mask = torch.ones_like(input_ids, dtype=torch.long)
tokens = input_ids.to(model.device)
attention_mask = attention_mask.to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
def signal_handler(sig, frame):
streamer.stop_generation()
print("\n[Generation stopped by user with Ctrl+C]")
signal.signal(signal.SIGINT, signal_handler)
generate_kwargs = {}
if do_sample:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"temperature": 0.6,
"top_k": 20,
"top_p": 0.95,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
else:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
tokens,
attention_mask=attention_mask,
#use_cache=False,
pad_token_id=tokenizer.pad_token_id,
streamer=streamer,
**generate_kwargs
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids, attention_mask
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
init_seed = set_random_seed()
# List to store activated expert indices
activated_experts = []
# Define hook function to capture gate_probs output
def hook_fn(module, input, output):
# output is gate_probs, shape: [batch_size, sequence_length, num_experts]
gate_probs = output
# Compute top-1 expert indices (since only one expert is activated)
_, topk_indices = gate_probs.topk(1, dim=-1) # Take top-1
# Flatten and store activated expert indices
activated_experts.extend(topk_indices.squeeze(-1).view(-1).cpu().tolist())
hooks = []
for layer in model.model.layers:
hooks.append(layer.mlp.gate.register_forward_hook(hook_fn))
while True:
if same_seed:
set_random_seed(init_seed)
else:
init_seed = set_random_seed()
print(f"\nnothink: {nothink}")
print(f"skip_prompt: {skip_prompt}")
print(f"skip_special_tokens: {skip_special_tokens}")
print(f"do_sample: {do_sample}")
print(f"same_seed: {same_seed}, {init_seed}\n")
user_input = input("User: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = []
print("Chat history cleared. Starting a new conversation.")
continue
if user_input.lower() == "/nothink":
nothink = not nothink
continue
if user_input.lower() == "/skip_prompt":
skip_prompt = not skip_prompt
continue
if user_input.lower() == "/skip_special_tokens":
skip_special_tokens = not skip_special_tokens
continue
if user_input.lower().startswith("/same_seed"):
parts = user_input.split()
if len(parts) == 1: # /same_seed (no number)
same_seed = not same_seed # Toggle switch
elif len(parts) == 2: # /same_seed <number>
try:
init_seed = int(parts[1]) # Extract and convert number to int
same_seed = True
except ValueError:
print("Error: Please provide a valid integer after /same_seed")
continue
if user_input.lower() == "/do_sample":
do_sample = not do_sample
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
activated_experts = []
response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 320960)
print("\n\nMetrics:")
for key, value in metrics.items():
print(f" {key}: {value}")
# Count the frequency of each activated expert
expert_counts = Counter(activated_experts)
# Print activation statistics
print("\nActivated Expert Statistics:")
for expert_idx, count in sorted(expert_counts.items()):
print(f"Expert {expert_idx}: {count} times")
print("", flush=True)
if stop_flag:
continue
messages.append({"role": "assistant", "content": response})
# Remove all hooks after inference
for h in hooks: h.remove()
```
## Applications
- **Text Generation: Articles**, dialogues, and creative writing.
- **Question Answering**: Information retrieval and query resolution.
- **Conversational AI**: Multi-turn dialogues for chatbots.
- **Research**: Exploration of MoE architectures and efficient model scaling.
## Limitations
- **Fine-Tuning Required**: Randomly initialized gating weights may lead to suboptimal expert utilization without fine-tuning.
- **Compatibility**: Developed with transformers 4.52.4; ensure matching versions to avoid loading issues.
- **Inference Speed**: While efficient for an MoE model, performance depends on hardware (GPU recommended).
## Ethical Considerations
- **Bias**: Inherits potential biases from the Qwen3-0.6B base model; users should evaluate outputs for fairness.
- **Usage**: Intended for research and responsible applications; avoid generating harmful or misleading content.
## Contact
- **Developer**: huihui.ai
- **Repository**: huihui-ai/Huihui-MoE-1.2B-A0.6B (available locally or on Hugging Face)
- **Issues**: Report bugs or request features via the repository or please send an email to [email protected]
## Acknowledgments
- Built upon the Qwen3-0.6B model by the Qwen team.
