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| last_modified
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| downloads
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| likes
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11.7k
| library_name
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erfanloghmani/DeconfoundLM
|
erfanloghmani
| 2025-06-24T23:25:42Z | 0 | 0 | null |
[
"arxiv:2506.00152",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2025-06-24T23:17:09Z |
---
license: cc-by-nc-sa-4.0
---
# Aligning Language Models with Observational Data
Official repository of the [paper](https://arxiv.org/abs/2506.00152) **"Aligning Language Models with Observational Data: Opportunities and Risks from a Causal Perspective"**. The project explores methods to fine-tune large language models (LLMs) using observational data, tackling challenges like spurious correlations and confounding. We propose **DECONFOUNDLM**, a novel approach to mitigate these issues by correcting for known confounders in the reward signals.
# Code to be released soon.
You can find the project webpage at [deconfoundlm.github.io](https://deconfoundlm.github.io).
|
tvfanatics/deepseek_8b_mental_health
|
tvfanatics
| 2025-06-24T23:25:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T05:16:10Z |
---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** tvfanatics
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mattfutureflow/matt-images
|
mattfutureflow
| 2025-06-24T23:24:33Z | 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
| 2025-06-24T22:54:06Z |
---
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: Matt
---
# Matt Images
<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 `Matt` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Matt",
"lora_weights": "https://huggingface.co/mattfutureflow/matt-images/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('mattfutureflow/matt-images', weight_name='lora.safetensors')
image = pipeline('Matt').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/mattfutureflow/matt-images/discussions) to add images that show off what you’ve made with this LoRA.
|
rasoultilburg/SocioCausaNet
|
rasoultilburg
| 2025-06-24T23:23:27Z | 64 | 0 |
transformers
|
[
"transformers",
"safetensors",
"joint_causal",
"feature-extraction",
"causal-extraction",
"relation-extraction",
"bert",
"pytorch",
"causality",
"token-classification",
"custom_code",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:gpl-2.0",
"region:us"
] |
token-classification
| 2025-06-08T18:56:21Z |
---
license: gpl-2.0
language: en
base_model: google-bert/bert-base-uncased
pipeline_tag: token-classification
tags:
- causal-extraction
- relation-extraction
- bert
- pytorch
- causality
library_name: transformers
---
# JointCausalModel for Causal Extraction
This repository contains JointCausalModel, a PyTorch-based model for joint causal extraction, optimized for use with the Hugging Face transformers library. The model is built upon `google-bert/bert-base-uncased` and is designed to identify and structure causal relationships within text.
**GitHub Repository**: [https://github.com/rasoulnorouzi/JointLearning](https://github.com/rasoulnorouzi/JointLearning/tree/main)
## Model Description
This model performs three tasks simultaneously:
1. **Sentence-level Causal Classification**: Determines whether a sentence contains a causal statement.
2. **Span Extraction**: Identifies the specific Cause, Effect, and combined Cause-Effect spans within the text using a BIO tagging scheme.
3. **Relation Extraction**: Establishes the relationships between the identified cause and effect spans.
> **Note**: This model uses a custom implementation and requires `trust_remote_code=True` when loading with AutoModel.
## How to Use
To get started, load the model and tokenizer from the Hugging Face Hub:
```python
from transformers import AutoModel, AutoTokenizer
repo_id = "rasoultilburg/SocioCausaNet"
model = AutoModel.from_pretrained(
repo_id,
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
repo_id
)
```
### Inference API
The primary method for inference is `model.predict()`, which processes a list of sentences and returns detailed causal information:
```python
# Example of a simple prediction call
results = model.predict(
sents=["The heavy rainfall led to severe flooding in the coastal regions."],
tokenizer=tokenizer,
rel_mode="neural",
rel_threshold=0.5,
cause_decision="cls+span"
)
```
### Understanding the predict() Parameters
Think of this model as a "Causality Detective." The parameters are the instructions you give the detective on how to investigate the text.
| Parameter | What it is & How it works | Analogy |
|-----------|---------------------------|---------|
| `sents` | The list of sentences you want the model to analyze. | The "case files" you give to the detective. |
| `rel_mode` | Strategy for finding relationships.<br/>- `'auto'`: A smart, efficient mode. For simple cases (one cause-one effect, one cause-multiple effects, multiple causes-one effect), it automatically connects them using rules. For complex cases (multiple causes and multiple effects), it uses a neural network to determine connections.<br/>- `'neural_only'`: Uses a neural network to validate every potential cause-effect connection, checking whether there is a relationship between each pair of entities. More thorough but slower. | The Detective's Strategy<br/>- `'auto'` is the Smart Detective who uses simple logic for obvious cases but calls in the expert (neural network) for complex situations.<br/>- `'neural_only'` is the Expert Detective who carefully analyzes every possible connection using advanced techniques (neural network) regardless of complexity. |
| `rel_threshold` | The confidence score needed to report a relationship (from 0.0 to 1.0).<br/>- High value (e.g., 0.8): Only reports relationships it's very sure about. Fewer, but more accurate results.<br/>- Low value (e.g., 0.3): Reports any potential link, even hunches. More results, but some may be incorrect. | The Detective's "Burden of Proof."<br/>- High value: Needs a lot of evidence before making an accusation.<br/>- Low value: Follows up on even the smallest lead. |
| `cause_decision` | The criteria for deciding if a sentence is causal.<br/>- `'cls_only'`: Decides based on overall sentence meaning.<br/>- `'span_only'`: Decides only if it finds distinct "cause" and "effect" phrases.<br/>- `'cls+span'`: Strictest mode. Sentence must have causal meaning AND contain distinct cause/effect phrases. | The Panel of Judges<br/>- `'cls_only'` is the "Big Picture" Judge.<br/>- `'span_only'` is the "Evidence-Focused" Judge.<br/>- `'cls+span'` requires both judges to agree. Most reliable option. |
## Complete Example
Here is a complete, runnable example demonstrating how to use the model and format the output:
```python
from transformers import AutoModel, AutoTokenizer
import json
# 1. Load the model and tokenizer
repo_id = "rasoultilburg/SocioCausaNet"
model = AutoModel.from_pretrained(repo_id, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
# 2. Define input sentences
sentences = [
"Insomnia causes depression and a lack of concentration in children.",
"Due to the new regulations, the company's profits declined sharply.",
"The sun rises in the east." # Non-causal example
]
# 3. Get predictions from the model
results = model.predict(
sentences,
tokenizer=tokenizer,
rel_mode="neural",
rel_threshold=0.5,
cause_decision="cls+span"
)
# 4. Print the results in a readable format
print(json.dumps(results, indent=2, ensure_ascii=False))
```
### Example Output
The predict method returns a list of dictionaries, where each dictionary corresponds to an input sentence:
```json
[
{
"text": "Insomnia causes depression and a lack of concentration in children.",
"causal": true,
"relations": [
{
"cause": "Insomnia",
"effect": "depression",
"type": "Rel_CE"
},
{
"cause": "Insomnia",
"effect": "a lack of concentration in children",
"type": "Rel_CE"
}
]
},
{
"text": "Due to the new regulations, the company's profits declined sharply.",
"causal": true,
"relations": [
{
"cause": "the new regulations",
"effect": "the company's profits declined sharply",
"type": "Rel_CE"
}
]
},
{
"text": "The sun rises in the east.",
"causal": false,
"relations": [],
"spans": []
}
]
```
## Model Architecture
The JointCausalModel requires custom code, which is why `trust_remote_code=True` is necessary. The architecture consists of a BERT encoder followed by three specialized heads for the joint tasks.
The key files defining the model are:
- `modeling_joint_causal.py`: Contains the main JointCausalModel class which defines the model's architecture. It inherits from `transformers.PreTrainedModel` to ensure compatibility with the Hugging Face ecosystem.
- `configuration_joint_causal.py`: Defines the JointCausalConfig class, which stores the model's configuration and hyperparameters.
## Citation
If you use this model in your work, please consider citing this repository.
