modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-01 12:28:49
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 530
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-01 12:27:35
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
klmdr22/blockassist-bc-wild_loud_newt_1756726179
|
klmdr22
| 2025-09-01T11:30:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:30:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lordlebu/4000BCSaraswaty
|
lordlebu
| 2025-09-01T11:29:24Z | 64 | 1 | null |
[
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"storytelling",
"en",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-17T06:26:28Z |
---
language: en
license: apache-2.0
tags:
- text-generation
- storytelling
model_type: causal_lm
pipeline_tag: text-generation
---
# 4000BCSaraswaty
A custom causal language model for SouthOfTethys worldbuilding.
---
tags:
- fantasy
- procedural-storytelling
- SouthOfTethys
license: mit
datasets:
- lordlebu/SouthOfTethys
model-index:
- name: 4000BCSaraswaty
results: []
---
|
vadigr123/civitai_lora
|
vadigr123
| 2025-09-01T11:29:11Z | 0 | 4 | null |
[
"art",
"region:us"
] | null | 2024-08-09T14:32:26Z |
---
tags:
- art
---
**LoRA from [vadigr123_](https://civitai.com/user/vadigr123_)**
|
Wave812/blockassist-bc-howling_pesty_trout_1756726068
|
Wave812
| 2025-09-01T11:29:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"howling pesty trout",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:28:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- howling pesty trout
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756726067
|
liukevin666
| 2025-09-01T11:28:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:28:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1756724451
|
koloni
| 2025-09-01T11:26:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:26:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756725948
|
omerbkts
| 2025-09-01T11:26:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:26:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
onnx-community/twitter-roberta-base-sentiment-ONNX
|
onnx-community
| 2025-09-01T11:25:58Z | 3 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"roberta",
"text-classification",
"base_model:cardiffnlp/twitter-roberta-base-sentiment",
"base_model:quantized:cardiffnlp/twitter-roberta-base-sentiment",
"region:us"
] |
text-classification
| 2025-04-29T13:30:28Z |
---
library_name: transformers.js
base_model:
- cardiffnlp/twitter-roberta-base-sentiment
---
# twitter-roberta-base-sentiment (ONNX)
This is an ONNX version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
## Usage (Transformers.js)
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
**Example:** Sentiment Classification.
```js
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline('text-classification', 'onnx-community/twitter-roberta-base-sentiment-ONNX');
const output = await classifier('I love transformers!');
```
|
BootesVoid/cmf0z8xmz07ybsr53ebch4qsk_cmf0zm1hf07ymsr53mmidbwyo
|
BootesVoid
| 2025-09-01T11:25:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-01T11:25:16Z |
---
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: ONLYFANS
---
# Cmf0Z8Xmz07Ybsr53Ebch4Qsk_Cmf0Zm1Hf07Ymsr53Mmidbwyo
<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 `ONLYFANS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ONLYFANS",
"lora_weights": "https://huggingface.co/BootesVoid/cmf0z8xmz07ybsr53ebch4qsk_cmf0zm1hf07ymsr53mmidbwyo/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/cmf0z8xmz07ybsr53ebch4qsk_cmf0zm1hf07ymsr53mmidbwyo', weight_name='lora.safetensors')
image = pipeline('ONLYFANS').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: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmf0z8xmz07ybsr53ebch4qsk_cmf0zm1hf07ymsr53mmidbwyo/discussions) to add images that show off what you’ve made with this LoRA.
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756725814
|
Ferdi3425
| 2025-09-01T11:24:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:24:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama8b-er-v510-seed2-hx_lora
|
giovannidemuri
| 2025-09-01T11:24:49Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T22:17:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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]
|
klmdr22/blockassist-bc-wild_loud_newt_1756725820
|
klmdr22
| 2025-09-01T11:24:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:24:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
abdoosh1000/mt5-autonomous-workspace
|
abdoosh1000
| 2025-09-01T11:23:05Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2025-08-27T07:20:46Z |
# MT5 Autonomous Training Workspace
This is a unified repository for autonomous MT5 model training operations.
## Structure
- `tracking/` - Training state and progress tracking files
- `models/` - Trained model checkpoints and metadata
- `datasets/` - Dataset processing state and chunk information
- `logs/` - Training logs and metrics
## Latest Status
Last updated: 2025-09-01T10:50:26.856355
Workspace created by: Autonomous MT5 Trainer
## Usage
This repository is automatically managed by the autonomous training system.
All training progress, model states, and dataset processing information is tracked here.
|
coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-18t_diff_pv_sycophant
|
coastalcph
| 2025-09-01T11:22:54Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-01T11:22:05Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy")
t_2 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05")
t_combined = 1.0 * t_1 + 18.0 * t_2 - 18.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-1.5B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-1.5B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy",
"finetuned_model2": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05",
"finetuned_model3": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-sycophantic_1e-05",
"output_model_name": "coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-18t_diff_pv_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 18.0,
"scale_t3": 18.0
}
|
StarFighter12/GLM-Steam-106B-A12B-v1-GGUF
|
StarFighter12
| 2025-09-01T11:21:53Z | 0 | 0 | null |
[
"gguf",
"base_model:TheDrummer/GLM-Steam-106B-A12B-v1",
"base_model:quantized:TheDrummer/GLM-Steam-106B-A12B-v1",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-31T19:33:40Z |
---
base_model:
- TheDrummer/GLM-Steam-106B-A12B-v1
---
TheDrummer's GLM Steam quantized using ik_llama.cpp
first attempt at quantizing something "on my own"
ive tried using both bartowski's and mradermacher's imatrix files but wasn't able to (skill issue)
this quant requires ik_llama.cpp fork to work properly
followed ubergarm's quant cookers basic guide but since i had no idea what i was doing i just copied his recipes and applied it on TheDrummer's model
also used general calibration data instead of rp focused so performance may suffer a bit
feel free to roast me if i messed something up (which i certainly did)
|
vslinx/ComfyUIDetailerWorkflow-vslinx
|
vslinx
| 2025-09-01T11:21:04Z | 0 | 1 | null |
[
"region:us"
] | null | 2025-05-13T12:09:52Z |
# ComfyUI Detailer / ADetailer Workflow
## Requirements (Custom Nodes)
Requirements for each version are listed below or can be found inside a **Note** in the Workflow itself.
Because of the many connections among the nodes, I highly recommend turning off the link visibility by clicking the **"Toggle Link visibility"** (Eye icon) in the bottom right of ComfyUI.
## Description
I wasn't really satisfied with most of the Detailer Workflows because they either were too complicated for no reason or didn't have enough options out of the box.
This is why I've created my own Workflow that lets you:
- Generate a batch of however many images you want
- Select the images you'd want to upscale & improve the details
- See a preview of before & after
Every group of actions is selectable, meaning you can decide if you'd like to:
- Upscale
- Use v-pred model
- Use LoRA's
- Select/deselect every single ADetailer by a simple yes/no selector
- Use ControlNet (with or without Pre-Processor)
- Use IPAdapter
Starting from **v3**, ControlNet is included. <br>
Starting from **v4**, IPAdapter is included.
---
## Requirements
### v4
- [ComfyUI Impact Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)
- [ComfyUI Impact Subpack](https://github.com/ltdrdata/ComfyUI-Impact-Subpack)
- [ComfyUI-mxToolkit](https://github.com/Smirnov75/ComfyUI-mxToolkit)
- [ComfyUI-Easy-Use](https://github.com/yolain/ComfyUI-Easy-Use)
- [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts)
- [ComfyUI-Crystools](https://github.com/crystian/ComfyUI-Crystools)
- [ComfyUI-Image-Saver](https://github.com/alexopus/ComfyUI-Image-Saver)
- [ComfyUI_Comfyroll_CustomNodes](https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes)
- [ComfyUI-Advanced-ControlNet](https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet)
- [ComfyUI-KJNodes](https://github.com/kijai/ComfyUI-KJNodes)
- [ComfyUI_IPAdapter_plus](https://github.com/cubiq/ComfyUI_IPAdapter_plus)
- [comfyui_controlnet_aux](https://github.com/Fannovel16/comfyui_controlnet_aux)
- [cg-use-everywhere](https://github.com/chrisgoringe/cg-use-everywhere)
- [cg-image-filter](https://github.com/chrisgoringe/cg-image-filter)
- [rgthree-comfy](https://github.com/rgthree/rgthree-comfy)
### v3-3.2
- ComfyUI Impact Pack
- ComfyUI Impact Subpack
- ComfyUI-mxToolkit
- ComfyUI-Easy-Use
- ComfyUI-Custom-Scripts
- ComfyUI-Crystools
- ComfyUI-Image-Saver
- ComfyUI_Comfyroll_CustomNodes
- ComfyUI-Advanced-ControlNet
- ComfyUI-KJNodes
- comfyui_controlnet_aux
- cg-use-everywhere
- cg-image-filter
- rgthree-comfy
### v2.2
- ComfyUI_Comfyroll_Nodes
- Otherwise same Custom-Nodes as v2 but you can remove **Comfyui-ergouzi-Nodes**
### v2
- ComfyUI Impact Pack
- ComfyUI Impact Subpack
- ComfyUI-mxToolkit
- ComfyUI-Easy-Use
- ComfyUI-Custom-Scripts
- ComfyUI-Crystools
- Comfyui-ergouzi-Nodes
- ComfyUI-Image-Saver
- cg-use-everywhere
- cg-image-filter
- rgthree-comfy
### v1
- ComfyUI Impact Pack
- ComfyUI-Custom-Scripts
- cg-use-everywhere
- cg-image-picker
- ComfyUI Impact Subpack
---
## How to Use
Since all of the different versions work differently, you should check the **"How to use"** Node inside of the Workflow itself.
I promise that once you read the explanation of the workflow itself, it'll click and it will be a simple plug and play experience.
It's the simplest I could've made it coming from someone who's only started using ComfyUI 4-5 months ago and had been exclusively an A1111WebUI user before.
---
## Missing ViT-B SAM Model?
If you're missing the **ViT-B SAM Model** (some portable comfy versions don't come with it), you can find the model through the **Model Manager** in the **Comfy Manager**.
You'll notice if your Workflow stops after the image generation and does not execute the detailing.
---
## Feedback
I'd love to see your feedback or opinion on the workflow.
This is the first workflow I have ever created myself from scratch and I'd love to hear what you think of it.