- Built upon the Experts model by the Suayptalha team.
- Powered by the Hugging Face transformers library. |
MinaMila/gemma_2b_unlearned_2nd_5e-7_1.0_0.05_0.05_0.25_epoch1 | MinaMila | 2025-06-16T03:48:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T03:46:51Z | ---
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] |
sunblaze-ucb/OLMo-2-7B-SFT-GRPO-MATH-1EPOCH | sunblaze-ucb | 2025-06-16T03:47:48Z | 0 | 0 | null | [
"safetensors",
"olmo2",
"text-generation",
"conversational",
"en",
"dataset:math",
"arxiv:2505.19590",
"base_model:allenai/OLMo-2-1124-7B-SFT",
"base_model:finetune:allenai/OLMo-2-1124-7B-SFT",
"license:apache-2.0",
"region:us"
] | text-generation | 2025-06-16T03:38:17Z | ---
base_model:
- allenai/OLMo-2-1124-7B-SFT
license: apache-2.0
datasets:
- math
metrics:
- accuracy
pipeline_tag: text-generation
language:
- en
---
# OLMo-2-7B-SFT-GRPO-MATH-1EPOCH
**Description:**
A GRPO-fine-tuned version of allenai/OLMo-2-1124-7B-SFT trained on the MATH dataset.
---
## Citation
```bibtex
@article{zhao2025learning,
title={Learning to Reason without External Rewards},
author={Zhao, Xuandong and Kang, Zhewei and Feng, Aosong and Levine, Sergey and Song, Dawn},
journal={arXiv preprint arXiv:2505.19590},
year={2025}
}
``` |
Songyao86/blackjack-qlearning-agent | Songyao86 | 2025-06-16T03:46:03Z | 0 | 0 | reinforcement-learning | [
"reinforcement-learning",
"Blackjack-v1",
"q-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-16T03:46:02Z | ---
tags:
- Blackjack-v1
- q-learning
- reinforcement-learning
- custom-implementation
library_name: reinforcement-learning
model-index:
- name: blackjack-qlearning-agent
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Blackjack-v1
type: Blackjack-v1
metrics:
- type: mean_reward
value: -0.19 +/- 0.95
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing **Blackjack-v1**
## Training Parameters
- **Environment ID**: `Blackjack-v1`
- **Training Episodes**: 10000
- **Max Steps per Episode**: 99
- **Learning Rate**: 0.7
- **Gamma (Discount Factor)**: 0.95
## Evaluation Results
- **Mean Reward**: -0.19 ± 0.95
- **Evaluation Episodes**: 100
## Usage
```python
from huggingface_hub import hf_hub_download
import pickle
import gymnasium as gym
import numpy as np
# 请将下面的占位符替换为你的实际仓库信息
repo_id = "YOUR_USERNAME/YOUR_REPO_NAME" # 替换为你的仓库
filename = "q-learning.pkl"
# 加载模型
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(model_path, "rb") as f:
model = pickle.load(f)
# 重建环境
env = gym.make(
model["env_id"],
render_mode="rgb_array",
**model.get("env_config", {})
)
# 使用Q表进行推理
qtable = model["qtable"]
# 简单的推理示例
state = env.reset()
terminated = False
while not terminated:
# 状态转换为索引
if isinstance(state, tuple):
state_idx = model.get("state_to_index", lambda s: s)(state)
else:
state_idx = state
action = np.argmax(qtable[state_idx])
state, reward, terminated, truncated, _ = env.step(action)
|
dimasandhk/multilabel-emotion-text-classification | dimasandhk | 2025-06-16T03:43:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"en",
"base_model:microsoft/deberta-v3-large",
"base_model:finetune:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-15T21:14:38Z | ---
license: mit
language:
- en
base_model:
- microsoft/deberta-v3-large
tags:
- text-classification
- transformers
pipeline_tag: text-classification
library_name: transformers
--- |
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