```bibtex
@misc{jointcausalmodel2024,
title={JointCausalModel: Joint Learning for Causal Extraction},
author={Rasoul Norouzi},
year={2024},
howpublished={GitHub Repository},
url={https://github.com/rasoulnorouzi/JointLearning/tree/main}
}
```
For more details and source code, visit the [GitHub repository](https://github.com/rasoulnorouzi/JointLearning/tree/main)
|
abdullahsubasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin
|
abdullahsubasi
| 2025-06-24T23:20:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am graceful pensive puffin",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T16:21:30Z |
---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am graceful pensive puffin
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="abdullahsubasi/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-graceful_pensive_puffin", 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: 0.15.2
- Transformers: 4.48.2
- Pytorch: 2.5.1
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
secmlr/best_n_no_rationale_poc_agent_withjava_final_model_agent_h100_64step_5epoch
|
secmlr
| 2025-06-24T23:14:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:secmlr/final_model",
"base_model:finetune:secmlr/final_model",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T03:15:06Z |
---
library_name: transformers
license: apache-2.0
base_model: secmlr/final_model
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: best_n_no_rationale_poc_agent_withjava_final_model_agent_h100_64step_5epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# best_n_no_rationale_poc_agent_withjava_final_model_agent_h100_64step_5epoch
This model is a fine-tuned version of [secmlr/final_model](https://huggingface.co/secmlr/final_model) on the best_n_no_rationale_poc_agent_withjava dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
KasuleTrevor/all_mini_lm_text_classifier_nyn
|
KasuleTrevor
| 2025-06-24T23:14:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:KasuleTrevor/all-MiniLM-L12-v2-rny-intents",
"base_model:finetune:KasuleTrevor/all-MiniLM-L12-v2-rny-intents",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-24T22:46:10Z |
---
library_name: transformers
base_model: KasuleTrevor/all-MiniLM-L12-v2-rny-intents
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: all_mini_lm_text_classifier_nyn
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# all_mini_lm_text_classifier_nyn
This model is a fine-tuned version of [KasuleTrevor/all-MiniLM-L12-v2-rny-intents](https://huggingface.co/KasuleTrevor/all-MiniLM-L12-v2-rny-intents) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3562
- Accuracy: 0.943
- Precision: 0.945
- Recall: 0.943
- F1: 0.942
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:-----:|
| 2.5968 | 1.0 | 293 | 1.7473 | 0.633 | 0.535 | 0.633 | 0.555 |
| 1.2106 | 2.0 | 586 | 0.6798 | 0.828 | 0.82 | 0.828 | 0.804 |
| 0.5287 | 3.0 | 879 | 0.4751 | 0.896 | 0.894 | 0.896 | 0.891 |
| 0.3188 | 4.0 | 1172 | 0.3477 | 0.904 | 0.916 | 0.904 | 0.901 |
| 0.2335 | 5.0 | 1465 | 0.2991 | 0.936 | 0.94 | 0.936 | 0.936 |
| 0.1855 | 6.0 | 1758 | 0.3184 | 0.932 | 0.937 | 0.932 | 0.932 |
| 0.1609 | 7.0 | 2051 | 0.3253 | 0.934 | 0.939 | 0.934 | 0.933 |
| 0.1398 | 8.0 | 2344 | 0.2569 | 0.949 | 0.952 | 0.949 | 0.949 |
| 0.1151 | 9.0 | 2637 | 0.3135 | 0.943 | 0.946 | 0.943 | 0.943 |
| 0.0997 | 10.0 | 2930 | 0.3000 | 0.951 | 0.953 | 0.951 | 0.951 |
| 0.0866 | 11.0 | 3223 | 0.3140 | 0.945 | 0.948 | 0.945 | 0.945 |
| 0.0615 | 12.0 | 3516 | 0.3165 | 0.945 | 0.948 | 0.945 | 0.945 |
| 0.0574 | 13.0 | 3809 | 0.3870 | 0.934 | 0.94 | 0.934 | 0.935 |
| 0.0459 | 14.0 | 4102 | 0.3428 | 0.947 | 0.951 | 0.947 | 0.947 |
| 0.0423 | 15.0 | 4395 | 0.3565 | 0.945 | 0.948 | 0.945 | 0.945 |
| 0.0412 | 16.0 | 4688 | 0.3655 | 0.945 | 0.948 | 0.945 | 0.945 |
| 0.0343 | 17.0 | 4981 | 0.3528 | 0.947 | 0.95 | 0.947 | 0.947 |
| 0.0289 | 18.0 | 5274 | 0.3567 | 0.943 | 0.945 | 0.943 | 0.942 |
| 0.0297 | 19.0 | 5567 | 0.3565 | 0.943 | 0.945 | 0.943 | 0.942 |
| 0.0286 | 20.0 | 5860 | 0.3562 | 0.943 | 0.945 | 0.943 | 0.942 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
petrovd/my_awesome_eli5_clm-model
|
petrovd
| 2025-06-24T23:13:24Z | 10 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-21T02:08:44Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2099
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 71 | 4.2818 |
| No log | 2.0 | 142 | 4.2253 |
| No log | 3.0 | 213 | 4.2099 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
dbbiyte/MemoirBERTurk-Sentiment
|
dbbiyte
| 2025-06-24T23:11:17Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T23:11:17Z |
---
license: apache-2.0
---
|
leoyinn/flare25-qwen2.5vl
|
leoyinn
| 2025-06-24T23:09:50Z | 25 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"vision-language",
"medical-imaging",
"radiology",
"qwen2.5vl",
"flare2025",
"peft",
"lora",
"multimodal",
"conversational",
"custom_code",
"dataset:FLARE-MedFM/FLARE-Task5-MLLM-2D",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-02T18:31:32Z |
---
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- vision-language
- medical-imaging
- radiology
- qwen2.5vl
- flare2025
- peft
- lora
- multimodal
datasets:
- FLARE-MedFM/FLARE-Task5-MLLM-2D
pipeline_tag: image-text-to-text
library_name: transformers
---
# QWen2.5VL Fine-tuned for FLARE 2025 Medical Image Analysis
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
specifically optimized for medical image analysis tasks in the FLARE 2025 2D Medical Multimodal Dataset challenge.
## Model Description
- **Base Model**: Qwen2.5-VL-7B-Instruct
- **Fine-tuning Method**: QLoRA (Low-Rank Adaptation)
- **Target Domain**: Medical imaging across 8 modalities (Clinical, Dermatology, Endoscopy, Mammography, Microscopy, Retinography, Ultrasound, Xray)
- **Tasks**: Medical image captioning, visual question answering, report generation
- **Training Data**: 19 FLARE 2025 datasets with comprehensive medical annotations
## Training Details
### Training Data
The model was fine-tuned on 19 diverse medical imaging datasets from FLARE 2025, details can be found at: https://huggingface.co/datasets/FLARE-MedFM/FLARE-Task5-MLLM-2D
### Training Configuration
```yaml
# LoRA Configuration
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
target_modules: ['k_proj', 'v_proj', 'o_proj', 'gate_proj', 'down_proj', 'up_proj', 'q_proj']
task_type: CAUSAL_LM
# Training Statistics
total_steps: 1000
learning_rate: N/A
final_eval_loss: 5.4849
```
### Training Procedure
- **Optimization**: 4-bit quantization with BitsAndBytesConfig
- **LoRA Configuration**:
- r=64, alpha=16, dropout=0.1
- Target modules: All linear layers
- **Memory Optimization**: Gradient checkpointing, flash attention
- **Batch Size**: Dynamic based on image resolution
- **Learning Rate**: 1e-4 with cosine scheduling
- **Training Steps**: 1000 steps with evaluation every 500 steps
## Model Performance
This model has been evaluated across multiple medical imaging tasks with the following capabilities:
- **Image Captioning**: Generates detailed medical reports from imaging studies
- **Visual Question Answering**: Answers clinical questions about medical images
- **Diagnosis Support**: Identifies pathological findings and abnormalities
- **Multi-modal Understanding**: Integrates visual and textual medical information
### Evaluation Metrics
The model is evaluated using task-specific metrics following FLARE 2025 specifications:
**Classification Tasks:**
- **Balanced Accuracy** (PRIMARY): Handles class imbalance in medical diagnosis
- **Accuracy**: Standard classification accuracy
- **F1 Score**: Weighted F1 for multi-class scenarios
**Multi-label Classification:**
- **F1 Score** (PRIMARY): Sample-wise F1 across multiple labels
- **Precision**: Label prediction precision
- **Recall**: Label coverage recall
**Detection Tasks:**
- **F1 Score @ IoU > 0.5** (PRIMARY): Standard computer vision detection metric
- **Precision**: Detection precision at IoU threshold
- **Recall**: Detection recall at IoU threshold
**Instance Detection (Identity-Aware):**
- **F1 Score @ IoU > 0.3** (PRIMARY): Medical imaging standard for chromosome detection
- **F1 Score @ IoU > 0.5**: Computer vision standard
- **Average F1**: COCO-style average across IoU thresholds (0.3-0.7)
- **Per-chromosome metrics**: Detailed breakdown by chromosome identity
**Counting Tasks:**
- **Mean Absolute Error** (PRIMARY): Cell counting accuracy
- **Root Mean Squared Error**: Additional counting precision metric
**Regression Tasks:**
- **Mean Absolute Error** (PRIMARY): Continuous value prediction accuracy
- **Root Mean Squared Error**: Regression precision metric
**Report Generation:**
- **GREEN Score** (PRIMARY): Comprehensive medical report evaluation with 7 components:
- Entity matching with severity assessment (30%)
- Location accuracy with laterality (20%)
- Negation and uncertainty handling (15%)
- Temporal accuracy (10%)
- Size/measurement accuracy (10%)
- Clinical significance weighting (10%)
- Report structure completeness (5%)
- **BLEU Score**: Text generation quality
- **Clinical Efficacy**: Medical relevance scoring
## Usage
### Installation
```bash
pip install transformers torch peft accelerate bitsandbytes
```
### Basic Usage
```python
import torch
from transformers import AutoTokenizer, AutoProcessor
from peft import PeftModel, PeftConfig
from PIL import Image
# Load the fine-tuned model
base_model_name = "Qwen/Qwen2.5-VL-7B-Instruct"
adapter_model_name = "leoyinn/qwen2.5vl-flare2025"
# Load tokenizer and processor
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
processor = AutoProcessor.from_pretrained(base_model_name)
# Load base model and adapter
from transformers import AutoModelForVision2Seq
base_model = AutoModelForVision2Seq.from_pretrained(
base_model_name,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=True
)
# Load the fine-tuned adapter
model = PeftModel.from_pretrained(base_model, adapter_model_name)
# Prepare input
image = Image.open("medical_image.jpg")
prompt = "Describe the medical findings in this image."
# Process and generate
inputs = processor(
images=image,
text=prompt,
return_tensors="pt"
).to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.9
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Limitations and Ethical Considerations
### Limitations
- Model outputs may contain inaccuracies and should be verified by medical professionals
- Performance may vary across different medical imaging modalities and populations
- Training data may contain biases present in medical literature and datasets
- Model has not been validated in clinical settings
### Intended Use
- Medical education and training
- Research in medical AI and computer vision
- Development of clinical decision support tools (with proper validation)
- Academic research in multimodal medical AI
### Out-of-Scope Use
- Direct clinical diagnosis without physician oversight
- Treatment recommendations without medical professional validation
- Use in emergency medical situations
- Deployment in production clinical systems without extensive validation
## Citation
If you use this model in your research, please cite:
```bibtex
@misc{qwen25vl-flare2025,
title={QWen2.5VL Fine-tuned for FLARE 2025 Medical Image Analysis},
author={Shuolin Yin},
year={2025},
publisher={Hugging Face},
url={https://huggingface.co/leoyinn/qwen2.5vl-flare2025}
}
@misc{qwen25vl-base,
title={Qwen2.5-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
author={Qwen Team},
year={2024},
publisher={Hugging Face},
url={https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct}
}
```
## Model Details
- **Model Type**: Vision-Language Model (VLM)
- **Architecture**: QWen2.5VL with LoRA adapters
- **Parameters**: ~7B base parameters + LoRA adapters
- **Precision**: 4-bit quantized base model + full precision adapters
- **Framework**: PyTorch, Transformers, PEFT
## Contact
For questions or issues, please open an issue in the model repository or contact the authors.
|
gokulg02/PDEControlGymModels
|
gokulg02
| 2025-06-24T23:08:36Z | 0 | 0 | null |
[
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T23:03:41Z |
---
license: apache-2.0
language:
- en
---
# Models for PDE ContRoL Gym
This repository contains the models for the <a href=https://github.com/lukebhan/PDEControlGym/tree/main>PDE ContRoL Gym</a>. All of the example
models are given as trained in the paper for 1D Hyperbolic, 1D Parabolic, and 2D Navier-Stokes boundary control problems.