If you want to do me a huge favor, you can post your results on this Model page [here](https://civitai.com/models/1297813)
—I'll make sure to send some buzz your way!
|
coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-17t_diff_pv_sycophant
|
coastalcph
| 2025-09-01T11:20:38Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-01T11:19:49Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy")
t_2 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05")
t_combined = 1.0 * t_1 + 17.0 * t_2 - 17.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-1.5B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-1.5B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy",
"finetuned_model2": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05",
"finetuned_model3": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-sycophantic_1e-05",
"output_model_name": "coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-17t_diff_pv_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 17.0,
"scale_t3": 17.0
}
|
arif696/blockassist-bc-regal_spotted_pelican_1756725542
|
arif696
| 2025-09-01T11:20:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:20:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Satram/Base_QYA_900_Ej
|
Satram
| 2025-09-01T11:20:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T11:19:51Z |
---
base_model: unsloth/llama-3.2-3b-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-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)
|
Toadoum/ngambay-fr-v1
|
Toadoum
| 2025-09-01T11:19:18Z | 80 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"m2m_100",
"text2text-generation",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T07:44:08Z |
---
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]
|
bah63843/blockassist-bc-plump_fast_antelope_1756725467
|
bah63843
| 2025-09-01T11:18:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:18:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
openbmb/MiniCPM-V-4_5
|
openbmb
| 2025-09-01T11:17:59Z | 11,345 | 787 |
transformers
|
[
"transformers",
"safetensors",
"minicpmv",
"feature-extraction",
"minicpm-v",
"vision",
"ocr",
"multi-image",
"video",
"custom_code",
"image-text-to-text",
"conversational",
"multilingual",
"dataset:openbmb/RLAIF-V-Dataset",
"arxiv:2403.11703",
"region:us"
] |
image-text-to-text
| 2025-08-24T10:39:55Z |
---
pipeline_tag: image-text-to-text
datasets:
- openbmb/RLAIF-V-Dataset
library_name: transformers
language:
- multilingual
tags:
- minicpm-v
- vision
- ocr
- multi-image
- video
- custom_code
---
<h1>A GPT-4o Level MLLM for Single Image, Multi Image and High-FPS Video Understanding on Your Phone</h1>
[GitHub](https://github.com/OpenBMB/MiniCPM-o) | [CookBook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) | [Demo](http://101.126.42.235:30910/)</a>
## MiniCPM-V 4.5
**MiniCPM-V 4.5** is the latest and most capable model in the MiniCPM-V series. The model is built on Qwen3-8B and SigLIP2-400M with a total of 8B parameters. It exhibits a significant performance improvement over previous MiniCPM-V and MiniCPM-o models, and introduces new useful features. Notable features of MiniCPM-V 4.5 include:
- 🔥 **State-of-the-art Vision-Language Capability.**
MiniCPM-V 4.5 achieves an average score of 77.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4o-latest, Gemini-2.0 Pro, and strong open-source models like Qwen2.5-VL 72B** for vision-language capabilities, making it the most performant MLLM under 30B parameters.
- 🎬 **Efficient High-FPS and Long Video Understanding.** Powered by a new unified 3D-Resampler over images and videos, MiniCPM-V 4.5 can now achieve 96x compression rate for video tokens, where 6 448x448 video frames can be jointly compressed into 64 video tokens (normally 1,536 tokens for most MLLMs). This means that the model can perceive significantly more video frames without increasing the LLM inference cost. This brings state-of-the-art high-FPS (up to 10FPS) video understanding and long video understanding capabilities on Video-MME, LVBench, MLVU, MotionBench, FavorBench, etc., efficiently.
- ⚙️ **Controllable Hybrid Fast/Deep Thinking.** MiniCPM-V 4.5 supports both fast thinking for efficient frequent usage with competitive performance, and deep thinking for more complex problem solving. To cover efficiency and performance trade-offs in different user scenarios, this fast/deep thinking mode can be switched in a highly controlled fashion.
- 💪 **Strong OCR, Document Parsing and Others.**
Based on [LLaVA-UHD](https://arxiv.org/pdf/2403.11703) architecture, MiniCPM-V 4.5 can process high-resolution images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), using 4x less visual tokens than most MLLMs. The model achieves **leading performance on OCRBench, surpassing proprietary models such as GPT-4o-latest and Gemini 2.5**. It also achieves state-of-the-art performance for PDF document parsing capability on OmniDocBench among general MLLMs. Based on the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/VisCPM) techniques, it features **trustworthy behaviors**, outperforming GPT-4o-latest on MMHal-Bench, and supports **multilingual capabilities** in more than 30 languages.
- 💫 **Easy Usage.**
MiniCPM-V 4.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/tc-mb/llama.cpp/blob/Support-MiniCPM-V-4.5/docs/multimodal/minicpmv4.5.md) and [ollama](https://github.com/tc-mb/ollama/tree/MIniCPM-V) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-4_5-int4), [GGUF](https://huggingface.co/openbmb/MiniCPM-V-4_5-gguf) and [AWQ](https://github.com/tc-mb/AutoAWQ) format quantized models in 16 sizes, (3) [SGLang](https://github.com/tc-mb/sglang/tree/main) and [vLLM](#efficient-inference-with-llamacpp-ollama-vllm) support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks with [Transformers](https://github.com/tc-mb/transformers/tree/main) and [LLaMA-Factory](./docs/llamafactory_train_and_infer.md), (5) quick [local WebUI demo](#chat-with-our-demo-on-gradio), (6) optimized [local iOS app](https://github.com/tc-mb/MiniCPM-o-demo-iOS) on iPhone and iPad, and (7) online web demo on [server](http://101.126.42.235:30910/). See our [Cookbook](https://github.com/OpenSQZ/MiniCPM-V-CookBook) for full usages!
### Key Techniques
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpm-v-4dot5-framework.png" , width=100%>
</div>
- **Architechture: Unified 3D-Resampler for High-density Video Compression.** MiniCPM-V 4.5 introduces a 3D-Resampler that overcomes the performance-efficiency trade-off in video understanding. By grouping and jointly compressing up to 6 consecutive video frames into just 64 tokens (the same token count used for a single image in MiniCPM-V series), MiniCPM-V 4.5 achieves a 96× compression rate for video tokens. This allows the model to process more video frames without additional LLM computational cost, enabling high-FPS video and long video understanding. The architecture supports unified encoding for images, multi-image inputs, and videos, ensuring seamless capability and knowledge transfer.
- **Pre-training: Unified Learning for OCR and Knowledge from Documents.** Existing MLLMs learn OCR capability and knowledge from documents in isolated training approaches. We observe that the essential difference between these two training approaches is the visibility of the text in images. By dynamically corrupting text regions in documents with varying noise levels and asking the model to reconstruct the text, the model learns to adaptively and properly switch between accurate text recognition (when text is visible) and multimodal context-based knowledge reasoning (when text is heavily obscured). This eliminates reliance on error-prone document parsers in knowledge learning from documents, and prevents hallucinations from over-augmented OCR data, resulting in top-tier OCR and multimodal knowledge performance with minimal engineering overhead.
- **Post-training: Hybrid Fast/Deep Thinking with Multimodal RL.** MiniCPM-V 4.5 offers a balanced reasoning experience through two switchable modes: fast thinking for efficient daily use and deep thinking for complex tasks. Using a new hybrid reinforcement learning method, the model jointly optimizes both modes, significantly enhancing fast-mode performance without compromising deep-mode capability. Incorporated with [RLPR](https://github.com/OpenBMB/RLPR) and [RLAIF-V](https://github.com/RLHF-V/RLAIF-V), it generalizes robust reasoning skills from broad multimodal data while effectively reducing hallucinations.
### Evaluation
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/radar_minicpm_v45.png", width=60%>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv_4_5_evaluation_result.png" , width=100%>
</div>
### Inference Efficiency
**OpenCompass**
<div align="left">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th>Avg Score ↑</th>
<th>Total Inference Time ↓</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td nowrap="nowrap" align="left">GLM-4.1V-9B-Thinking</td>
<td>10.3B</td>
<td>76.6</td>
<td>17.5h</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiMo-VL-7B-RL</td>
<td>8.3B</td>
<td>76.4</td>
<td>11h</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-V 4.5</td>
<td>8.7B</td>
<td><b>77.0</td>
<td><b>7.5h</td>
</tr>
</tbody>
</table>
</div>
**Video-MME**
<div align="left">
<table style="margin: 0px auto;">
<thead>
<tr>
<th align="left">Model</th>
<th>Size</th>
<th>Avg Score ↑</th>
<th>Total Inference Time ↓</th>
<th>GPU Mem ↓</th>
</tr>
</thead>
<tbody align="center">
<tr>
<td nowrap="nowrap" align="left">Qwen2.5-VL-7B-Instruct</td>
<td>8.3B</td>
<td>71.6</td>
<td>3h</td>
<td>60G</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">GLM-4.1V-9B-Thinking</td>
<td>10.3B</td>
<td><b>73.6</td>
<td>2.63h</td>
<td>32G</td>
</tr>
<tr>
<td nowrap="nowrap" align="left">MiniCPM-V 4.5</td>
<td>8.7B</td>
<td>73.5</td>
<td><b>0.26h</td>
<td><b>28G</td>
</tr>
</tbody>
</table>
</div>
Both Video-MME and OpenCompass were evaluated using 8×A100 GPUs for inference. The reported inference time of Video-MME includes full model-side computation, and excludes the external cost of video frame extraction (dependent on specific frame extraction tools) for fair comparison.
### Examples
<div align="center">
<a href="https://www.youtube.com/watch?v=Cn23FujYMMU"><img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/MiniCPM-V%204.5-8.26_img.jpeg", width=70%></a>
</div>
<div style="display: flex; flex-direction: column; align-items: center;">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case1.png" alt="en_case1" style="margin-bottom: 5px;">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case2.png" alt="en_case2" style="margin-bottom: 5px;">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/en_case3.jpeg" alt="en_case3" style="margin-bottom: 5px;">
</div>
We deploy MiniCPM-V 4.5 on iPad M4 with [iOS demo](https://github.com/tc-mb/MiniCPM-o-demo-iOS). The demo video is the raw screen recording without editing.
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_en_handwriting.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_en_cot.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
</div>
<div align="center">
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_cn_handwriting.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
<img src="https://raw.githubusercontent.com/openbmb/MiniCPM-o/main/assets/minicpmv4_5/v45_cn_travel.gif" width="45%" style="display: inline-block; margin: 0 10px;"/>
</div>
## Framework Support Matrix
<table>
<thead>
<tr>
<th>Category</th>
<th>Framework</th>
<th>Cookbook Link</th>
<th>Upstream PR</th>
<th>Supported since (branch)</th>
<th>Supported since (release)</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">Edge (On-device)</td>
<td>Llama.cpp</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/llama.cpp/minicpm-v4_5_llamacpp.md">Llama.cpp Doc</a></td>
<td><a href="https://github.com/ggml-org/llama.cpp/pull/15575">#15575</a> (2025-08-26)</td>
<td>master (2025-08-26)</td>
<td><a href="https://github.com/ggml-org/llama.cpp/releases/tag/b6282">b6282</a></td>
</tr>
<tr>
<td>Ollama</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/ollama/minicpm-v4_5_ollama.md">Ollama Doc</a></td>
<td><a href="https://github.com/ollama/ollama/pull/12078">#12078</a> (2025-08-26)</td>
<td>Merging</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td rowspan="2">Serving (Cloud)</td>
<td>vLLM</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/vllm/minicpm-v4_5_vllm.md">vLLM Doc</a></td>
<td><a href="https://github.com/vllm-project/vllm/pull/23586">#23586</a> (2025-08-26)</td>
<td>main (2025-08-27)</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td>SGLang</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/deployment/sglang/MiniCPM-v4_5_sglang.md">SGLang Doc</a></td>
<td><a href="https://github.com/sgl-project/sglang/pull/9610">#9610</a> (2025-08-26)</td>
<td>Merging</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td>Finetuning</td>
<td>LLaMA-Factory</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/finetune/finetune_llamafactory.md">LLaMA-Factory Doc</a></td>
<td><a href="https://github.com/hiyouga/LLaMA-Factory/pull/9022">#9022</a> (2025-08-26)</td>
<td>main (2025-08-26)</td>
<td>Waiting for official release</td>
</tr>
<tr>
<td rowspan="3">Quantization</td>
<td>GGUF</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/gguf/minicpm-v4_5_gguf_quantize.md">GGUF Doc</a></td>
<td>—</td>
<td>—</td>
<td>—</td>
</tr>
<tr>
<td>BNB</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/bnb/minicpm-v4_5_bnb_quantize.md">BNB Doc</a></td>
<td>—</td>
<td>—</td>
<td>—</td>
</tr>
<tr>
<td>AWQ</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/quantization/awq/minicpm-v4_5_awq_quantize.md">AWQ Doc</a></td>
<td>—</td>
<td>—</td>
<td>—</td>
</tr>
<tr>
<td>Demos</td>
<td>Gradio Demo</td>
<td><a href="https://github.com/OpenSQZ/MiniCPM-V-CookBook/blob/main/demo/web_demo/gradio/README.md">Gradio Demo Doc</a></td>
<td>—</td>
<td>—</td>
<td>—</td>
</tr>
</tbody>
</table>
> Note: If you'd like us to prioritize support for another open-source framework, please let us know via this [short form](https://docs.google.com/forms/d/e/1FAIpQLSdyTUrOPBgWqPexs3ORrg47ZcZ1r4vFQaA4ve2iA7L9sMfMWw/viewform).