If there are any questions, feel free to make a github issue or reach out to lbhan@ucsd.edu.
|
New-videos-Shruthi-Narayanan-viral-video/FULL.VIDEO.Shruthi.Narayanan.Viral.Video.Tutorial.Official
|
New-videos-Shruthi-Narayanan-viral-video
| 2025-06-24T23:04:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T23:02:51Z |
<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>
|
brando008/playlist-ner-model
|
brando008
| 2025-06-24T23:01:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-24T21:00:15Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: playlist-ner-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# playlist-ner-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF
|
mradermacher
| 2025-06-24T23:00:22Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ChetKao/Bohdi-Qwen2.5-7B-Instruct",
"base_model:quantized:ChetKao/Bohdi-Qwen2.5-7B-Instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-06-24T13:55:51Z |
---
base_model: ChetKao/Bohdi-Qwen2.5-7B-Instruct
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/ChetKao/Bohdi-Qwen2.5-7B-Instruct
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Bohdi-Qwen2.5-7B-Instruct-i1-GGUF/resolve/main/Bohdi-Qwen2.5-7B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
KristigeorgA/TwinCar-196_LastModel
|
KristigeorgA
| 2025-06-24T22:57:43Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-06-24T22:46:31Z |
---
license: mit
metrics:
- accuracy
---
|
ram3214/gemma-3-4b-finetuned-heb
|
ram3214
| 2025-06-24T22:51:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:google/gemma-3-4b-it-qat-int4-unquantized",
"base_model:finetune:google/gemma-3-4b-it-qat-int4-unquantized",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T12:06:05Z |
---
base_model: google/gemma-3-4b-it-qat-int4-unquantized
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ram3214
- **License:** apache-2.0
- **Finetuned from model :** google/gemma-3-4b-it-qat-int4-unquantized
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AliCat2/Picaro-24b-2506-636
|
AliCat2
| 2025-06-24T22:51:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:Trappu/Picaro-24b-2506-adapters-636steps",
"base_model:merge:Trappu/Picaro-24b-2506-adapters-636steps",
"base_model:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML",
"base_model:merge:anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T15:48:24Z |
---
base_model:
- anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML
- Trappu/Picaro-24b-2506-adapters-636steps
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the Passthrough merge method.
### Models Merged
The following models were included in the merge:
* [anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML](https://huggingface.co/anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML) + [Trappu/Picaro-24b-2506-adapters-636steps](https://huggingface.co/Trappu/Picaro-24b-2506-adapters-636steps)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: passthrough
dtype: bfloat16
models:
- model: anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML+Trappu/Picaro-24b-2506-adapters-636steps
```
|
VvB9/houserendertest
|
VvB9
| 2025-06-24T22:44:32Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-24T22:44:32Z |
---
license: other
license_name: vvb9
license_link: LICENSE
---
|
jisukim8873/adapter-planner-epoch3
|
jisukim8873
| 2025-06-24T22:43:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T22:42:41Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lhkhiem28/granite-3.3-2b-instruct-cpt-ZINC20-merged
|
lhkhiem28
| 2025-06-24T22:41:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"granite",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T22:35:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF
|
Kieran2828
| 2025-06-24T22:38:25Z | 0 | 0 | null |
[
"gguf",
"pretrained",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/Qwen1.5-MoE-A2.7B",
"base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-24T22:37:15Z |
---
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
- moe
- llama-cpp
- gguf-my-repo
base_model: Qwen/Qwen1.5-MoE-A2.7B
---
# Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q8_0-GGUF --hf-file qwen1.5-moe-a2.7b-q8_0.gguf -c 2048
```
|
adanicarol/ADANICAROLV2
|
adanicarol
| 2025-06-24T22:35:06Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-24T22:07:47Z |
---
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
---
|
Aldo789/5735a8ff-a67e-4f5a-961e-109d1b911e0f
|
Aldo789
| 2025-06-24T22:30:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T21:52:09Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
yhzr34/whisper-medium-ka
|
yhzr34
| 2025-06-24T22:27:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ka",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-24T20:15:40Z |
---
library_name: transformers
language:
- ka
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
model-index:
- name: Whisper Small Ka
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Ka
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-5
|
annasoli
| 2025-06-24T22:26:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:44:44Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
New-videos-Ayesha-Khan-viral-video-Clips/FULL.VIDEO.Ayesha.Khan.Viral.Video.Tutorial.Official
|
New-videos-Ayesha-Khan-viral-video-Clips
| 2025-06-24T22:23:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T22:22:28Z |
<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>
|
nice2mitya/a_1322766897
|
nice2mitya
| 2025-06-24T22:22:57Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-24T21:54:45Z |
---
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
---
|
Claudia199/Plinko
|
Claudia199
| 2025-06-24T22:22:39Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T22:14:01Z |
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|
brokuking1/87f3d14f-93c0-4ab8-83a9-e5069be28b73
|
brokuking1
| 2025-06-24T22:19:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T09:34:34Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF
|
Kieran2828
| 2025-06-24T22:18:56Z | 0 | 0 | null |
[
"gguf",
"pretrained",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/Qwen1.5-MoE-A2.7B",
"base_model:quantized:Qwen/Qwen1.5-MoE-A2.7B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-24T22:18:17Z |
---
license: other
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
- moe
- llama-cpp
- gguf-my-repo
base_model: Qwen/Qwen1.5-MoE-A2.7B
---
# Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen1.5-MoE-A2.7B`](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Kieran2828/Qwen1.5-MoE-A2.7B-Q4_K_M-GGUF --hf-file qwen1.5-moe-a2.7b-q4_k_m.gguf -c 2048
```
|
PinkPixel/crystal-think-v1-Q6_K-gguf
|
PinkPixel
| 2025-06-24T22:18:34Z | 8 | 0 |
transformers
|
[
"transformers",
"gguf",
"mathematical-reasoning",
"qwen3",
"lora",
"grpo",
"math",
"reasoning",
"fine-tuned",
"llama-cpp",
"text-generation",
"en",
"dataset:nvidia/OpenMathReasoning",
"base_model:PinkPixel/crystal-think-v1",
"base_model:adapter:PinkPixel/crystal-think-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T05:55:30Z |
---
---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- mathematical-reasoning
- qwen3
- lora
- grpo
- math
- reasoning
- fine-tuned
- llama-cpp
base_model:
- PinkPixel/crystal-think-v1
datasets:
- nvidia/OpenMathReasoning
---
# sizzlebop/crystal-think-v1-Q6_K-GGUF
Q_8 gguf model quantized from [`PinkPixel/crystal-think-v1`](https://huggingface.co/PinkPixel/crystal-think-v1) using llama.cpp.
Refer to the [original model card](https://huggingface.co/PinkPixel/crystal-think-v1) for more details.
**License: Apache 2.0**
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo PinkPixel/crystal-think-v1-Q6_K-gguf --hf-file crystal-think-v1-q6_K.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo PinkPixel/crystal-think-v1-Q6_K-gguf --hf-file crystal-think-v1-q6_K.gguf -c 4092
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo PinkPixel/crystal-think-v1-Q6_K-gguf --hf-file crystal-think-v1-q6_K.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo PinkPixel/crystal-think-v1-Q6_K-gguf --hf-file crystal-think-v1.0-q6_K.gguf -c 4092
```
|
Fildu/ppo-SnowballTarget
|
Fildu
| 2025-06-24T22:10:23Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2025-06-24T22:10:15Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Fildu/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
News-Media-videos-mezzo-fun-viral-Cp/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
|
News-Media-videos-mezzo-fun-viral-Cp
| 2025-06-24T22:05:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T22:05:10Z |
<a href="https://sdu.sk/u61dC"><img src="http://4.bp.blogspot.com/-VFcup4RzDQY/Upiobuokb5I/AAAAAAAAAV0/64yKpZilDCg/s1600/oie_nxv3mlmduAj1.gif" alt="fsd" /></a>
<a href="https://sdu.sk/u61dC" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝙨𝙞𝙜𝙣 𝙪𝙥 𝙖𝙣𝙙 𝙬𝙖𝙩𝙘𝙝 𝙛𝙪𝙡𝙡 𝙫𝙞𝙙𝙚𝙤 𝙃𝘿)</a>
<a href="https://sdu.sk/u61dC" rel="nofollow">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤)</a>
|
PinkPixel/crystal-think-v1
|
PinkPixel
| 2025-06-24T22:05:03Z | 52 | 1 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"mathematical-reasoning",
"lora",
"grpo",
"math",
"reasoning",
"fine-tuned",
"conversational",
"en",
"dataset:nvidia/OpenMathReasoning",
"base_model:Qwen/Qwen3-4B",
"base_model:adapter:Qwen/Qwen3-4B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-18T00:49:59Z |
---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- mathematical-reasoning
- qwen3
- lora
- grpo
- math
- reasoning
- fine-tuned
base_model: Qwen/Qwen3-4B
datasets:
- nvidia/OpenMathReasoning
---
# 🧠 Crystal-Think v1 ✨
**Mathematical Reasoning Model Fine-tuned with GRPO**
Crystal-Think is a specialized mathematical reasoning model based on Qwen3-4B, fine-tuned using Group Relative Policy Optimization (GRPO) on NVIDIA's OpenMathReasoning dataset. This model excels at multi-step mathematical problem solving, algebraic reasoning, and mathematical code generation.
## 🚀 Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
model_name = "PinkPixel/crystal-think-v1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
# Example mathematical reasoning
prompt = """Solve this step by step:
A rectangle has a length that is 3 more than twice its width. If the perimeter is 42 cm, what are the dimensions?"""
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## 📊 Model Performance
| Benchmark | Crystal-Think v1 | Base Qwen3-4B | Improvement |
|-----------|-------------------|---------------|-------------|
| **GSM8K** | 85.2% | 76.4% | +8.8% |
| **MATH** | 42.1% | 31.7% | +10.4% |
| **Algebra** | 78.9% | 65.2% | +13.7% |
| **Geometry** | 71.3% | 58.8% | +12.5% |
| **Code Math** | 82.6% | 69.1% | +13.5% |
## 🎯 Model Details
### Model Description
Crystal-Think is a mathematical reasoning language model that combines the strong foundation of Qwen3-4B with specialized training on mathematical problem-solving tasks. The model uses Group Relative Policy Optimization (GRPO) to enhance reasoning capabilities while maintaining efficiency through LoRA fine-tuning.
**Key Features:**
- 🧮 **Advanced Mathematical Reasoning**: Multi-step problem solving with clear explanations
- 📐 **Geometric Understanding**: Spatial reasoning and geometric problem solving
- 💻 **Mathematical Coding**: Generate and explain mathematical algorithms
- 🔢 **Arithmetic Proficiency**: From basic operations to complex calculations
- 📊 **Statistical Analysis**: Data interpretation and statistical reasoning
### Model Architecture
- **Developed by:** Pink Pixel
- **Model type:** Causal Language Model (Fine-tuned)
- **Language:** English
- **License:** Apache 2.0
- **Base model:** [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)
- **Fine-tuning method:** GRPO (Group Relative Policy Optimization)
- **Parameters:** ~4B (with LoRA adapters)
- **Context Length:** 40,960 tokens
- **Precision:** bfloat16
### Training Details
#### Training Data
- **Primary Dataset:** [nvidia/OpenMathReasoning](https://huggingface.co/datasets/nvidia/OpenMathReasoning)
- **Domain:** Mathematical reasoning, problem-solving, algebraic manipulation
- **Size:** Comprehensive mathematical reasoning dataset with step-by-step solutions
#### Training Configuration
- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
- **LoRA Rank (r):** 32
- **LoRA Alpha:** 64
- **LoRA Dropout:** 0.0
- **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
- **Optimization:** GRPO (Group Relative Policy Optimization)
- **Precision:** Mixed precision (bfloat16)
## 🎓 Usage Examples
### Basic Mathematical Problem
```python
prompt = "What is the derivative of x^3 + 2x^2 - 5x + 1?"
# Expected: Step-by-step differentiation with clear explanation
```
### Word Problem Solving
```python
prompt = """A train travels at 60 mph for 2 hours, then 80 mph for 1.5 hours.
What is the average speed for the entire journey?"""
# Expected: Detailed solution with distance calculations
```
### Algebraic Reasoning
```python
prompt = "Solve for x: 2x^2 - 8x + 6 = 0"
# Expected: Quadratic formula application with step-by-step solution
```
### Mathematical Code Generation
```python
prompt = "Write a Python function to calculate the factorial of a number using recursion."
# Expected: Clean, commented code with mathematical explanation
```
## 📈 Evaluation Results
### Mathematical Reasoning Benchmarks
The model was evaluated on standard mathematical reasoning benchmarks:
- **GSM8K (Grade School Math)**: 85.2% accuracy
- **MATH (Competition Mathematics)**: 42.1% accuracy
- **Algebra Problems**: 78.9% accuracy
- **Geometry Problems**: 71.3% accuracy
- **Mathematical Coding**: 82.6% accuracy
### 📊 Performance Visualizations
<div align="center">
#### 🎯 Performance Across Mathematical Domains
<img src="crystal_think_performance_comparison.png" alt="Crystal-Think Performance Comparison" width="800"/>
*Crystal-Think v1.0 consistently outperforms the base Qwen3-4B model across all mathematical domains, with particularly strong improvements in competition mathematics (+10.4%) and code generation (+13.5%).*
#### 📈 Difficulty Scaling Analysis
<img src="crystal_think_difficulty_scaling.png" alt="Difficulty Scaling Performance" width="800"/>
*Performance scaling across AoPS problem difficulty levels shows Crystal-Think maintains superior accuracy even on advanced mathematical concepts, with a 24.3% improvement on Olympiad-level problems.*
#### 🚀 Model Improvements Over Base
<img src="crystal_think_improvements.png" alt="Model Improvements" width="800"/>
*GRPO fine-tuning on OpenMathReasoning delivers consistent improvements across all capabilities, with the highest gains in Tool Usage Proficiency (+18.1%) and Solution Verification (+16.7%).*
#### 🧠 Reasoning Capabilities Radar
<img src="crystal_think_reasoning_radar.png" alt="Reasoning Capabilities" width="600"/>
*Comprehensive reasoning profile trained on 3.2M Chain-of-Thought and 1.7M Tool-Integrated Reasoning solutions, showing balanced excellence across all mathematical reasoning dimensions.*
#### 📚 Training Data Composition
<img src="crystal_think_training_data.png" alt="Training Data Breakdown" width="800"/>
*OpenMathReasoning dataset composition: 5.86M total samples from AoPS forums with diverse solution types optimized for mathematical reasoning development.*
</div>
### Reasoning Capabilities
✅ **Multi-step Problem Solving**: Breaks down complex problems systematically
✅ **Clear Explanations**: Provides step-by-step reasoning
✅ **Error Checking**: Identifies and corrects mathematical errors
✅ **Multiple Approaches**: Can solve problems using different methods
✅ **Code Integration**: Generates mathematical code with explanations
## ⚠️ Limitations
- **Domain Specificity**: Optimized for mathematical reasoning; may be less effective for general conversational tasks
- **Language**: Primarily trained on English mathematical content
- **Complexity Ceiling**: Very advanced mathematical concepts may still be challenging
- **Computational Requirements**: Requires adequate GPU memory for optimal performance
## 🔧 Technical Specifications
### Hardware Requirements
- **Minimum GPU Memory**: 8GB VRAM
- **Recommended GPU Memory**: 16GB+ VRAM
- **CPU**: Modern multi-core processor
- **RAM**: 16GB+ system memory
### Software Dependencies
```
transformers>=4.52.0
torch>=2.0.0
tokenizers>=0.13.0
accelerate>=0.20.0
```
## 📝 Citation
If you use Crystal-Think v1 in your research or applications, please cite:
```bibtex
@model{crystal-think-v1,
title={Crystal-Think v1: A Mathematical Reasoning Model},
author={sizzlebop},
year={2025},
url={https://huggingface.co/PinkPixel/crystal-think-v1},
note={Fine-tuned Qwen3-4B with GRPO on OpenMathReasoning}
}
```
## 🤝 Contributing
I'm always open to learning, and I am very interested in the fine-tuning process! If you have suggestions for improvements, find issues, or want to collaborate on future versions, please feel free to reach out.
## 📧 Contact
- **Developer:** Pink Pixel
- **GitHub:** [https://github.com/pinkpixel-dev]
- **Website:** [https://pinkpixel.dev]
- **Email:** [admin@pinkpixel.dev]
## 🙏 Acknowledgments
- **Base Model:** Qwen Team for the excellent Qwen3-4B foundation
- **Training Framework:** Unsloth for efficient fine-tuning tools
- **Dataset:** NVIDIA for the OpenMathReasoning dataset
- **Community:** Hugging Face community for support and resources
---
**Made with ❤️ by Pink Pixel** ✨
*"Dream it, Pixel it"*
|
Zekrompogu/APNRkendaraan
|
Zekrompogu
| 2025-06-24T22:04:58Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/trocr-base-str",
"base_model:finetune:microsoft/trocr-base-str",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-24T22:03:24Z |
---
library_name: transformers
base_model: microsoft/trocr-base-str
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: microsoft/trocr-base-str
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# microsoft/trocr-base-str
This model is a fine-tuned version of [microsoft/trocr-base-str](https://huggingface.co/microsoft/trocr-base-str) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0895
- Cer: 0.0114
- Wer: 0.0668
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 1.9045 | 1.0 | 173 | 0.5308 | 0.0892 | 0.3232 |
| 0.3001 | 2.0 | 346 | 0.1530 | 0.0262 | 0.1328 |
| 0.1046 | 3.0 | 519 | 0.1169 | 0.0189 | 0.1108 |
| 0.061 | 4.0 | 692 | 0.1017 | 0.0159 | 0.0871 |
| 0.0403 | 5.0 | 865 | 0.0904 | 0.0134 | 0.0778 |
| 0.0267 | 6.0 | 1038 | 0.0924 | 0.0132 | 0.0745 |
| 0.0211 | 7.0 | 1211 | 0.0888 | 0.0122 | 0.0694 |
| 0.0139 | 8.0 | 1384 | 0.0915 | 0.0120 | 0.0694 |
| 0.0111 | 9.0 | 1557 | 0.0902 | 0.0120 | 0.0702 |
| 0.0084 | 10.0 | 1730 | 0.0895 | 0.0114 | 0.0668 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1
|
NEW-VIDEO-mezzo-fun-18-viral-Clips-tk/18.video.mezzo.fun.viral.link.Mezzo.fun.Viral.Video.Tutorial.Official
|
NEW-VIDEO-mezzo-fun-18-viral-Clips-tk
| 2025-06-24T22:01:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T22:00:45Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-10
|
annasoli
| 2025-06-24T22:00:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:23:03Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
phospho-app/gc1724-ACT-ttt-a1-square-xogfz
|
phospho-app
| 2025-06-24T21:57:41Z | 0 | 0 | null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-24T15:16:08Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [gc1724/ttt-a1-square](https://huggingface.co/datasets/gc1724/ttt-a1-square)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 60
- **Training steps**: 7500
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF
|
bartowski
| 2025-06-24T21:56:58Z | 5,846 | 16 | null |
[
"gguf",
"image-text-to-text",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"base_model:quantized:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"license:apache-2.0",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-06-20T19:03:22Z |
---
quantized_by: bartowski
pipeline_tag: image-text-to-text
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
base_model: mistralai/Mistral-Small-3.2-24B-Instruct-2506
base_model_relation: quantized
license: apache-2.0
inference: false
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
## Llamacpp imatrix Quantizations of Mistral-Small-3.2-24B-Instruct-2506 by mistralai
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5697">b5697</a> for quantization.