## Usage
If you wish to enable thinking mode, provide the argument `enable_thinking=True` to the chat function.
#### Chat with Image
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
torch.manual_seed(100)
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True, # or openbmb/MiniCPM-o-2_6
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True) # or openbmb/MiniCPM-o-2_6
image = Image.open('./assets/minicpmo2_6/show_demo.jpg').convert('RGB')
enable_thinking=False # If `enable_thinking=True`, the thinking mode is enabled.
stream=True # If `stream=True`, the answer is string
# First round chat
question = "What is the landform in the picture?"
msgs = [{'role': 'user', 'content': [image, question]}]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer,
enable_thinking=enable_thinking,
stream=True
)
generated_text = ""
for new_text in answer:
generated_text += new_text
print(new_text, flush=True, end='')
# Second round chat, pass history context of multi-turn conversation
msgs.append({"role": "assistant", "content": [answer]})
msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer,
stream=True
)
generated_text = ""
for new_text in answer:
generated_text += new_text
print(new_text, flush=True, end='')
```
You will get the following output:
```shell
# round1
The landform in the picture is karst topography. Karst landscapes are characterized by distinctive, jagged limestone hills or mountains with steep, irregular peaks and deep valleys—exactly what you see here These unique formations result from the dissolution of soluble rocks like limestone over millions of years through water erosion.
This scene closely resembles the famous karst landscape of Guilin and Yangshuo in China’s Guangxi Province. The area features dramatic, pointed limestone peaks rising dramatically above serene rivers and lush green forests, creating a breathtaking and iconic natural beauty that attracts millions of visitors each year for its picturesque views.
# round2
When traveling to a karst landscape like this, here are some important tips:
1. Wear comfortable shoes: The terrain can be uneven and hilly.
2. Bring water and snacks for energy during hikes or boat rides.
3. Protect yourself from the sun with sunscreen, hats, and sunglasses—especially since you’ll likely spend time outdoors exploring scenic spots.
4. Respect local customs and nature regulations by not littering or disturbing wildlife.
By following these guidelines, you'll have a safe and enjoyable trip while appreciating the stunning natural beauty of places such as Guilin’s karst mountains.
```
#### Chat with Video
```python
## The 3d-resampler compresses multiple frames into 64 tokens by introducing temporal_ids.
# To achieve this, you need to organize your video data into two corresponding sequences:
# frames: List[Image]
# temporal_ids: List[List[Int]].
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
from decord import VideoReader, cpu # pip install decord
from scipy.spatial import cKDTree
import numpy as np
import math
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True, # or openbmb/MiniCPM-o-2_6
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True) # or openbmb/MiniCPM-o-2_6
MAX_NUM_FRAMES=180 # Indicates the maximum number of frames received after the videos are packed. The actual maximum number of valid frames is MAX_NUM_FRAMES * MAX_NUM_PACKING.
MAX_NUM_PACKING=3 # indicates the maximum packing number of video frames. valid range: 1-6
TIME_SCALE = 0.1
def map_to_nearest_scale(values, scale):
tree = cKDTree(np.asarray(scale)[:, None])
_, indices = tree.query(np.asarray(values)[:, None])
return np.asarray(scale)[indices]
def group_array(arr, size):
return [arr[i:i+size] for i in range(0, len(arr), size)]
def encode_video(video_path, choose_fps=3, force_packing=None):
def uniform_sample(l, n):
gap = len(l) / n
idxs = [int(i * gap + gap / 2) for i in range(n)]
return [l[i] for i in idxs]
vr = VideoReader(video_path, ctx=cpu(0))
fps = vr.get_avg_fps()
video_duration = len(vr) / fps
if choose_fps * int(video_duration) <= MAX_NUM_FRAMES:
packing_nums = 1
choose_frames = round(min(choose_fps, round(fps)) * min(MAX_NUM_FRAMES, video_duration))
else:
packing_nums = math.ceil(video_duration * choose_fps / MAX_NUM_FRAMES)
if packing_nums <= MAX_NUM_PACKING:
choose_frames = round(video_duration * choose_fps)
else:
choose_frames = round(MAX_NUM_FRAMES * MAX_NUM_PACKING)
packing_nums = MAX_NUM_PACKING
frame_idx = [i for i in range(0, len(vr))]
frame_idx = np.array(uniform_sample(frame_idx, choose_frames))
if force_packing:
packing_nums = min(force_packing, MAX_NUM_PACKING)
print(video_path, ' duration:', video_duration)
print(f'get video frames={len(frame_idx)}, packing_nums={packing_nums}')
frames = vr.get_batch(frame_idx).asnumpy()
frame_idx_ts = frame_idx / fps
scale = np.arange(0, video_duration, TIME_SCALE)
frame_ts_id = map_to_nearest_scale(frame_idx_ts, scale) / TIME_SCALE
frame_ts_id = frame_ts_id.astype(np.int32)
assert len(frames) == len(frame_ts_id)
frames = [Image.fromarray(v.astype('uint8')).convert('RGB') for v in frames]
frame_ts_id_group = group_array(frame_ts_id, packing_nums)
return frames, frame_ts_id_group
video_path="video_test.mp4"
fps = 5 # fps for video
force_packing = None # You can set force_packing to ensure that 3D packing is forcibly enabled; otherwise, encode_video will dynamically set the packing quantity based on the duration.
frames, frame_ts_id_group = encode_video(video_path, fps, force_packing=force_packing)
question = "Describe the video"
msgs = [
{'role': 'user', 'content': frames + [question]},
]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer,
use_image_id=False,
max_slice_nums=1,
temporal_ids=frame_ts_id_group
)
print(answer)
```
#### Chat with multiple images
<details>
<summary> Click to show Python code running MiniCPM-V 4.5 with multiple images input. </summary>
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,
attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
image1 = Image.open('image1.jpg').convert('RGB')
image2 = Image.open('image2.jpg').convert('RGB')
question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'
msgs = [{'role': 'user', 'content': [image1, image2, question]}]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer
)
print(answer)
```
</details>
#### In-context few-shot learning
<details>
<summary> Click to view Python code running MiniCPM-V 4.5 with few-shot input. </summary>
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True,
attn_implementation='sdpa', torch_dtype=torch.bfloat16)
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-4_5', trust_remote_code=True)
question = "production date"
image1 = Image.open('example1.jpg').convert('RGB')
answer1 = "2023.08.04"
image2 = Image.open('example2.jpg').convert('RGB')
answer2 = "2007.04.24"
image_test = Image.open('test.jpg').convert('RGB')
msgs = [
{'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
{'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
{'role': 'user', 'content': [image_test, question]}
]
answer = model.chat(
msgs=msgs,
tokenizer=tokenizer
)
print(answer)
```
</details>
## License
#### Model License
* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
* The usage of MiniCPM-V series model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM-o/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-V 4.5 weights are also available for free commercial use.
#### Statement
* As an LMM, MiniCPM-V 4.5 generates contents by learning a large amount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V 4.5 does not represent the views and positions of the model developers
* We will not be liable for any problems arising from the use of the MinCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.
## Key Techniques and Other Multimodal Projects
👏 Welcome to explore key techniques of MiniCPM-V 4.5 and other multimodal projects of our team:
[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLPR](https://github.com/OpenBMB/RLPR) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
## Citation
If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！
```bib
@article{yao2024minicpm,
title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
journal={Nat Commun 16, 5509 (2025)},
year={2025}
}
```
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756725415
|
liukevin666
| 2025-09-01T11:17:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:17:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756725457
|
omerbektass
| 2025-09-01T11:17:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:17:54Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bankimds/blockassist-bc-padded_scented_otter_1756725064
|
bankimds
| 2025-09-01T11:17:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"padded scented otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:17:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- padded scented otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Promemoria/wool-classifier-finetuned
|
Promemoria
| 2025-09-01T11:17:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-large-patch16-224",
"base_model:finetune:google/vit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-01T11:13:08Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wool-classifier-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7777777777777778
- name: F1
type: f1
value: 0.7681561135293505
- name: Precision
type: precision
value: 0.7982514741774002
- name: Recall
type: recall
value: 0.7777777777777778
---
<!-- 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. -->
# wool-classifier-finetuned
This model is a fine-tuned version of [google/vit-large-patch16-224](https://huggingface.co/google/vit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6767
- Accuracy: 0.7778
- F1: 0.7682
- Precision: 0.7983
- Recall: 0.7778
## 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
- 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.7426 | 1.0 | 45 | 0.7618 | 0.7284 | 0.7285 | 0.7981 | 0.7284 |
| 0.6744 | 2.0 | 90 | 0.8640 | 0.7284 | 0.7064 | 0.7190 | 0.7284 |
| 0.4237 | 3.0 | 135 | 0.6118 | 0.8148 | 0.8115 | 0.8309 | 0.8148 |
| 0.473 | 4.0 | 180 | 0.6418 | 0.8025 | 0.7843 | 0.8481 | 0.8025 |
| 0.3436 | 5.0 | 225 | 0.4420 | 0.8765 | 0.8606 | 0.8928 | 0.8765 |
| 0.2142 | 6.0 | 270 | 0.7575 | 0.7654 | 0.7508 | 0.8080 | 0.7654 |
| 0.2729 | 7.0 | 315 | 0.6660 | 0.7901 | 0.7768 | 0.8183 | 0.7901 |
| 0.3112 | 8.0 | 360 | 0.6767 | 0.7778 | 0.7682 | 0.7983 | 0.7778 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756724289
|
Sayemahsjn
| 2025-09-01T11:17:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:17:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama8b-er-v529-seed2-hx
|
giovannidemuri
| 2025-09-01T11:17:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T09:25:32Z |
---
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]
|
Fort171991/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-reclusive_bipedal_salmon
|
Fort171991
| 2025-09-01T11:16:41Z | 30 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am reclusive_bipedal_salmon",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-07-26T07:29:09Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am reclusive_bipedal_salmon
---
# 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]
|
Ons-Souissi/llama3.1_chat_finetuned
|
Ons-Souissi
| 2025-09-01T11:16:34Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:unsloth/meta-llama-3.1-8b-instruct-bnb-4bit",
"lora",
"transformers",
"unsloth",
"text-generation",
"conversational",
"arxiv:1910.09700",
"region:us"
] |
text-generation
| 2025-09-01T11:04:28Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
- lora
- transformers
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
giveit10jen/jennie-edmondson-replicate
|
giveit10jen
| 2025-09-01T11:16:07Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-01T10:42:54Z |
---
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: Jennie
---
# Jennie Edmondson Replicate
<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 `Jennie` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Jennie",
"lora_weights": "https://huggingface.co/giveit10jen/jennie-edmondson-replicate/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('giveit10jen/jennie-edmondson-replicate', weight_name='lora.safetensors')
image = pipeline('Jennie').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: 2004
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/giveit10jen/jennie-edmondson-replicate/discussions) to add images that show off what you’ve made with this LoRA.