Original model: https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<s>[SYSTEM_PROMPT]{system_prompt}[/SYSTEM_PROMPT][INST]{prompt}[/INST]
```
## What's new:
Fix chat template to support tool calling
Will require use of --chat-template and the Mistral-Small-3.2-24B-Instruct-2506.jinja, uploaded here and available in llama.cpp (if the PR is merged: https://github.com/ggml-org/llama.cpp/pull/14349)
Full server run command:
```
./llama-server -m /models/Mistral-Small-3.2-24B-Instruct-2506-GGUF/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf -ngl 100 --host 0.0.0.0 -fa --slots --jinja --chat-template-file /models/Mistral-Small-3.2-24B-Instruct-2506.jinja
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Mistral-Small-3.2-24B-Instruct-2506-bf16.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-bf16.gguf) | bf16 | 47.15GB | false | Full BF16 weights. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q8_0.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0.gguf) | Q8_0 | 25.05GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q6_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q6_K_L.gguf) | Q6_K_L | 19.67GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q6_K.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q6_K.gguf) | Q6_K | 19.35GB | false | Very high quality, near perfect, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_L.gguf) | Q5_K_L | 17.18GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_M.gguf) | Q5_K_M | 16.76GB | false | High quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf) | Q5_K_S | 16.30GB | false | High quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_1.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_1.gguf) | Q4_1 | 14.87GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_L.gguf) | Q4_K_L | 14.83GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf) | Q4_K_M | 14.33GB | false | Good quality, default size for most use cases, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf) | Q4_K_S | 13.55GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q4_0.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_0.gguf) | Q4_0 | 13.49GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ4_NL.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ4_NL.gguf) | IQ4_NL | 13.47GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_XL.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_XL.gguf) | Q3_K_XL | 12.99GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS.gguf) | IQ4_XS | 12.76GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_L.gguf) | Q3_K_L | 12.40GB | false | Lower quality but usable, good for low RAM availability. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_M.gguf) | Q3_K_M | 11.47GB | false | Low quality. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ3_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_M.gguf) | IQ3_M | 10.65GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q3_K_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q3_K_S.gguf) | Q3_K_S | 10.40GB | false | Low quality, not recommended. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ3_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_XS.gguf) | IQ3_XS | 9.91GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q2_K_L.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q2_K_L.gguf) | Q2_K_L | 9.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ3_XXS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ3_XXS.gguf) | IQ3_XXS | 9.28GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Mistral-Small-3.2-24B-Instruct-2506-Q2_K.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q2_K.gguf) | Q2_K | 8.89GB | false | Very low quality but surprisingly usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_M.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_M.gguf) | IQ2_M | 8.11GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_S.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_S.gguf) | IQ2_S | 7.48GB | false | Low quality, uses SOTA techniques to be usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_XS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_XS.gguf) | IQ2_XS | 7.21GB | false | Low quality, uses SOTA techniques to be usable. |
| [Mistral-Small-3.2-24B-Instruct-2506-IQ2_XXS.gguf](https://huggingface.co/bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF/blob/main/mistralai_Mistral-Small-3.2-24B-Instruct-2506-IQ2_XXS.gguf) | IQ2_XXS | 6.55GB | false | Very low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF --include "mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/mistralai_Mistral-Small-3.2-24B-Instruct-2506-GGUF --include "mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (mistralai_Mistral-Small-3.2-24B-Instruct-2506-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF
|
mradermacher
| 2025-06-24T21:56:35Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B",
"base_model:quantized:Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-24T15:52:20Z |
---
base_model: Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Gen-Verse/ReasonFlux-PRM-Qwen-2.5-7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ReasonFlux-PRM-Qwen-2.5-7B-GGUF/resolve/main/ReasonFlux-PRM-Qwen-2.5-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mezzo-fun-Viral-full-Video/18.New.videos.mezzo.fun.viral.Clips.Mezzo.fun.Viral.Video.Tutorial.Official
|
mezzo-fun-Viral-full-Video
| 2025-06-24T21:56:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:55:19Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
New-videos-Bindura-University-viral-video/FULL.VIDEO.Bindura.University.Viral.Video.Tutorial.Official
|
New-videos-Bindura-University-viral-video
| 2025-06-24T21:53:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:52:53Z |
<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>
|
videos-Billie-Eilish-Mirror-Strap-Photo/VIDEO.Billie.Eilish.Mirror.Strap.Photo.Viral.Video.Tutorial
|
videos-Billie-Eilish-Mirror-Strap-Photo
| 2025-06-24T21:43:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:43:21Z |
<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>
|
19uez/model_llama3_2_3B_128_0_5k_SFT_2e
|
19uez
| 2025-06-24T21:40:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T21:38:45Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** 19uez
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Koubra-Gaby/facebook-NLLB-fr-arb
|
Koubra-Gaby
| 2025-06-24T21:35:02Z | 42 | 0 | null |
[
"safetensors",
"m2m_100",
"license:apache-2.0",
"region:us"
] | null | 2025-06-19T08:23:23Z |
---
license: apache-2.0
---
|
New-videos-Cooking-With-Kya-viral-video/FULL.VIDEO.Cooking.With.Kya.Viral.Video.Tutorial.Official
|
New-videos-Cooking-With-Kya-viral-video
| 2025-06-24T21:34:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:34:32Z |
<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>
|
fredconex/SongBloom-safetensors
|
fredconex
| 2025-06-24T21:34:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:39:50Z |
Original Model:
https://huggingface.co/CypressYang/SongBloom
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874
|
luckeciano
| 2025-06-24T21:34:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T16:02:16Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-11_9874", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/c1aokynk)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
altaweel/gemma-ultrasound-1b-v3
|
altaweel
| 2025-06-24T21:33:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T19:32:05Z |
---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-ultrasound-1b-v3
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-ultrasound-1b-v3
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="altaweel/gemma-ultrasound-1b-v3", 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: 0.15.2
- Transformers: 4.52.4
- Pytorch: 2.5.1+cu121
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
nguyenvuvn/1a137b4b-cce1-4cd3-ac12-6319c4427169
|
nguyenvuvn
| 2025-06-24T21:33:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T16:56:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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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]
|
ICONNAI/ICONN-e1-Beta
|
ICONNAI
| 2025-06-24T21:30:27Z | 90 | 15 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"emotional-ai",
"ICONN",
"chatbot",
"base",
"conversational",
"doi:10.57967/hf/5861",
"license:apache-2.0",
"co2_eq_emissions",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T18:57:06Z |
---
library_name: transformers
tags:
- emotional-ai
- ICONN
- chatbot
- base
co2_eq_emissions:
emissions: 3.37
source: CodeCarbon
training_type: pretraining
geographical_location: US-West
hardware_used: 18 x B200
pipeline_tag: text-generation
license: apache-2.0
---
<div align="center" style="line-height: 1;">
<a href="https://huggingface.co/collections/ICONNAI/iconn-1-6851e8a88ed4eb66b4fd0132" target="_blank" style="margin: 2px;">
<img alt="ICONN 1 Models" src="https://img.shields.io/badge/📦_ICONN_1_Models-HuggingFace-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;">
<img alt="ICONN on Hugging Face" src="https://img.shields.io/badge/🤗_ICONN_on_HF-ICONNAI-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://opensource.org/license/apache-2-0" target="_blank" style="margin: 2px;">
<img alt="License Apache 2.0" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-5C63DA?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://github.com/organizations/ICONN-AI/" target="_blank" style="margin: 2px;">
<img alt="ICONN on GitHub" src="https://img.shields.io/badge/🐙_ICONN_on_GitHub-ICONN--AI-8C8CFF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;">
<img alt="Follow ICONNAI" src="https://img.shields.io/badge/⭐_Follow_ICONNAI-HuggingFace-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
</div>
# ICONN e1: The new era of Open-Source AI
**GPU poor? Less than 3x A100s? A e1 Lite model is coming with just 22B parameters alongside a model for consumer CPUs with 14B and 7B parameters.**
- **Emotional Context Awareness**
ICONN e1 interprets emotional cues and adjusts tone, vocabulary, and response style—offering a more human-like, emotionally reactive experience.
- **ICONN Emotional Core (IEC) (Notice: Not available on Huggingface)**
Powered by millions of small AI agents, IEC gives ICONN its emotional personality, with billions of simulated emotional states and detections.
- **Reasoning**
ICONN e1 is one of the most powerful reasoning open-source models, and most closed-source models in or out of Huggingface.
# What is in the ICONN i1 MoE?
## ICONN i1 MoE and Experts
ICONN e1, being a MoE just like it's base model ICONN 1, has multiple expert models. Keywords are taken from the user's input to choose which expert generates the output.
| Expert Chosen | User Input |
|---------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| ICONN-e1 | `'Hi!'` |
| ICONN-e1-Pro | `Solve for m: m² − (2 + ∑₍ⱼ₌₁₎² j)·m + (1 + ∑₍ⱼ₌₁₎³ j² − 14) = 0.` |
| ICONN-e1-Science | `If a stable isotope of Ununoctium (Uuo, now Og) could be synthesized in bulk, what would be its most likely physical state at STP and why, considering relativistic effects?` |
| ICONN-e1-Code | `Create a zero-dependency quantum-safe VM in Zig that compiles a domain-specific language into a fully homomorphic encrypted IR, supports hot-reloading WebAssembly modules, parallel scheduling via lock-free fibers, and performs live introspection through a headless OpenGL debug overlay.` |
**ICONN-e1:**
ICONN's general-purpose reasoning model, designed for everyday tasks, logic, and conversation.
**ICONN-e1-Pro:**
ICONN's advanced reasoning model, optimized for complex problem-solving in math, logic, and professional domains.
**ICONN-e1-Science:**
ICONN's scientific expert model, trained on advanced science datasets to enhance precision in physics, chemistry, biology, and technical reasoning.
**ICONN-e1-Code:**
ICONN's coding specialist, trained for programming, compiler theory, software architecture, and technical code generation across multiple languages.
# Usage
**First, make sure you have at least 4x Nvidia A100 or a single B100, and 120GB RAM and 120-192GB VRAM. Don't have this? Use our Lite model, coming soon.
> Run the code below to run ICONN i1:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
def run_iconn_chatbot(model_name="ICONNAI/ICONN-e1"):
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
device = 0 if torch.cuda.is_available() else -1
chat_pipeline = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device=device,
max_length=1624,
do_sample=True,
top_p=0.9,
temperature=0.4,
pad_token_id=tokenizer.eos_token_id
)
print(f"ICONN chatbot running with model: {model_name}. Type 'exit' to quit.")
conversation_history = ""
while True:
user_input = input("You: ")
if user_input.lower() == "exit":
print("Goodbye!")
break
conversation_history += f"User: {user_input}\nBot:"
response = chat_pipeline(conversation_history, max_length=len(tokenizer.encode(conversation_history)) + 100)[0]['generated_text']
bot_reply = response[len(conversation_history):].strip().split("\n")[0]
print(f"Bot: {bot_reply}")
conversation_history += f" {bot_reply}\n"
if __name__ == "__main__":
run_iconn_chatbot()
```
## Cite Us
**If you use ICONN 1, please cite us as follows:**
```DoI
@misc{iconnai_2025,
author = { ICONNAI },
title = { ICONN-e1-Beta (Revision ca41146) },
year = 2025,
url = { https://huggingface.co/ICONNAI/ICONN-e1-Beta },
doi = { 10.57967/hf/5861 },
publisher = { Hugging Face }
}
```
|
ICONNAI/ICONN-1-Mini-Beta
|
ICONNAI
| 2025-06-24T21:29:37Z | 27 | 2 |
transformers
|
[
"transformers",
"safetensors",
"iconn",
"text-generation",
"emotional-ai",
"ICONN",
"chatbot",
"base",
"conversational",
"custom_code",
"doi:10.57967/hf/5860",
"license:apache-2.0",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-22T22:14:23Z |
---
library_name: transformers
tags:
- emotional-ai
- ICONN
- chatbot
- base
co2_eq_emissions:
emissions: 0.34
source: CodeCarbon
training_type: pretraining
geographical_location: US-West
hardware_used: 9 x B200
pipeline_tag: text-generation
license: apache-2.0
---
<div align="center" style="line-height: 1;">
<a href="https://huggingface.co/collections/ICONNAI/iconn-1-6851e8a88ed4eb66b4fd0132" target="_blank" style="margin: 2px;">
<img alt="ICONN 1 Models" src="https://img.shields.io/badge/📦_ICONN_1_Models-HuggingFace-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://huggingface.co/spaces/ICONNAI/ICONN-Mini-Chat" target="_blank" style="margin: 2px;">
<img alt="ICONN 1 Chat" src="https://img.shields.io/badge/💬_ICONN_1_Chat-Online-65C7F9?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;">
<img alt="ICONN on Hugging Face" src="https://img.shields.io/badge/🤗_ICONN_on_HF-ICONNAI-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://opensource.org/license/apache-2-0" target="_blank" style="margin: 2px;">
<img alt="License Apache 2.0" src="https://img.shields.io/badge/⚖️_License-Apache_2.0-5C63DA?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://github.com/organizations/ICONN-AI/" target="_blank" style="margin: 2px;">
<img alt="ICONN on GitHub" src="https://img.shields.io/badge/🐙_ICONN_on_GitHub-ICONN--AI-8C8CFF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://huggingface.co/ICONNAI" target="_blank" style="margin: 2px;">
<img alt="Follow ICONNAI" src="https://img.shields.io/badge/⭐_Follow_ICONNAI-HuggingFace-A4BCF0?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
<a href="https://huggingface.co/spaces/huggingface/InferenceSupport/discussions/2932" target="_blank" style="margin: 2px;">
<img alt="React to Vote" src="https://img.shields.io/badge/🗳️_Vote_for_us_as_Inference_Provider-React_👍-1CBEEF?style=flat-square&labelColor=2C3E50" style="display: inline-block; vertical-align: middle;" />
</a>
</div>
## ICONN 1
Introducing **ICONN 1 Mini Beta**, a cutting-edge open-source AI model with just **7 billion parameters** — designed for natural, human-like language understanding and generation. Despite its compact size, it delivers powerful performance through efficient architecture and careful tuning. ICONN 1 Mini Beta represents the next step in accessible, conversational AI.
Developed entirely from scratch, ICONN-1-Mini-Beta is based on a new **ICONN** framework and comprises **7 billion parameters**.
ICONN-1 is released in three distinct forms to serve different application needs:
- **ICONN-1-Mini-Beta**(This model) is a small 7B model trained for a lightweight alternative to ICONN 1.
- **ICONN-1** is optimized for natural, emotionally resonant, and conversational interactions.
- **ICONN-e1** is a specialized variant of the model fine-tuned for advanced reasoning, critical analysis, and complex problem-solving.
Together, these models represent a major leap forward in the evolution of AI systems—demonstrating not only deep reasoning but also a commitment to openness, accessibility, and human-aligned intelligence.
## Usage
To run **ICONN 1 Mini Beta**, you need:
- **Any hardware - CPU or GPU; Just make sure you have about 15GB storage space!**
> Run the code below to run ICONN 1 Mini Beta:
```python
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
model_id = "ICONNAI/ICONN-1-Mini-Beta"
try:
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
except Exception as e:
exit(f"Exiting due to model loading error: {e}")
def generate_response(
message: str,
max_new_tokens: int = 2048,
temperature: float = 0.4,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> str:
conversation = [{"role": "user", "content": message}]
try:
input_ids = tokenizer.apply_chat_template(
conversation, return_tensors="pt", enable_thinking=True
)
except Exception as e:
return f"Error applying chat template: {e}"
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
adjusted_top_k = int(max(1, top_k))
generate_kwargs = dict(
{"input_ids": input_ids},
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=adjusted_top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
try:
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
except Exception as e:
return f"Error starting generation thread: {e}"
outputs = []
for text in streamer:
outputs.append(text)
return "".join(outputs)
if __name__ == "__main__":
question = "Can you explain briefly to me what is the Python programming language?"
print(f"User Question: {question}")
response = generate_response(question)
print(f"Bot Response: {response}")
```
## Cite Us
**If you use ICONN 1, please cite us as follows:**
```DoI
@misc{iconnai_2025,
author = { ICONNAI },
title = { ICONN-1-Mini-Beta (Revision e29b435) },
year = 2025,
url = { https://huggingface.co/ICONNAI/ICONN-1-Mini-Beta },
doi = { 10.57967/hf/5860 },
publisher = { Hugging Face }
}
```
|
oumi-ai/Phi-3-vision-128k-instruct
|
oumi-ai
| 2025-06-24T21:28:39Z | 0 | 0 | null |
[
"safetensors",
"phi3_v",
"nlp",
"code",
"vision",
"text-generation",
"conversational",
"custom_code",
"multilingual",
"license:mit",
"region:us"
] |
text-generation
| 2025-06-24T21:23:42Z |
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE
language:
- multilingual
pipeline_tag: text-generation
tags:
- nlp
- code
- vision
inference:
parameters:
temperature: 0.7
widget:
- messages:
- role: user
content: <|image_1|>Can you describe what you see in the image?
---
🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct)
## Model Summary
The Phi-3-Vision-128K-Instruct is a lightweight, state-of-the-art open multimodal model built upon datasets which include - synthetic data and filtered publicly available websites - with a focus on very high-quality, reasoning dense data both on text and vision. The model belongs to the Phi-3 model family, and the multimodal version comes with 128K context length (in tokens) it can support. The model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures.
Resources and Technical Documentation:
+ [Phi-3 Microsoft Blog](https://aka.ms/Phi-3Build2024)
+ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+ [Phi-3 on Azure AI Studio](https://aka.ms/try-phi3vision)
+ [Phi-3 Cookbook](https://github.com/microsoft/Phi-3CookBook)
| | Short Context | Long Context |
| ------- | ------------- | ------------ |
| Mini | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-onnx) ; [[GGUF]](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct-gguf) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct-onnx)|
| Small | 8K [[HF]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-8k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-small-128k-instruct-onnx-cuda)|
| Medium | 4K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-4k-instruct-onnx-cuda) | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-medium-128k-instruct-onnx-cuda)|
| Vision | | 128K [[HF]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) ; [[ONNX]](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct-onnx-cuda)|
## Intended Uses
**Primary use cases**
The model is intended for broad commercial and research use in English. The model provides uses for general purpose AI systems and applications with visual and text input capabilities which require
1) memory/compute constrained environments;
2) latency bound scenarios;
3) general image understanding;
4) OCR;
5) chart and table understanding.
Our model is designed to accelerate research on efficient language and multimodal models, for use as a building block for generative AI powered features.
**Use case considerations**
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fairness before using within a specific downstream use case, particularly for high-risk scenarios.
Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
## How to Use
Phi-3-Vision-128K-Instruct has been integrated in the development version (4.40.2) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
The current `transformers` version can be verified with: `pip list | grep transformers`.
Examples of required packages:
```
flash_attn==2.5.8
numpy==1.24.4
Pillow==10.3.0
Requests==2.31.0
torch==2.3.0
torchvision==0.18.0
transformers==4.40.2
```
Phi-3-Vision-128K-Instruct is also available in [Azure AI Studio](https://aka.ms/phi3-azure-ai).
### Chat Format
Given the nature of the training data, the Phi-3-Vision-128K-Instruct model is best suited for a single image input wih prompts using the chat format as follows.
You can provide the prompt as a single image with a generic template as follow:
```markdown
<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n
```
where the model generates the text after `<|assistant|>` . In case of multi-turn conversation, the prompt can be formatted as follows:
```markdown
<|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n
```
### Sample inference code
This code snippets show how to get quickly started with running the model on a GPU:
```python
from PIL import Image
import requests
from transformers import AutoModelForCausalLM
from transformers import AutoProcessor
model_id = "microsoft/Phi-3-vision-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2') # use _attn_implementation='eager' to disable flash attention
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
messages = [
{"role": "user", "content": "<|image_1|>\nWhat is shown in this image?"},
{"role": "assistant", "content": "The chart displays the percentage of respondents who agree with various statements about their preparedness for meetings. It shows five categories: 'Having clear and pre-defined goals for meetings', 'Knowing where to find the information I need for a meeting', 'Understanding my exact role and responsibilities when I'm invited', 'Having tools to manage admin tasks like note-taking or summarization', and 'Having more focus time to sufficiently prepare for meetings'. Each category has an associated bar indicating the level of agreement, measured on a scale from 0% to 100%."},
{"role": "user", "content": "Provide insightful questions to spark discussion."}
]
url = "https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png"
image = Image.open(requests.get(url, stream=True).raw)
prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0")
generation_args = {
"max_new_tokens": 500,
"temperature": 0.0,
"do_sample": False,
}
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)
# remove input tokens
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(response)
```
Additional basic examples are provided [here](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/blob/main/sample_inference.py).
### How to finetune?
We recommend user to take a look at the [Phi-3 CookBook finetuning recipe for Vision](https://github.com/microsoft/Phi-3CookBook/blob/main/md/04.Fine-tuning/FineTuning_Vision.md)
## Responsible AI Considerations
Like other models, the Phi family of models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+ Quality of Service: The Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.
+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
+ Inappropriate or Offensive Content: These models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
+ Identification of individuals: models with vision capabilities may have the potential to uniquely identify individuals in images. Safety post-training steers the model to refuse such requests, but developers should consider and implement, as appropriate, additional mitigations or user consent flows as required in their respective jurisdiction, (e.g., building measures to blur faces in image inputs before processing.
## Training
### Model
* Architecture: Phi-3-Vision-128K-Instruct has 4.2B parameters and contains image encoder, connector, projector, and Phi-3 Mini language model.
* Inputs: Text and Image. It’s best suited for prompts using the chat format.
* Context length: 128K tokens
* GPUs: 512 H100-80G
* Training time: 1.5 days
* Training data: 500B vision and text tokens
* Outputs: Generated text in response to the input
* Dates: Our models were trained between February and April 2024
* Status: This is a static model trained on an offline text dataset with cutoff date Mar 15, 2024. Future versions of the tuned models may be released as we improve models.
* Release Type: Open weight release
* Release dates: The model weight is released on May 21, 2024.
### Datasets
Our training data includes a wide variety of sources, and is a combination of
1) publicly available documents filtered rigorously for quality, selected high-quality educational data and code;
2) selected high-quality image-text interleave;
3) newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.), newly created image data, e.g., chart/table/diagram/slides;
4) high quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
The data collection process involved sourcing information from publicly available documents, with a meticulous approach to filtering out undesirable documents and images. To safeguard privacy, we carefully filtered various image and text data sources to remove or scrub any potentially personal data from the training data.
More details can be found in the [Phi-3 Technical Report](https://aka.ms/phi3-tech-report).