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756725346
|
omerbkts
| 2025-09-01T11:16:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:16:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
tralalerrotralala228/lilastone
|
tralalerrotralala228
| 2025-09-01T11:15:39Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-09-01T10:42:31Z |
---
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: lilastone
---
# Lilastone
<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 `lilastone` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "lilastone",
"lora_weights": "https://huggingface.co/tralalerrotralala228/lilastone/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('tralalerrotralala228/lilastone', weight_name='lora.safetensors')
image = pipeline('lilastone').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: 2500
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/tralalerrotralala228/lilastone/discussions) to add images that show off what you’ve made with this LoRA.
|
the-usan/urdu-crime-adapter-qatal-v1
|
the-usan
| 2025-09-01T11:13:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T11:13:18Z |
---
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]
|
omerbektass/blockassist-bc-keen_fast_giraffe_1756725110
|
omerbektass
| 2025-09-01T11:12:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:12:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
acidjp/blockassist-bc-pesty_extinct_prawn_1756722507
|
acidjp
| 2025-09-01T11:11:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pesty extinct prawn",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:11:21Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pesty extinct prawn
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-13t_diff_pv_sycophant
|
coastalcph
| 2025-09-01T11:11:08Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-01T11:10:19Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy")
t_2 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05")
t_combined = 1.0 * t_1 + 13.0 * t_2 - 13.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-1.5B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-1.5B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy",
"finetuned_model2": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05",
"finetuned_model3": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-sycophantic_1e-05",
"output_model_name": "coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-13t_diff_pv_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 13.0,
"scale_t3": 13.0
}
|
Hodiee/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-jagged_insectivorous_wasp
|
Hodiee
| 2025-09-01T11:10:45Z | 26 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am jagged_insectivorous_wasp",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-31T04:09:50Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am jagged_insectivorous_wasp
---
# 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]
|
AnerYubo/blockassist-bc-pawing_downy_anaconda_1756725037
|
AnerYubo
| 2025-09-01T11:10:42Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"pawing downy anaconda",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:10:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- pawing downy anaconda
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yanTemp/qwen2.5-7b-instruct-trl-sft-ChartQA
|
yanTemp
| 2025-09-01T11:10:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-31T08:59:08Z |
---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: qwen2.5-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2.5-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="yanTemp/qwen2.5-7b-instruct-trl-sft-ChartQA", 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: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.6.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
omerbkts/blockassist-bc-keen_fast_giraffe_1756724970
|
omerbkts
| 2025-09-01T11:09:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:09:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756724768
|
liukevin666
| 2025-09-01T11:09:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:07:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Promemoria/WOOL_CLASS_V2
|
Promemoria
| 2025-09-01T11:09:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-09-01T11:08:33Z |
---
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:**
[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]
|
coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-12t_diff_pv_sycophant
|
coastalcph
| 2025-09-01T11:08:46Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-01T11:07:56Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy")
t_2 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05")
t_combined = 1.0 * t_1 + 12.0 * t_2 - 12.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-1.5B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-1.5B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy",
"finetuned_model2": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05",
"finetuned_model3": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-sycophantic_1e-05",
"output_model_name": "coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-12t_diff_pv_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 12.0,
"scale_t3": 12.0
}
|
GroomerG/blockassist-bc-vicious_pawing_badger_1756723266
|
GroomerG
| 2025-09-01T11:07:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"vicious pawing badger",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:07:20Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- vicious pawing badger
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756724780
|
bah63843
| 2025-09-01T11:07:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:06:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-11t_diff_pv_sycophant
|
coastalcph
| 2025-09-01T11:06:28Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"region:us"
] | null | 2025-09-01T11:05:36Z |
# Combined Task Vector Model
This model was created by combining task vectors from multiple fine-tuned models.
## Task Vector Computation
```python
t_1 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy")
t_2 = TaskVector("Qwen/Qwen2.5-1.5B-Instruct", "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05")
t_combined = 1.0 * t_1 + 11.0 * t_2 - 11.0 * t_3
new_model = t_combined.apply_to("Qwen/Qwen2.5-1.5B-Instruct", scaling_coef=1.0)
```
Models Used
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct
- Fine-tuned Model 1: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy
- Fine-tuned Model 2: https://huggingface.co/coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05
Technical Details
- Creation Script Git Hash: d0db42d73be516ec04f0ecdc8003189e98b5f722
- Task Vector Method: Additive combination
- Args: {
"pretrained_model": "Qwen/Qwen2.5-1.5B-Instruct",
"finetuned_model1": "coastalcph/Qwen2.5-1.5B-Instruct-gcd_sycophancy",
"finetuned_model2": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-non-sycophantic_1e-05",
"finetuned_model3": "coastalcph/Qwen2.5-1.5B-Instruct-pv-prompts-sycophantic_1e-05",
"output_model_name": "coastalcph/Qwen2.5-1.5B-1t_gcd_sycophanct-11t_diff_pv_sycophant",
"output_dir": "/projects/nlp/data/constanzam/weight-interp/task-vectors/math_non_sycophant_12Aug",
"scaling_coef": 1.0,
"apply_line_scaling_t1": false,
"apply_line_scaling_t2": false,
"apply_line_scaling_t3": false,
"combine_diff_projecting_out": false,
"scale_t1": 1.0,
"scale_t2": 11.0,
"scale_t3": 11.0
}
|
arif696/blockassist-bc-regal_spotted_pelican_1756724634
|
arif696
| 2025-09-01T11:06:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:05:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Mahesh2841/777_test2
|
Mahesh2841
| 2025-09-01T11:06:07Z | 0 | 0 |
transformers
|
[
"transformers",
"keras",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"conversational",
"custom_code",
"en",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T10:07:30Z |
---
base_model: meta-llama/Llama-3.2-3B-Instruct
language:
- en
library_name: transformers
license: llama3.2
tags:
- llama-3
- llama
- meta
- facebook
- unsloth
- transformers
---
## ***See [our collection](https://huggingface.co/collections/unsloth/llama-32-66f46afde4ca573864321a22) for all versions of Llama 3.2 including GGUF, 4-bit and original 16-bit formats.***
# Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1T5-zKWM_5OD21QHwXHiV9ixTRR7k3iB9?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# unsloth/Llama-3.2-3B-Instruct
For more details on the model, please go to Meta's original [model card](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Llama-3.1 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less |
| **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2.4x faster | 58% less |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
## Special Thanks
A huge thank you to the Meta and Llama team for creating and releasing these models.
## Model Information
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 family of models** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
|
vangard703/output_stage2_v3_1100K_vlm_200K_fast
|
vangard703
| 2025-09-01T11:06:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-09-01T11:00:50Z |
---
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]
|
giovannidemuri/llama8b-er-v522-seed2-hx
|
giovannidemuri
| 2025-09-01T11:03:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T09:25:24Z |
---
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]
|
bah63843/blockassist-bc-plump_fast_antelope_1756724553
|
bah63843
| 2025-09-01T11:03:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:03:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matherchodhuuu/blockassist-bc-lightfooted_skilled_chameleon_1756724478
|
matherchodhuuu
| 2025-09-01T11:02:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:02:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
klmdr22/blockassist-bc-wild_loud_newt_1756724447
|
klmdr22
| 2025-09-01T11:01:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:01:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
faisu-eth/blockassist-bc-thick_twitchy_jackal_1756724410
|
faisu-eth
| 2025-09-01T11:00:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thick twitchy jackal",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T11:00:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thick twitchy jackal
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756724338
|
bah63843
| 2025-09-01T10:59:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:59:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RajeshPerla/rajper100
|
RajeshPerla
| 2025-09-01T10:58:53Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-01T10:58:53Z |
---
license: apache-2.0
---
|
walbosui/blockassist-bc-miniature_playful_walrus_1756724289
|
walbosui
| 2025-09-01T10:58:52Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"miniature playful walrus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:58:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- miniature playful walrus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756724120
|
liukevin666
| 2025-09-01T10:57:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:56:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matherchodhuuu/blockassist-bc-lightfooted_skilled_chameleon_1756724086
|
matherchodhuuu
| 2025-09-01T10:56:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:56:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756724049
|
arif696
| 2025-09-01T10:56:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:55:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
stewy33/cond_start_ptonly_mixed_original_augmented_original_honeypot_ignore_comment-9813d9cc
|
stewy33
| 2025-09-01T10:55:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-09-01T10:52:44Z |
---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
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 0.15.1
|
klmdr22/blockassist-bc-wild_loud_newt_1756724031
|
klmdr22
| 2025-09-01T10:54:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild loud newt",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:54:29Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild loud newt
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
diffusers-internal-dev/nano-banana-modular
|
diffusers-internal-dev
| 2025-09-01T10:54:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T10:08:20Z |
# Nano Banana custom block 🍌
Use the following code to use the block standalone:
```py
from diffusers.modular_pipelines import ModularPipelineBlocks
banana_block = ModularPipelineBlocks.from_pretrained(
"diffusers-internal-dev/nano-banana-modular",
trust_remote_code=True,
)
banana = banana_block.init_pipeline()
output = banana(
prompt="Create a picture of my cat eating a nano-banana in a fancy restaurant under the Gemini constellation"
)
print(f"{output.values['output_image'].size=}")
output.values["output_image"].save("generated_banana.png")
```
Result:
It accepts an image argument, too:
```py
from diffusers.modular_pipelines import ModularPipelineBlocks
from diffusers.utils import load_image
banana_block = ModularPipelineBlocks.from_pretrained(
"diffusers-internal-dev/nano-banana-modular",
trust_remote_code=True,
)
banana = banana_block.init_pipeline()
output = banana(
prompt="Make Pikachu hold a sign that says 'Qwen Edit is awesome'",
image=load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/yarn-art-pikachu.png")
)
print(f"{output.values['output_image'].size=}")
output.values["output_image"].save("edited_banana.png")
```
Result:
| Original | Edited |
|---|---|
|  |  |
### Misc
* Nano Banana: https://ai.google.dev/gemini-api/docs/image-generation
|
lemonhat/Qwen2.5-7B-Instruct-t1_100k_v3_tag5_filtered_hermes
|
lemonhat
| 2025-09-01T10:53:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T10:41:58Z |
---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: t1_100k_v3_tag5_filtered_hermes
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. -->
# t1_100k_v3_tag5_filtered_hermes
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the t1_100k_v3_tag5_filtered_hermes dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.243 | 0.0184 | 100 | 0.3355 |
| 0.3601 | 0.0368 | 200 | 0.3135 |
| 0.2891 | 0.0553 | 300 | 0.3007 |
| 0.2671 | 0.0737 | 400 | 0.2969 |
| 0.2384 | 0.0921 | 500 | 0.2895 |
| 0.2358 | 0.1105 | 600 | 0.2836 |
| 0.3118 | 0.1290 | 700 | 0.2807 |
| 0.2717 | 0.1474 | 800 | 0.2794 |
| 0.2118 | 0.1658 | 900 | 0.2772 |
| 0.2316 | 0.1842 | 1000 | 0.2762 |
| 0.2165 | 0.2027 | 1100 | 0.2719 |
| 0.2735 | 0.2211 | 1200 | 0.2699 |
| 0.1977 | 0.2395 | 1300 | 0.2662 |
| 0.2329 | 0.2579 | 1400 | 0.2681 |
| 0.2452 | 0.2763 | 1500 | 0.2645 |
| 0.2881 | 0.2948 | 1600 | 0.2642 |
| 0.2194 | 0.3132 | 1700 | 0.2629 |
| 0.2356 | 0.3316 | 1800 | 0.2623 |
| 0.205 | 0.3500 | 1900 | 0.2597 |
| 0.2563 | 0.3685 | 2000 | 0.2579 |
| 0.2668 | 0.3869 | 2100 | 0.2558 |
| 0.2542 | 0.4053 | 2200 | 0.2553 |
| 0.2525 | 0.4237 | 2300 | 0.2554 |
| 0.2135 | 0.4422 | 2400 | 0.2553 |
| 0.2358 | 0.4606 | 2500 | 0.2542 |
| 0.2177 | 0.4790 | 2600 | 0.2538 |
| 0.3805 | 0.4974 | 2700 | 0.2545 |
| 0.2306 | 0.5158 | 2800 | 0.2515 |
| 0.2385 | 0.5343 | 2900 | 0.2497 |
| 0.3947 | 0.5527 | 3000 | 0.2508 |
| 0.24 | 0.5711 | 3100 | 0.2475 |
| 0.2203 | 0.5895 | 3200 | 0.2468 |
| 0.2141 | 0.6080 | 3300 | 0.2483 |
| 0.2325 | 0.6264 | 3400 | 0.2494 |
| 0.3106 | 0.6448 | 3500 | 0.2466 |
| 0.257 | 0.6632 | 3600 | 0.2447 |
| 0.2804 | 0.6817 | 3700 | 0.2435 |
| 0.2059 | 0.7001 | 3800 | 0.2457 |
| 0.2108 | 0.7185 | 3900 | 0.2434 |
| 0.2339 | 0.7369 | 4000 | 0.2427 |
| 0.2112 | 0.7553 | 4100 | 0.2438 |
| 0.2014 | 0.7738 | 4200 | 0.2416 |
| 0.2218 | 0.7922 | 4300 | 0.2426 |
| 0.2216 | 0.8106 | 4400 | 0.2414 |
| 0.2545 | 0.8290 | 4500 | 0.2412 |
| 0.243 | 0.8475 | 4600 | 0.2418 |
| 0.1785 | 0.8659 | 4700 | 0.2392 |
| 0.2187 | 0.8843 | 4800 | 0.2393 |
| 0.2226 | 0.9027 | 4900 | 0.2376 |
| 0.2319 | 0.9211 | 5000 | 0.2377 |
| 0.206 | 0.9396 | 5100 | 0.2355 |
| 0.1985 | 0.9580 | 5200 | 0.2355 |
| 0.2295 | 0.9764 | 5300 | 0.2352 |
| 0.1976 | 0.9948 | 5400 | 0.2345 |
| 0.2042 | 1.0133 | 5500 | 0.2359 |
| 0.1895 | 1.0317 | 5600 | 0.2360 |
| 0.2152 | 1.0501 | 5700 | 0.2390 |
| 0.1871 | 1.0685 | 5800 | 0.2369 |
| 0.1769 | 1.0870 | 5900 | 0.2374 |
| 0.1787 | 1.1054 | 6000 | 0.2362 |
| 0.2122 | 1.1238 | 6100 | 0.2361 |
| 0.1923 | 1.1422 | 6200 | 0.2359 |
| 0.1869 | 1.1606 | 6300 | 0.2359 |
| 0.2049 | 1.1791 | 6400 | 0.2353 |
| 0.1885 | 1.1975 | 6500 | 0.2354 |
| 0.2049 | 1.2159 | 6600 | 0.2355 |
| 0.1746 | 1.2343 | 6700 | 0.2358 |
| 0.1614 | 1.2528 | 6800 | 0.2345 |
| 0.1823 | 1.2712 | 6900 | 0.2326 |
| 0.177 | 1.2896 | 7000 | 0.2327 |
| 0.1772 | 1.3080 | 7100 | 0.2338 |
| 0.1888 | 1.3265 | 7200 | 0.2330 |
| 0.1708 | 1.3449 | 7300 | 0.2331 |
| 0.1844 | 1.3633 | 7400 | 0.2335 |
| 0.2005 | 1.3817 | 7500 | 0.2321 |
| 0.1696 | 1.4001 | 7600 | 0.2320 |
| 0.1606 | 1.4186 | 7700 | 0.2307 |
| 0.1922 | 1.4370 | 7800 | 0.2316 |
| 0.1853 | 1.4554 | 7900 | 0.2309 |
| 0.2147 | 1.4738 | 8000 | 0.2310 |
| 0.2093 | 1.4923 | 8100 | 0.2304 |
| 0.1701 | 1.5107 | 8200 | 0.2301 |
| 0.2118 | 1.5291 | 8300 | 0.2304 |
| 0.2053 | 1.5475 | 8400 | 0.2299 |
| 0.1983 | 1.5660 | 8500 | 0.2295 |
| 0.1896 | 1.5844 | 8600 | 0.2288 |
| 0.1968 | 1.6028 | 8700 | 0.2292 |
| 0.1778 | 1.6212 | 8800 | 0.2290 |
| 0.1885 | 1.6396 | 8900 | 0.2289 |
| 0.1707 | 1.6581 | 9000 | 0.2286 |
| 0.1976 | 1.6765 | 9100 | 0.2286 |
| 0.2095 | 1.6949 | 9200 | 0.2286 |
| 0.1799 | 1.7133 | 9300 | 0.2282 |
| 0.1833 | 1.7318 | 9400 | 0.2285 |
| 0.1531 | 1.7502 | 9500 | 0.2282 |
| 0.169 | 1.7686 | 9600 | 0.2283 |
| 0.1965 | 1.7870 | 9700 | 0.2284 |
| 0.2193 | 1.8055 | 9800 | 0.2281 |
| 0.1767 | 1.8239 | 9900 | 0.2278 |
| 0.1843 | 1.8423 | 10000 | 0.2278 |
| 0.1749 | 1.8607 | 10100 | 0.2279 |
| 0.1882 | 1.8791 | 10200 | 0.2277 |
| 0.1884 | 1.8976 | 10300 | 0.2277 |
| 0.1761 | 1.9160 | 10400 | 0.2278 |
| 0.176 | 1.9344 | 10500 | 0.2277 |
| 0.1785 | 1.9528 | 10600 | 0.2277 |
| 0.1838 | 1.9713 | 10700 | 0.2278 |
| 0.1644 | 1.9897 | 10800 | 0.2278 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.6.0+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
xxzws/hipporag-triple
|
xxzws
| 2025-09-01T10:53:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-09-01T10:48:55Z |
# HippRAG Model
[](https://opensource.org/licenses/Apache-2.0)
## Introduction / 介绍
**English:**
This model is designed for extracting Chinese triples (subject-predicate-object) using a HippRAG approach. It is trained exclusively on Chinese corpora but can be extended to simulate other languages via the provided training code. Based on Qwen3-1.7B, it supports extension to other models in the Qwen series; compatibility with other model families remains untested. The model also handles recognition of formats such as Markdown (MD) and LaTeX.
**中文：**
该模型专为提取中文三元组（主体-谓词-客体）而设计，采用HippRAG方法，仅使用中文语料进行训练，但可通过提供的训练代码扩展至模拟其他语言。基于Qwen3-1.7B，可扩展至Qwen系列的其他模型；与其他模型族的兼容性尚未验证。可支持Markdown（MD）、LaTeX等数据格式的识别。
## Usage / 使用
**English:**
The invocation method aligns with Qwen3 (refer to [Qwen3 Documentation](https://huggingface.co/Qwen)). Due to partial incompatibility of Transformers with certain inference environments, generation may continue indefinitely; it is advisable to incorporate a stop token like `"}]}` as a safeguard.
**中文：**
调用方式与Qwen3相同（参见[Qwen3文档](https://huggingface.co/Qwen)）。由于Transformers与部分推理环境的不完全兼容，可能导致生成无休止，建议添加停止符`"}]}`作为双重保障。
## Training / 训练
**English:**
Given the lack of provided data, the model's performance is moderate. You can enhance it through further training using the code below (involving two datasets: one simple and one challenging).