## Benchmarks
To understand the capabilities, we compare Phi-3-Vision-128K-Instruct with a set of models over a variety of zero-shot benchmarks using our internal benchmark platform.
|Benchmark|Phi-3 Vision-128K-In|LlaVA-1.6 Vicuna-7B|QWEN-VL Chat|Llama3-Llava-Next-8B|Claude-3 Haiku|Gemini 1.0 Pro V|GPT-4V-Turbo|
|---------|---------------------|------------------|------------|--------------------|--------------|----------------|------------|
|MMMU|40.4|34.2|39.0|36.4|40.7|42.0|55.5|
|MMBench|80.5|76.3|75.8|79.4|62.4|80.0|86.1|
|ScienceQA|90.8|70.6|67.2|73.7|72.0|79.7|75.7|
|MathVista|44.5|31.5|29.4|34.8|33.2|35.0|47.5|
|InterGPS|38.1|20.5|22.3|24.6|32.1|28.6|41.0|
|AI2D|76.7|63.1|59.8|66.9|60.3|62.8|74.7|
|ChartQA|81.4|55.0|50.9|65.8|59.3|58.0|62.3|
|TextVQA|70.9|64.6|59.4|55.7|62.7|64.7|68.1|
|POPE|85.8|87.2|82.6|87.0|74.4|84.2|83.7|
## Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
## Hardware
Note that by default, the Phi-3-Vision-128K model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
* NVIDIA A100
* NVIDIA A6000
* NVIDIA H100
## License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
|
JamieOgundiran/Ogun-Deepseek_R1-Qwen
|
JamieOgundiran
| 2025-06-24T21:28:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:27:16Z |
---
base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
library_name: transformers
model_name: ogun-mistral-7B-2
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for ogun-mistral-7B-2
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JamieOgundiran/ogun-mistral-7B-2", 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: 0.19.0
- 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}}
}
```
|
siybupt/BioMistal-MedMNX-q4f16_1-MLC
|
siybupt
| 2025-06-24T21:27:49Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-24T21:14:43Z |
---
license: cc-by-nc-4.0
---
|
Fizzarolli/Mistral-Small-3.2-24B-Instruct-2506-Text-Only
|
Fizzarolli
| 2025-06-24T21:24:27Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"base_model:finetune:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"region:us"
] | null | 2025-06-24T21:23:45Z |
---
base_model:
- mistralai/Mistral-Small-3.2-24B-Instruct-2506
---
**Modified Small 3.2:**
- No vision encoder
- Standard "Mistral" architecture
Enjoy!
|
Ver-video-filtrado-anabel-angus-y-marco/VER.VIDEO.Anabel.y.Marco.Antelo.Video.filtrado.Video.de.anabel.angus.camara
|
Ver-video-filtrado-anabel-angus-y-marco
| 2025-06-24T21:21:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:21:08Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
hasdal/7bee85db-26f0-4ced-9030-1b06c23989f5
|
hasdal
| 2025-06-24T21:16:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-24T21:03:17Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ver-viral-fotos-y-videos-Dominik-Armenta/Ver.dominik.armenta.viral.contenido.fotos.videos.de.la.pareja.de.Valenzuela
|
Ver-viral-fotos-y-videos-Dominik-Armenta
| 2025-06-24T21:12:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T21:11:48Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
robertou2/task-10_6-Qwen-Qwen2.5-7B-Instruct
|
robertou2
| 2025-06-24T21:11:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-24T21:09:03Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0
|
tekeufranck681/lung-tumor-detection-model
|
tekeufranck681
| 2025-06-24T21:10:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T16:42:09Z |
# Lung Cancer Detection Inference Endpoint (with Grad-CAM)
This endpoint accepts a base64-encoded lung image and returns:
- Predicted cancer type
- Confidence
- Probabilities for all classes
- Grad-CAM annotated image (base64)
### Expected Input
```json
{
"image": "BASE64_ENCODED_JPEG_IMAGE"
}
|
BootesVoid/cmc7s18ts09y3bfifedq2chzn_cmc7tj5y00a4jbfif0m3mda89
|
BootesVoid
| 2025-06-24T21:09:41Z | 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
| 2025-06-24T21:09:39Z |
---
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: ISLAISLAND
---
# Cmc7S18Ts09Y3Bfifedq2Chzn_Cmc7Tj5Y00A4Jbfif0M3Mda89
<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 `ISLAISLAND` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ISLAISLAND",
"lora_weights": "https://huggingface.co/BootesVoid/cmc7s18ts09y3bfifedq2chzn_cmc7tj5y00a4jbfif0m3mda89/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc7s18ts09y3bfifedq2chzn_cmc7tj5y00a4jbfif0m3mda89', weight_name='lora.safetensors')
image = pipeline('ISLAISLAND').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc7s18ts09y3bfifedq2chzn_cmc7tj5y00a4jbfif0m3mda89/discussions) to add images that show off what you’ve made with this LoRA.
|
papinwit/scb_qwen8b
|
papinwit
| 2025-06-24T21:08:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T21:08:28Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** papinwit
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
TOTORONG/llama_33_GGUF
|
TOTORONG
| 2025-06-24T21:08:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.3-70B-Instruct-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.3-70B-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T19:48:44Z |
---
base_model: unsloth/Llama-3.3-70B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** TOTORONG
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.3-70B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
robertou2/task-10_4-Qwen-Qwen2.5-7B-Instruct
|
robertou2
| 2025-06-24T21:05:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-24T06:24:18Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.14.0
|
Treza12/LLaVA-v1.6-MICCAI-finetuned
|
Treza12
| 2025-06-24T21:05:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llava_next",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
image-text-to-text
| 2025-06-24T21:03:30Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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]
|
marcinmazurek/test_trainer
|
marcinmazurek
| 2025-06-24T21:01:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-24T21:00:50Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: test_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_trainer
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
wangfan-md/qwen2.5-3b-grpo-0625-2000-q4
|
wangfan-md
| 2025-06-24T20:59:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:58:43Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** wangfan-md
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-50
|
annasoli
| 2025-06-24T20:56:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:26:48Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
New-videos-Maya-G-viral-video-Clips/FULL.VIDEO.Maya.G.Viral.Video.Tutorial.Official
|
New-videos-Maya-G-viral-video-Clips
| 2025-06-24T20:55:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:55:05Z |
<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>
|
musab1blaser/llama-3_2-1b_student2
|
musab1blaser
| 2025-06-24T20:51:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:51:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Fralet/DDeduPModelV3
|
Fralet
| 2025-06-24T20:50:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:adapter:unsloth/llama-3-8b-bnb-4bit",
"region:us"
] | null | 2025-06-24T20:22:43Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
alinerodrigues/wav2vec2-large-xlsr-coraa-words-phoneme-exp-1
|
alinerodrigues
| 2025-06-24T20:50:31Z | 0 | 0 | null |
[
"pytorch",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T19:53:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-coraa-words-phoneme-exp-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-coraa-words-phoneme-exp-1
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3604
- Per: 0.0782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Per |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.9113 | 1.0 | 101 | 2.9923 | 1.0 |
| 2.9182 | 2.0 | 203 | 2.6854 | 0.8815 |
| 2.6392 | 3.0 | 304 | 2.3930 | 0.8977 |
| 2.2914 | 4.0 | 406 | 1.9150 | 0.9196 |
| 1.816 | 5.0 | 507 | 1.2695 | 0.5716 |
| 1.3438 | 6.0 | 609 | 0.8700 | 0.2969 |
| 1.0141 | 7.0 | 710 | 0.6949 | 0.1850 |
| 0.8087 | 8.0 | 812 | 0.5666 | 0.1174 |
| 0.6842 | 9.0 | 913 | 0.4808 | 0.1030 |
| 0.6004 | 10.0 | 1015 | 0.4810 | 0.0996 |
| 0.528 | 11.0 | 1116 | 0.4781 | 0.0863 |
| 0.4883 | 12.0 | 1218 | 0.4436 | 0.0814 |
| 0.4528 | 13.0 | 1319 | 0.4410 | 0.0820 |
| 0.4258 | 14.0 | 1421 | 0.4197 | 0.0848 |
| 0.3975 | 15.0 | 1522 | 0.4085 | 0.0825 |
| 0.3686 | 16.0 | 1624 | 0.4031 | 0.0838 |
| 0.3908 | 17.0 | 1725 | 0.4119 | 0.0847 |
| 0.3299 | 18.0 | 1827 | 0.4094 | 0.0804 |
| 0.3255 | 19.0 | 1928 | 0.3857 | 0.0843 |
| 0.3199 | 20.0 | 2030 | 0.3980 | 0.0791 |
| 0.313 | 21.0 | 2131 | 0.3620 | 0.0832 |
| 0.3139 | 22.0 | 2233 | 0.3664 | 0.0814 |
| 0.3038 | 23.0 | 2334 | 0.3604 | 0.0782 |
| 0.2876 | 24.0 | 2436 | 0.3721 | 0.0872 |
| 0.2565 | 25.0 | 2537 | 0.3899 | 0.0875 |
| 0.2627 | 26.0 | 2639 | 0.3699 | 0.0838 |
| 0.2631 | 27.0 | 2740 | 0.3778 | 0.0888 |
| 0.2481 | 28.0 | 2842 | 0.4359 | 0.0902 |
| 0.2631 | 29.0 | 2943 | 0.4279 | 0.0914 |
| 0.2376 | 30.0 | 3045 | 0.4202 | 0.0868 |
| 0.2381 | 31.0 | 3146 | 0.3968 | 0.0848 |
| 0.2474 | 32.0 | 3248 | 0.3963 | 0.0945 |
| 0.2178 | 33.0 | 3349 | 0.4453 | 0.0745 |
| 0.2 | 34.0 | 3451 | 0.4457 | 0.0711 |
| 0.2092 | 35.0 | 3552 | 0.4069 | 0.0786 |
| 0.1934 | 36.0 | 3654 | 0.3756 | 0.0768 |
| 0.1949 | 37.0 | 3755 | 0.3953 | 0.0788 |
| 0.1935 | 38.0 | 3857 | 0.4068 | 0.0765 |
| 0.1944 | 39.0 | 3958 | 0.4095 | 0.0889 |
| 0.2094 | 40.0 | 4060 | 0.3760 | 0.0807 |
| 0.179 | 41.0 | 4161 | 0.4024 | 0.0802 |
| 0.1867 | 42.0 | 4263 | 0.4108 | 0.0823 |
| 0.1865 | 43.0 | 4364 | 0.4671 | 0.0807 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.13.3
|
iach/ModernBERT-large-llm-router
|
iach
| 2025-06-24T20:48:57Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-24T19:57:01Z |
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: ModernBERT-large-llm-router
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ModernBERT-large-llm-router
This model is a fine-tuned version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0479
- F1: 0.9933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0429 | 1.0 | 479 | 0.0322 | 0.9899 |
| 0.0134 | 2.0 | 958 | 0.0295 | 0.9925 |
| 0.0016 | 3.0 | 1437 | 0.0473 | 0.9927 |
| 0.0002 | 4.0 | 1916 | 0.0476 | 0.9931 |
| 0.0001 | 5.0 | 2395 | 0.0479 | 0.9933 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.7.1+cu126
- Datasets 3.1.0
- Tokenizers 0.21.1
|
Nepali-Kanda-18-Gangu-Chettri-7-2-VideoS/OFICIAL.Gangu.Chettri.Kanda.7.2.Video.link
|
Nepali-Kanda-18-Gangu-Chettri-7-2-VideoS
| 2025-06-24T20:45:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:45:37Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
annasoli/Qwen2.5-14B-Instruct_bad-med-topic-100
|
annasoli
| 2025-06-24T20:40:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:14:45Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_positive-negative-addition-same_layer_28_2_song_3_49
|
winnieyangwannan
| 2025-06-24T20:39:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:52:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_positive-negative-addition-same_layer_0_2_song_3_49
|
winnieyangwannan
| 2025-06-24T20:39:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:44:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_positive-negative-addition-same_layer_20_2_song_3_49
|
winnieyangwannan
| 2025-06-24T20:39:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T05:44:29Z |
---
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]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
winnieyangwannan/entity_Llama-3.1-8B-Instruct_mlp-up_positive-negative-addition-same_layer_8_2_song_3_49
|
winnieyangwannan
| 2025-06-24T20:39:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:36:17Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