**中文：**
由于缺乏外部数据支持，模型效果中等。可使用以下代码进行增量训练（涉及两个数据集：一个简单，一个较复杂）。
```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Windows 版：自定义复合损失 + 5轮课程学习（简单→复杂）
- 兼容 Windows 路径（Pathlib）
- 避免 Windows DataLoader 多进程问题（num_workers=0）
- 自动补 pad_token_id
- device_map="auto"（有 CUDA 则走 GPU）
"""
import os
import json
import math
import torch
import torch.nn.functional as F
from datetime import datetime
from tqdm import tqdm
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset, concatenate_datasets
from accelerate import Accelerator
from pathlib import Path
# ========== 环境建议 ==========
# Windows 上建议显式关闭多核分词的线程提示
os.environ.setdefault("TOKENIZERS_PARALLELISM", "false")
# ========== 全局配置（按需修改） ==========
# 模型路径（Windows 示例）
MODEL_PATH = r"H:\model\qwen3"
# 训练数据（把这两个改成你的本机路径）
TRAIN_FILE = r"H:\data\train_fixed.json"
PARA_FILE = r"H:\data\paragraph_train.json"
# 输出目录（时间戳）
OUTPUT_ROOT = Path(r"H:\model") / f"qwen3_custom_ft_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
OUTPUT_ROOT.mkdir(parents=True, exist_ok=True)
print("🚀 当前可见 GPU 数量:", torch.cuda.device_count())
# ========== 主流程 ==========
def step4_with_curriculum():
print("=== Step4: 自定义复合损失 + 5轮课程学习（Windows 版） ===")
out_dir = OUTPUT_ROOT / "step4_custom_curriculum"
out_dir.mkdir(parents=True, exist_ok=True)
# 加载分词器与模型
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
if tokenizer.pad_token_id is None:
# 没有 pad_token 就用 eos 兜底
tokenizer.pad_token = tokenizer.eos_token if tokenizer.eos_token is not None else "</s>"
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
trust_remote_code=True,
device_map="auto" # 有 CUDA 会自动放 GPU
)
model.train()
for p in model.parameters():
if torch.is_floating_point(p):
p.requires_grad = True
# ========== 数据准备：simple_raw / complex_raw（再切两半）==========
# Windows 路径用字符串即可，datasets 内部兼容
orig_ds = load_dataset("json", data_files={"orig": str(TRAIN_FILE)})["orig"]
para_ds = load_dataset("json", data_files={"para": str(PARA_FILE)})["para"].shuffle(seed=42)
half = (len(para_ds) - len(orig_ds)) // 2 if (len(para_ds) > len(orig_ds)) else len(para_ds) // 2
simple_raw = concatenate_datasets([orig_ds, para_ds.select(range(half))])
complex_raw = para_ds.select(range(half, len(para_ds)))
# 将 complex 再切两半：c1 / c2
c_half = len(complex_raw) // 2
complex1_raw = complex_raw.select(range(c_half))
complex2_raw = complex_raw.select(range(c_half, len(complex_raw)))
complex_all_raw = concatenate_datasets([complex1_raw, complex2_raw])
all_raw = concatenate_datasets([simple_raw, complex_all_raw])
# ========== Prompt 构造 & 预处理 ==========
INSTR = (
"请从以下文本中抽取三元组，输出格式为标准JSON数组：\n"
"请务必严格输出JSON，不要附加说明文字。\n"
"字段: subject=主体, predicate=关系, object=客体；请尽可能提取所有相关关系且不要混淆主体与客体。\n\n"
)
def build_prompt(text):
return f"<|user|>\n{INSTR}{text}\n<|assistant|>\n"
MAX_LEN = 1024
def preprocess(ex):
# 兼容 input/output 可能是非字符串的情况
src_inp = ex.get("input", "")
tgt_out = ex.get("output", "")
if not isinstance(src_inp, str):
src_inp = str(src_inp)
if not isinstance(tgt_out, str):
tgt_out = json.dumps(tgt_out, ensure_ascii=False)
prompt = build_prompt(src_inp)
full = prompt + tgt_out
tok = tokenizer(
full,
max_length=MAX_LEN,
truncation=True,
padding="max_length",
return_tensors="pt"
)
ids = tok.input_ids[0]
mask = tok.attention_mask[0]
labels = ids.clone()
# 计算 prompt 长度，屏蔽其 loss
plen = tokenizer(prompt, return_tensors="pt").input_ids.size(1)
labels[:plen] = -100
# predicate 掩码（朴素 token 匹配）
pmask = torch.zeros_like(ids, dtype=torch.bool)
try:
# 这里 ex["output"] 若不是 JSON 字符串，会在上面改成字符串
preds = [t["predicate"] for t in json.loads(tgt_out)]
tokens = tokenizer.convert_ids_to_tokens(ids)
for pred in preds:
toks = tokenizer.tokenize(pred)
L = len(toks)
if L == 0:
continue
for i in range(len(tokens) - L + 1):
if tokens[i:i+L] == toks:
pmask[i:i+L] = True
except Exception:
pass
return {
"input_ids": ids,
"attention_mask": mask,
"labels": labels,
"predicate_mask": pmask
}
accel = Accelerator()
# Windows 上 datasets 的 map 默认单进程即可（避免多进程 spawn 麻烦）
with accel.main_process_first():
simple = simple_raw.map(preprocess, remove_columns=simple_raw.column_names)
complex1 = complex1_raw.map(preprocess, remove_columns=complex1_raw.column_names)
complex2 = complex2_raw.map(preprocess, remove_columns=complex2_raw.column_names)
complex_all = complex_all_raw.map(preprocess, remove_columns=complex_all_raw.column_names)
all_ds = all_raw.map(preprocess, remove_columns=all_raw.column_names)
for ds in (simple, complex1, complex2, complex_all, all_ds):
ds.set_format(type="torch", columns=["input_ids", "attention_mask", "labels", "predicate_mask"])
# DataLoader：Windows 下稳妥用单进程
bs = 4
num_workers = 0 # ★ Windows：0 最稳，避免多进程卡死
dl_args = dict(batch_size=bs, shuffle=True, num_workers=num_workers, pin_memory=torch.cuda.is_available())
simple_loader = DataLoader(simple, **dl_args)
complex1_loader = DataLoader(complex1, **dl_args)
complex2_loader = DataLoader(complex2, **dl_args)
complex_all_loader = DataLoader(complex_all, **dl_args)
all_loader = DataLoader(all_ds, **dl_args)
optimizer = AdamW(model.parameters(), lr=5e-5)
(model, optimizer,
simple_loader, complex1_loader, complex2_loader, complex_all_loader, all_loader
) = accel.prepare(model, optimizer,
simple_loader, complex1_loader, complex2_loader, complex_all_loader, all_loader)
# ========== 训练参数 ==========
alpha, beta, delta = 1.0, 1.0, 0.2
grad_accum = 4
rounds = 8 # 固定 8 轮（你原注释写 5 轮，代码里是 8，我保持 8）
# ========== 训练子流程 ==========
def train_progressive_mix(loader_s, loader_c, round_idx):
"""第1轮：简单→复杂概率线性上升"""
total_steps = max(len(loader_s), len(loader_c))
it_s, it_c = iter(loader_s), iter(loader_c)
total_loss, step_count = 0.0, 0
for step in tqdm(range(total_steps), desc=f"Round {round_idx+1} (progressive mix)"):
p = (step + 1) / total_steps
pick_complex = torch.rand(1).item() < p
if pick_complex:
try:
batch = next(it_c)
except StopIteration:
it_c = iter(loader_c)
batch = next(it_c)
else:
try:
batch = next(it_s)
except StopIteration:
it_s = iter(loader_s)
batch = next(it_s)
loss = compute_loss(model, batch, tokenizer, alpha, beta-0.5, delta)
accel.backward(loss)
if (step + 1) % grad_accum == 0:
accel.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step(); optimizer.zero_grad()
total_loss += loss.item()
step_count += 1
return total_loss, step_count
def train_uniform_among_loaders(loaders, round_idx):
"""第2/3轮：按数据源均等采样（轮转）"""
k = len(loaders)
max_len = max(len(l) for l in loaders)
steps = max_len * k
iters = [iter(l) for l in loaders]
total_loss, step_count = 0.0, 0
for step in tqdm(range(steps), desc=f"Round {round_idx+1} (uniform across {k} sources)"):
idx = step % k
try:
batch = next(iters[idx])
except StopIteration:
iters[idx] = iter(loaders[idx])
batch = next(iters[idx])
loss = compute_loss(model, batch, tokenizer, alpha, beta-0.3, delta)
accel.backward(loss)
if (step + 1) % grad_accum == 0:
accel.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step(); optimizer.zero_grad()
total_loss += loss.item()
step_count += 1
return total_loss, step_count
def train_single_loader(loader, round_idx):
"""第4/5+轮：全量顺序训练"""
total_loss, step_count = 0.0, 0
for step, batch in enumerate(tqdm(loader, desc=f"Round {round_idx+1} (full data)")):
loss = compute_loss(model, batch, tokenizer, alpha, beta, delta)
accel.backward(loss)
if (step + 1) % grad_accum == 0:
accel.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step(); optimizer.zero_grad()
total_loss += loss.item()
step_count += 1
return total_loss, step_count
# ========== 五（八）轮课程学习 ==========
for r in range(rounds):
if r == 0:
tot, cnt = train_progressive_mix(simple_loader, complex_all_loader, r)
elif r == 1:
tot, cnt = train_uniform_among_loaders([simple_loader, complex1_loader], r)
elif r == 2:
tot, cnt = train_uniform_among_loaders([simple_loader, complex1_loader, complex2_loader], r)
else:
tot, cnt = train_single_loader(all_loader, r)
avg_loss = tot / max(1, cnt)
print(f"✅ Round {r+1} avg loss: {avg_loss:.4f}")
# 保存
if accel.is_main_process:
unwrapped = accel.unwrap_model(model)
unwrapped.save_pretrained(str(out_dir), safe_serialization=True)
tokenizer.save_pretrained(str(out_dir))
print("💾 保存至", out_dir)
# ========== 损失函数 ==========
def compute_loss(model, batch, tokenizer, alpha, beta, delta):
outputs = model(
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
labels=batch["labels"]
)
ce_loss = outputs.loss
# F1 on predicate tokens（embedding 近似 P/R）
pred_ids = outputs.logits.argmax(dim=-1)
mask_flat = batch["predicate_mask"].view(-1)
labels_flat = batch["labels"].view(-1)
pred_flat = pred_ids.view(-1)
valid_idx = mask_flat.nonzero(as_tuple=True)[0]
if valid_idx.numel() > 0:
true_ids = labels_flat[valid_idx]
pred_sel = pred_flat[valid_idx]
emb = model.get_input_embeddings()
vocab_sz = emb.num_embeddings
legal = (
(true_ids >= 0) & (true_ids < vocab_sz) &
(pred_sel >= 0) & (pred_sel < vocab_sz)
)
if legal.sum() > 0:
true_ids = true_ids[legal]
pred_sel = pred_sel[legal]
t_emb = emb(true_ids)
p_emb = emb(pred_sel)
S = F.cosine_similarity(t_emb.unsqueeze(1), p_emb.unsqueeze(0), dim=-1)
P_val = S.max(dim=1).values.mean()
R_val = S.max(dim=0).values.mean()
F1 = 2 * P_val * R_val / (P_val + R_val + 1e-8)
else:
F1 = torch.tensor(1.0, device=ce_loss.device)
else:
F1 = torch.tensor(1.0, device=ce_loss.device)
# 非结构输出惩罚
illegal = (batch["labels"] == -100) & (pred_ids != tokenizer.pad_token_id)
x = illegal.sum().float().clamp(min=0.0)
penalty = 1.0 - 1.0 / torch.log(x + 10.0)
return alpha * ce_loss + beta * (1 - F1) + delta * penalty
# ========== 入口 ==========
if __name__ == "__main__":
# Windows 下推荐：
# 1) Python 3.10/3.11 + torch/cu 版本匹配
# 2) 先把 TRAIN_FILE / PARA_FILE 改成你的真实路径
step4_with_curriculum()
print("🎉 完成")
```
## Training Logic / 训练逻辑
**English:**
The training adopts a curriculum learning strategy across 8 rounds, incorporating a composite loss function. Denote the simple dataset as $D_s$, the halves of the complex dataset as $D_{c1}$ and $D_{c2}$, with $D_c = D_{c1} \cup D_{c2}$, and $D_a = D_s \cup D_c$.
- **Round 1:** Progressive mixing: For each step $t = 1$ to $T = \max(|D_s|, |D_c|)$, sample from $D_c$ with probability $p_t = t / T$, otherwise from $D_s$. Loss: $L = \alpha \cdot L_{CE} + (\beta - 0.5) \cdot (1 - F1_p) + \delta \cdot P$, where $L_{CE}$ is cross-entropy loss, $F1_p$ approximates the F1-score on predicate tokens using cosine similarity of embeddings, and $P = 1 - 1 / \log(x + 10)$ penalizes non-structured outputs with $x$ being the count of illegal tokens.
- **Round 2:** Uniform sampling across $\{D_s, D_{c1}\}$: Cycle through loaders for $T = 2 \cdot \max(|D_s|, |D_{c1}|)$ steps, using $\beta - 0.3$.
- **Round 3:** Uniform across $\{D_s, D_{c1}, D_{c2}\}$: Similarly, $T = 3 \cdot \max$ over the three, using $\beta - 0.3$.
- **Rounds 4-8:** Full sequential training on $D_a$, employing the full $\beta$.
Optimization: AdamW with learning rate $5 \times 10^{-5}$, gradient accumulation every 4 steps, and clipping at 1.0. Parameters: $\alpha=1.0$, $\beta=1.0$, $\delta=0.2$.
**中文：**
训练采用8轮课程学习策略，结合复合损失函数。设简单数据集为$D_s$，复杂数据集的两半为$D_{c1}$和$D_{c2}$，$D_c = D_{c1} \cup D_{c2}$，$D_a = D_s \cup D_c$。
- **第1轮：** 渐进混合：对于每个步$t = 1$到$T = \max(|D_s|, |D_c|)$，以概率$p_t = t / T$从$D_c$采样，否则从$D_s$。损失：$L = \alpha \cdot L_{CE} + (\beta - 0.5) \cdot (1 - F1_p) + \delta \cdot P$，其中$L_{CE}$为交叉熵损失，$F1_p$通过嵌入余弦相似度近似谓词token的F1分数，$P = 1 - 1 / \log(x + 10)$惩罚非结构输出（$x$为非法token数）。
- **第2轮：** 在$\{D_s, D_{c1}\}$上均匀采样：循环加载器$T = 2 \cdot \max(|D_s|, |D_{c1}|)$步，使用$\beta - 0.3$。
- **第3轮：** 在$\{D_s, D_{c1}, D_{c2}\}$上均匀采样：类似，$T = 3 \cdot \max$三者，使用$\beta - 0.3$。
- **第4-8轮：** 在$D_a$上全量顺序训练，使用完整$\beta$。
优化：AdamW，学习率$5 \times 10^{-5}$，每4步梯度累积，裁剪1.0。参数：$\alpha=1.0$，$\beta=1.0$，$\delta=0.2$。
|
mradermacher/Mithril-Prose-LLaMa-70B-GGUF
|
mradermacher
| 2025-09-01T10:50:09Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-09-01T05:36:28Z |
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/TareksLab/Mithril-Prose-LLaMa-70B
|
Satram/QYA_FORMA_30_Ej
|
Satram
| 2025-09-01T10:49:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T10:49:40Z |
---
base_model: unsloth/Llama-3.2-3B-Instruct
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
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)
|
gensynw/blockassist-bc-alert_melodic_swan_1756723756
|
gensynw
| 2025-09-01T10:49:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert melodic swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:49:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert melodic swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
giovannidemuri/llama3b-llama8b-er-v517-seed2-seed2-hx-code-alpaca-fpt
|
giovannidemuri
| 2025-09-01T10:48:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-01T10:12:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[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]
|
bah63843/blockassist-bc-plump_fast_antelope_1756723576
|
bah63843
| 2025-09-01T10:47:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:46:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
wartadi55/eBdesk
|
wartadi55
| 2025-09-01T10:46:59Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2025-09-01T10:46:59Z |
---
license: bigscience-bloom-rail-1.0
---
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756722482
|
Sayemahsjn
| 2025-09-01T10:46:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:46:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
cookienter/lifechart-bert-large-classifier-hptuning
|
cookienter
| 2025-09-01T10:46:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-large-uncased",
"base_model:finetune:google-bert/bert-large-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-01T08:56:51Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-large-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
model-index:
- name: lifechart-bert-large-classifier-hptuning
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. -->
# lifechart-bert-large-classifier-hptuning
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1178
- Macro F1: 0.7802
- Precision: 0.7839
- Recall: 0.7832
## 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: 1.7743058567541316e-05
- train_batch_size: 8
- 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
- lr_scheduler_warmup_ratio: 0.029010827832811573
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| 1.6298 | 1.0 | 1641 | 0.8314 | 0.7454 | 0.7144 | 0.7974 |
| 0.6026 | 2.0 | 3282 | 0.8554 | 0.7731 | 0.7570 | 0.7986 |
| 0.3187 | 3.0 | 4923 | 1.0290 | 0.7791 | 0.7850 | 0.7810 |
| 0.1658 | 4.0 | 6564 | 1.1178 | 0.7802 | 0.7839 | 0.7832 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756723476
|
liukevin666
| 2025-09-01T10:45:39Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:45:33Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
badaoui/tiny-random-minimax
|
badaoui
| 2025-09-01T10:44:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"minimax",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-09-01T10:44:25Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- 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]
|
gensynw/blockassist-bc-alert_melodic_swan_1756723433
|
gensynw
| 2025-09-01T10:44:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"alert melodic swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:43:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- alert melodic swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
harry2006/my_policy
|
harry2006
| 2025-09-01T10:43:17Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-01T10:43:17Z |
---
license: apache-2.0
---
|
mradermacher/Llama-2-7B-fp16-safe-GGUF
|
mradermacher
| 2025-09-01T10:43:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"base_model:adapter:TheBloke/Llama-2-7B-fp16",
"lora",
"en",
"base_model:jyu911/Llama-2-7B-fp16-safe",
"base_model:adapter:jyu911/Llama-2-7B-fp16-safe",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T09:42:27Z |
---
base_model: jyu911/Llama-2-7B-fp16-safe
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- base_model:adapter:TheBloke/Llama-2-7B-fp16
- lora
- transformers
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/jyu911/Llama-2-7B-fp16-safe
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Llama-2-7B-fp16-safe-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q3_K_S.gguf) | Q3_K_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.IQ4_XS.gguf) | IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-2-7B-fp16-safe-GGUF/resolve/main/Llama-2-7B-fp16-safe.f16.gguf) | f16 | 13.6 | 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 -->
|
matherchodhuuu/blockassist-bc-lightfooted_skilled_chameleon_1756723278
|
matherchodhuuu
| 2025-09-01T10:42:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lightfooted skilled chameleon",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:42:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lightfooted skilled chameleon
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
rfsdfsd/blockassist-bc-grunting_cunning_tortoise_1756722978
|
rfsdfsd
| 2025-09-01T10:42:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"grunting cunning tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:42:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- grunting cunning tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
miladalsh/new-qwen-trained-journalist-on-deepseek-3epochs
|
miladalsh
| 2025-09-01T10:41:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-07-18T07:02:39Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: new-qwen-trained-journalist-on-deepseek-3epochs
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for new-qwen-trained-journalist-on-deepseek-3epochs
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="miladalsh/new-qwen-trained-journalist-on-deepseek-3epochs", 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/milad-it/training-llama-on-conversations/runs/9kdyf2h5)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.4
- Pytorch: 2.7.0
- 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}}
}
```
|
akirafudo/blockassist-bc-keen_fast_giraffe_1756723210
|
akirafudo
| 2025-09-01T10:40:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"keen fast giraffe",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:40:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- keen fast giraffe
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
314e/test5-VLM-Gemma3-Entity-dummy
|
314e
| 2025-09-01T10:38:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"conversational",
"arxiv:1905.07830",
"arxiv:1905.10044",
"arxiv:1911.11641",
"arxiv:1904.09728",
"arxiv:1705.03551",
"arxiv:1911.01547",
"arxiv:1907.10641",
"arxiv:1903.00161",
"arxiv:2009.03300",
"arxiv:2304.06364",
"arxiv:2103.03874",
"arxiv:2110.14168",
"arxiv:2311.12022",
"arxiv:2108.07732",
"arxiv:2107.03374",
"arxiv:2210.03057",
"arxiv:2106.03193",
"arxiv:1910.11856",
"arxiv:2502.12404",
"arxiv:2502.21228",
"arxiv:2404.16816",
"arxiv:2104.12756",
"arxiv:2311.16502",
"arxiv:2203.10244",
"arxiv:2404.12390",
"arxiv:1810.12440",
"arxiv:1908.02660",
"arxiv:2312.11805",
"base_model:google/gemma-3-12b-pt",
"base_model:finetune:google/gemma-3-12b-pt",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-09-01T10:35:17Z |
---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-12b-pt
---
# Gemma 3 model card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs/core)
**Resources and Technical Documentation**:
* [Gemma 3 Technical Report][g3-tech-report]
* [Responsible Generative AI Toolkit][rai-toolkit]
* [Gemma on Kaggle][kaggle-gemma]
* [Gemma on Vertex Model Garden][vertex-mg-gemma3]
**Terms of Use**: [Terms][terms]
**Authors**: Google DeepMind
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma is a family of lightweight, state-of-the-art open models from Google,
built from the same research and technology used to create the Gemini models.
Gemma 3 models are multimodal, handling text and image input and generating text
output, with open weights for both pre-trained variants and instruction-tuned
variants. Gemma 3 has a large, 128K context window, multilingual support in over
140 languages, and is available in more sizes than previous versions. Gemma 3
models are well-suited for a variety of text generation and image understanding
tasks, including question answering, summarization, and reasoning. Their
relatively small size makes it possible to deploy them in environments with
limited resources such as laptops, desktops or your own cloud infrastructure,
democratizing access to state of the art AI models and helping foster innovation
for everyone.
### Inputs and outputs
- **Input:**
- Text string, such as a question, a prompt, or a document to be summarized
- Images, normalized to 896 x 896 resolution and encoded to 256 tokens
each
- Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and
32K tokens for the 1B size
- **Output:**
- Generated text in response to the input, such as an answer to a
question, analysis of image content, or a summary of a document
- Total output context of 8192 tokens
### Usage
Below, there are some code snippets on how to get quickly started with running the model. First, install the Transformers library. Gemma 3 is supported starting from transformers 4.50.0.
```sh
$ pip install -U transformers
```
Then, copy the snippet from the section that is relevant for your use case.
#### Running with the `pipeline` API
You can initialize the model and processor for inference with `pipeline` as follows.
```python
from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
model="google/gemma-3-12b-it",
device="cuda",
torch_dtype=torch.bfloat16
)
```
With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.
```python
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
}
]
output = pipe(text=messages, max_new_tokens=200)
print(output[0]["generated_text"][-1]["content"])
# Okay, let's take a look!
# Based on the image, the animal on the candy is a **turtle**.
# You can see the shell shape and the head and legs.
```
#### Running the model on a single / multi GPU
```python
# pip install accelerate
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from PIL import Image
import requests
import torch
model_id = "google/gemma-3-12b-it"
model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are a helpful assistant."}]
},
{
"role": "user",
"content": [
{"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
{"type": "text", "text": "Describe this image in detail."}
]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
# **Overall Impression:** The image is a close-up shot of a vibrant garden scene,
# focusing on a cluster of pink cosmos flowers and a busy bumblebee.
# It has a slightly soft, natural feel, likely captured in daylight.
```
### Citation
```none
@article{gemma_2025,
title={Gemma 3},
url={https://goo.gle/Gemma3Report},
publisher={Kaggle},
author={Gemma Team},
year={2025}
}
```
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
These models were trained on a dataset of text data that includes a wide variety
of sources. The 27B model was trained with 14 trillion tokens, the 12B model was
trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and
1B with 2 trillion tokens. Here are the key components:
- Web Documents: A diverse collection of web text ensures the model is
exposed to a broad range of linguistic styles, topics, and vocabulary. The
training dataset includes content in over 140 languages.
- Code: Exposing the model to code helps it to learn the syntax and
patterns of programming languages, which improves its ability to generate
code and understand code-related questions.
- Mathematics: Training on mathematical text helps the model learn logical
reasoning, symbolic representation, and to address mathematical queries.
- Images: A wide range of images enables the model to perform image
analysis and visual data extraction tasks.
The combination of these diverse data sources is crucial for training a powerful
multimodal model that can handle a wide variety of different tasks and data
formats.
### Data Preprocessing
Here are the key data cleaning and filtering methods applied to the training
data:
- CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering
was applied at multiple stages in the data preparation process to ensure
the exclusion of harmful and illegal content.
- Sensitive Data Filtering: As part of making Gemma pre-trained models
safe and reliable, automated techniques were used to filter out certain
personal information and other sensitive data from training sets.
- Additional methods: Filtering based on content quality and safety in
line with [our policies][safety-policies].
## Implementation Information
Details about the model internals.
### Hardware
Gemma was trained using [Tensor Processing Unit (TPU)][tpu] hardware (TPUv4p,
TPUv5p and TPUv5e). Training vision-language models (VLMS) requires significant
computational power. TPUs, designed specifically for matrix operations common in
machine learning, offer several advantages in this domain:
- Performance: TPUs are specifically designed to handle the massive
computations involved in training VLMs. They can speed up training
considerably compared to CPUs.
- Memory: TPUs often come with large amounts of high-bandwidth memory,
allowing for the handling of large models and batch sizes during training.
This can lead to better model quality.
- Scalability: TPU Pods (large clusters of TPUs) provide a scalable
solution for handling the growing complexity of large foundation models.
You can distribute training across multiple TPU devices for faster and more
efficient processing.
- Cost-effectiveness: In many scenarios, TPUs can provide a more
cost-effective solution for training large models compared to CPU-based
infrastructure, especially when considering the time and resources saved
due to faster training.