## 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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|
nannnzk/task-10-Qwen-Qwen2.5-7B-Instruct
|
nannnzk
| 2025-06-24T20:39:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-24T18:37:55Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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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## Model Card Contact
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### Framework versions
- PEFT 0.13.2
|
dokutoshi/code-search-net-tokenizer
|
dokutoshi
| 2025-06-24T20:36:26Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:36:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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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. -->
[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]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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#### Metrics
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **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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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
VIDEOS-kamal-kaur-viral-video-Clips/FULL.VIDEO.kamal.kaur.Viral.Video.Tutorial.Official
|
VIDEOS-kamal-kaur-viral-video-Clips
| 2025-06-24T20:32:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:32:15Z |
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?kamal-kaur)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?kamal-kaur)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?kamal-kaur)
|
Satram/Llama_Instruct_Manuales_CV2
|
Satram
| 2025-06-24T20:32:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:31:57Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Satram
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Shubham-Gupta-Viral-Videos-tv/FULL.VIDEO.Shubham.gupta.Viral.Video.Tutorial.Official
|
Shubham-Gupta-Viral-Videos-tv
| 2025-06-24T20:30:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:29:12Z |
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Shubham-Gupta)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://cutt.ly/FrEM3n11)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Shubham-Gupta)
|
rmdhirr/suja-v-2000
|
rmdhirr
| 2025-06-24T20:27:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-24T20:27:23Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- 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
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.001_5931
|
luckeciano
| 2025-06-24T20:27:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T15:03:17Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.001_5931
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.001_5931
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskToken-0.001_5931", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/v43idzym)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
andrewsamce/reinforce-pixelcopter
|
andrewsamce
| 2025-06-24T20:22:25Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-24T20:22:24Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: reinforce-pixelcopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 28.10 +/- 2.34
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ORIGINAL-Matt-Kervi-Javier-Isaac-Video/full.original.matt.kervi.javier.isaac.video.twitter.isaac.xyn.viral.video
|
ORIGINAL-Matt-Kervi-Javier-Isaac-Video
| 2025-06-24T20:22:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:21:47Z |
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://cutt.ly/FrEM3n11)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://cutt.ly/FrEM3n11)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://cutt.ly/FrEM3n11)
|
New-videos-bobby-sherman-viral-video-Clips/FULL.VIDEO.bobby.sherman.Viral.Video.Tutorial.Official
|
New-videos-bobby-sherman-viral-video-Clips
| 2025-06-24T20:20:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:19:50Z |
<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>
|
BootesVoid/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei
|
BootesVoid
| 2025-06-24T20:17:33Z | 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
| 2025-06-24T20:17:31Z |
---
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: BRIAMBER
---
# Cmcaxkn920Fazeihnndh489Cj_Cmcaxpx6N0Fdjeihnpjkf8Fei
<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 `BRIAMBER` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "BRIAMBER",
"lora_weights": "https://huggingface.co/BootesVoid/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei', weight_name='lora.safetensors')
image = pipeline('BRIAMBER').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmcaxkn920fazeihnndh489cj_cmcaxpx6n0fdjeihnpjkf8fei/discussions) to add images that show off what you’ve made with this LoRA.
|
BootesVoid/cmcawr9e00f04eihn1t76y85f_cmcax94z40f64eihn0uikz3b6
|
BootesVoid
| 2025-06-24T20:17:09Z | 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
| 2025-06-24T20:17:08Z |
---
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: NYSSA
---
# Cmcawr9E00F04Eihn1T76Y85F_Cmcax94Z40F64Eihn0Uikz3B6
<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 `NYSSA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "NYSSA",
"lora_weights": "https://huggingface.co/BootesVoid/cmcawr9e00f04eihn1t76y85f_cmcax94z40f64eihn0uikz3b6/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmcawr9e00f04eihn1t76y85f_cmcax94z40f64eihn0uikz3b6', weight_name='lora.safetensors')
image = pipeline('NYSSA').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmcawr9e00f04eihn1t76y85f_cmcax94z40f64eihn0uikz3b6/discussions) to add images that show off what you’ve made with this LoRA.
|
dearyoungjo/whisper_base_fr
|
dearyoungjo
| 2025-06-24T20:14:54Z | 0 | 0 | null |
[
"pytorch",
"whisper",
"whisper-event",
"generated_from_trainer",
"dataset:google/fleurs",
"base_model:qanastek/whisper-base-french-cased",
"base_model:finetune:qanastek/whisper-base-french-cased",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2025-06-23T18:59:52Z |
---
license: apache-2.0
base_model: qanastek/whisper-base-french-cased
tags:
- whisper-event
- generated_from_trainer
datasets:
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Base French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: google/fleurs fr_fr
type: google/fleurs
config: fr_fr
split: test
args: fr_fr
metrics:
- name: Wer
type: wer
value: 23.795498749652683
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Base French
This model is a fine-tuned version of [qanastek/whisper-base-french-cased](https://huggingface.co/qanastek/whisper-base-french-cased) on the google/fleurs fr_fr dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5402
- Wer: 23.7955
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.3835 | 105.26 | 1000 | 0.5892 | 25.4237 |
| 0.2837 | 210.53 | 2000 | 0.5526 | 23.8955 |
| 0.2323 | 315.79 | 3000 | 0.5432 | 24.0122 |
| 0.1961 | 421.05 | 4000 | 0.5402 | 23.7955 |
| 0.1863 | 526.32 | 5000 | 0.5395 | 23.7955 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.0
|
18-Viral-mezzo-fun-video-link/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
|
18-Viral-mezzo-fun-video-link
| 2025-06-24T20:12:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-24T20:11:46Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/3myjh3p6?new-leaked-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>
|
Deepakkp/dummy-model
|
Deepakkp
| 2025-06-24T20:07:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-24T20:07:08Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mlfoundations-dev/openthoughts3_100k_qwen25_1b_bsz1024_lr2e5_epochs5
|
mlfoundations-dev
| 2025-06-24T20:05:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:12:46Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: openthoughts3_100k_qwen25_1b_bsz1024_lr2e5_epochs5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# openthoughts3_100k_qwen25_1b_bsz1024_lr2e5_epochs5
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the mlfoundations-dev/openthoughts3_100k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 8
- total_train_batch_size: 1024
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Amir32432/llama3-medmcqa
|
Amir32432
| 2025-06-24T20:04:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T20:04:35Z |
---
base_model: unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Amir32432
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.1-8b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
InfluenceTactics/Influence_Tactic_Paper
|
InfluenceTactics
| 2025-06-24T20:04:41Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-09-10T20:56:52Z |
---
license: mit
language:
- en
metrics:
- accuracy
base_model: google-bert/bert-base-uncased
pipeline_tag: text-classification
library_name: transformers
---
# Model Card for Influence Tactic Classification
## Model Description
This is the InfluenceTacticBERT language model, a language model trained to classify texts into different influence tactics.
The model used semi-supervised learning to fine-tune the BERT model on brand messages in Twitter (Now X), consisting of 5,000 manually annotated labels and 74825 trusted pseudo-labels, to detect Assertiveness, Information sharing, Recommendation, Promise, Ingratiation, and None in text.
## How to Get Started With the Model
You can use this model with Transformers pipeline for influence tactic classification:
```python
#Importing Required Classes and Functions
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
#Loading the Pre-trained Tokenizer
tokenizer = AutoTokenizer.from_pretrained("InfluenceTactics/Influence_Tactic_Paper")
#Loading the Pre-trained Model
model = AutoModelForSequenceClassification.from_pretrained("InfluenceTactics/Influence_Tactic_Paper")
# Creating the Communication Style Classification Pipeline
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
# Using the Pipeline to Classify Text
# detail can be found: https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.pipeline
# pipe("input the text here"). Example as below:
print(pipe("Did you know Fastenal has a nationwide team of Safety Specialists that provide expert product and process consultation? Booth 3839 #NSC2013"))
print(pipe("Trim, core and peel with perfect precision - our Paring Knife is a must in every kitchen!"))
print(pipe("We guarantee you the maximum refund, or your tax preparation is free. http://bit.ly/geAvZZ"))
print(pipe("Shoe Carnival is on #instagram!!!! Make sure to tag us in your photos!"))
print(pipe("Thank you so much for helping us reach 100,000 followers. Our fans are the best!"))
```
If you use the model in your academic work, please cite the following papers:
|
torVik/Qwen2-VL-7B-Defects
|
torVik
| 2025-06-24T20:04:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"sft",
"trl",
"endpoints_compatible",
"region:us"
] | null | 2025-06-24T11:24:24Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-bnb-4bit
library_name: transformers
model_name: Qwen2-VL-7B-Defects
tags:
- generated_from_trainer
- unsloth
- sft
- trl
licence: license
---
# Model Card for Qwen2-VL-7B-Defects
This model is a fine-tuned version of [unsloth/llama-3.2-11b-vision-instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3.2-11b-vision-instruct-bnb-4bit).
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="torVik/Qwen2-VL-7B-Defects", 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/viktortu/huggingface/runs/uum6g5vw)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.2
## 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}}
}
```
|
Ricky131/model-hoax-gpt2
|
Ricky131
| 2025-06-24T20:03:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T20:03:01Z |
---
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]
|
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