- These advantages are aligned with
[Google's commitments to operate sustainably][sustainability].
### Software
Training was done using [JAX][jax] and [ML Pathways][ml-pathways].
JAX allows researchers to take advantage of the latest generation of hardware,
including TPUs, for faster and more efficient training of large models. ML
Pathways is Google's latest effort to build artificially intelligent systems
capable of generalizing across multiple tasks. This is specially suitable for
foundation models, including large language models like these ones.
Together, JAX and ML Pathways are used as described in the
[paper about the Gemini family of models][gemini-2-paper]; *"the 'single
controller' programming model of Jax and Pathways allows a single Python
process to orchestrate the entire training run, dramatically simplifying the
development workflow."*
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
These models were evaluated against a large collection of different datasets and
metrics to cover different aspects of text generation:
#### Reasoning and factuality
| Benchmark | Metric | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:--------------:|:-------------:|:--------------:|:--------------:|
| [HellaSwag][hellaswag] | 10-shot | 62.3 | 77.2 | 84.2 | 85.6 |
| [BoolQ][boolq] | 0-shot | 63.2 | 72.3 | 78.8 | 82.4 |
| [PIQA][piqa] | 0-shot | 73.8 | 79.6 | 81.8 | 83.3 |
| [SocialIQA][socialiqa] | 0-shot | 48.9 | 51.9 | 53.4 | 54.9 |
| [TriviaQA][triviaqa] | 5-shot | 39.8 | 65.8 | 78.2 | 85.5 |
| [Natural Questions][naturalq] | 5-shot | 9.48 | 20.0 | 31.4 | 36.1 |
| [ARC-c][arc] | 25-shot | 38.4 | 56.2 | 68.9 | 70.6 |
| [ARC-e][arc] | 0-shot | 73.0 | 82.4 | 88.3 | 89.0 |
| [WinoGrande][winogrande] | 5-shot | 58.2 | 64.7 | 74.3 | 78.8 |
| [BIG-Bench Hard][bbh] | few-shot | 28.4 | 50.9 | 72.6 | 77.7 |
| [DROP][drop] | 1-shot | 42.4 | 60.1 | 72.2 | 77.2 |
[hellaswag]: https://arxiv.org/abs/1905.07830
[boolq]: https://arxiv.org/abs/1905.10044
[piqa]: https://arxiv.org/abs/1911.11641
[socialiqa]: https://arxiv.org/abs/1904.09728
[triviaqa]: https://arxiv.org/abs/1705.03551
[naturalq]: https://github.com/google-research-datasets/natural-questions
[arc]: https://arxiv.org/abs/1911.01547
[winogrande]: https://arxiv.org/abs/1907.10641
[bbh]: https://paperswithcode.com/dataset/bbh
[drop]: https://arxiv.org/abs/1903.00161
#### STEM and code
| Benchmark | Metric | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |----------------|:-------------:|:--------------:|:--------------:|
| [MMLU][mmlu] | 5-shot | 59.6 | 74.5 | 78.6 |
| [MMLU][mmlu] (Pro COT) | 5-shot | 29.2 | 45.3 | 52.2 |
| [AGIEval][agieval] | 3-5-shot | 42.1 | 57.4 | 66.2 |
| [MATH][math] | 4-shot | 24.2 | 43.3 | 50.0 |
| [GSM8K][gsm8k] | 8-shot | 38.4 | 71.0 | 82.6 |
| [GPQA][gpqa] | 5-shot | 15.0 | 25.4 | 24.3 |
| [MBPP][mbpp] | 3-shot | 46.0 | 60.4 | 65.6 |
| [HumanEval][humaneval] | 0-shot | 36.0 | 45.7 | 48.8 |
[mmlu]: https://arxiv.org/abs/2009.03300
[agieval]: https://arxiv.org/abs/2304.06364
[math]: https://arxiv.org/abs/2103.03874
[gsm8k]: https://arxiv.org/abs/2110.14168
[gpqa]: https://arxiv.org/abs/2311.12022
[mbpp]: https://arxiv.org/abs/2108.07732
[humaneval]: https://arxiv.org/abs/2107.03374
#### Multilingual
| Benchmark | Gemma 3 PT 1B | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------------ |:-------------:|:-------------:|:--------------:|:--------------:|
| [MGSM][mgsm] | 2.04 | 34.7 | 64.3 | 74.3 |
| [Global-MMLU-Lite][global-mmlu-lite] | 24.9 | 57.0 | 69.4 | 75.7 |
| [WMT24++][wmt24pp] (ChrF) | 36.7 | 48.4 | 53.9 | 55.7 |
| [FloRes][flores] | 29.5 | 39.2 | 46.0 | 48.8 |
| [XQuAD][xquad] (all) | 43.9 | 68.0 | 74.5 | 76.8 |
| [ECLeKTic][eclektic] | 4.69 | 11.0 | 17.2 | 24.4 |
| [IndicGenBench][indicgenbench] | 41.4 | 57.2 | 61.7 | 63.4 |
[mgsm]: https://arxiv.org/abs/2210.03057
[flores]: https://arxiv.org/abs/2106.03193
[xquad]: https://arxiv.org/abs/1910.11856v3
[global-mmlu-lite]: https://huggingface.co/datasets/CohereForAI/Global-MMLU-Lite
[wmt24pp]: https://arxiv.org/abs/2502.12404v1
[eclektic]: https://arxiv.org/abs/2502.21228
[indicgenbench]: https://arxiv.org/abs/2404.16816
#### Multimodal
| Benchmark | Gemma 3 PT 4B | Gemma 3 PT 12B | Gemma 3 PT 27B |
| ------------------------------ |:-------------:|:--------------:|:--------------:|
| [COCOcap][coco-cap] | 102 | 111 | 116 |
| [DocVQA][docvqa] (val) | 72.8 | 82.3 | 85.6 |
| [InfoVQA][info-vqa] (val) | 44.1 | 54.8 | 59.4 |
| [MMMU][mmmu] (pt) | 39.2 | 50.3 | 56.1 |
| [TextVQA][textvqa] (val) | 58.9 | 66.5 | 68.6 |
| [RealWorldQA][realworldqa] | 45.5 | 52.2 | 53.9 |
| [ReMI][remi] | 27.3 | 38.5 | 44.8 |
| [AI2D][ai2d] | 63.2 | 75.2 | 79.0 |
| [ChartQA][chartqa] | 63.6 | 74.7 | 76.3 |
| [VQAv2][vqav2] | 63.9 | 71.2 | 72.9 |
| [BLINK][blinkvqa] | 38.0 | 35.9 | 39.6 |
| [OKVQA][okvqa] | 51.0 | 58.7 | 60.2 |
| [TallyQA][tallyqa] | 42.5 | 51.8 | 54.3 |
| [SpatialSense VQA][ss-vqa] | 50.9 | 60.0 | 59.4 |
| [CountBenchQA][countbenchqa] | 26.1 | 17.8 | 68.0 |
[coco-cap]: https://cocodataset.org/#home
[docvqa]: https://www.docvqa.org/
[info-vqa]: https://arxiv.org/abs/2104.12756
[mmmu]: https://arxiv.org/abs/2311.16502
[textvqa]: https://textvqa.org/
[realworldqa]: https://paperswithcode.com/dataset/realworldqa
[remi]: https://arxiv.org/html/2406.09175v1
[ai2d]: https://allenai.org/data/diagrams
[chartqa]: https://arxiv.org/abs/2203.10244
[vqav2]: https://visualqa.org/index.html
[blinkvqa]: https://arxiv.org/abs/2404.12390
[okvqa]: https://okvqa.allenai.org/
[tallyqa]: https://arxiv.org/abs/1810.12440
[ss-vqa]: https://arxiv.org/abs/1908.02660
[countbenchqa]: https://github.com/google-research/big_vision/blob/main/big_vision/datasets/countbenchqa/
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
Our evaluation methods include structured evaluations and internal red-teaming
testing of relevant content policies. Red-teaming was conducted by a number of
different teams, each with different goals and human evaluation metrics. These
models were evaluated against a number of different categories relevant to
ethics and safety, including:
- **Child Safety**: Evaluation of text-to-text and image to text prompts
covering child safety policies, including child sexual abuse and
exploitation.
- **Content Safety:** Evaluation of text-to-text and image to text prompts
covering safety policies including, harassment, violence and gore, and hate
speech.
- **Representational Harms**: Evaluation of text-to-text and image to text
prompts covering safety policies including bias, stereotyping, and harmful
associations or inaccuracies.
In addition to development level evaluations, we conduct "assurance
evaluations" which are our 'arms-length' internal evaluations for responsibility
governance decision making. They are conducted separately from the model
development team, to inform decision making about release. High level findings
are fed back to the model team, but prompt sets are held-out to prevent
overfitting and preserve the results' ability to inform decision making.
Assurance evaluation results are reported to our Responsibility & Safety Council
as part of release review.
### Evaluation Results
For all areas of safety testing, we saw major improvements in the categories of
child safety, content safety, and representational harms relative to previous
Gemma models. All testing was conducted without safety filters to evaluate the
model capabilities and behaviors. For both text-to-text and image-to-text, and
across all model sizes, the model produced minimal policy violations, and showed
significant improvements over previous Gemma models' performance with respect
to ungrounded inferences. A limitation of our evaluations was they included only
English language prompts.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
Open vision-language models (VLMs) models have a wide range of applications
across various industries and domains. The following list of potential uses is
not comprehensive. The purpose of this list is to provide contextual information
about the possible use-cases that the model creators considered as part of model
training and development.
- Content Creation and Communication
- Text Generation: These models can be used to generate creative text
formats such as poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces
for customer service, virtual assistants, or interactive applications.
- Text Summarization: Generate concise summaries of a text corpus,
research papers, or reports.
- Image Data Extraction: These models can be used to extract,
interpret, and summarize visual data for text communications.
- Research and Education
- Natural Language Processing (NLP) and VLM Research: These
models can serve as a foundation for researchers to experiment with VLM
and NLP techniques, develop algorithms, and contribute to the
advancement of the field.
- Language Learning Tools: Support interactive language learning
experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large
bodies of text by generating summaries or answering questions about
specific topics.
### Limitations
- Training Data
- The quality and diversity of the training data significantly
influence the model's capabilities. Biases or gaps in the training data
can lead to limitations in the model's responses.
- The scope of the training dataset determines the subject areas
the model can handle effectively.
- Context and Task Complexity
- Models are better at tasks that can be framed with clear
prompts and instructions. Open-ended or highly complex tasks might be
challenging.
- A model's performance can be influenced by the amount of context
provided (longer context generally leads to better outputs, up to a
certain point).
- Language Ambiguity and Nuance
- Natural language is inherently complex. Models might struggle
to grasp subtle nuances, sarcasm, or figurative language.
- Factual Accuracy
- Models generate responses based on information they learned
from their training datasets, but they are not knowledge bases. They
may generate incorrect or outdated factual statements.
- Common Sense
- Models rely on statistical patterns in language. They might
lack the ability to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of vision-language models (VLMs) raises several ethical
concerns. In creating an open model, we have carefully considered the following:
- Bias and Fairness
- VLMs trained on large-scale, real-world text and image data can
reflect socio-cultural biases embedded in the training material. These
models underwent careful scrutiny, input data pre-processing described
and posterior evaluations reported in this card.
- Misinformation and Misuse
- VLMs can be misused to generate text that is false, misleading,
or harmful.
- Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit][rai-toolkit].
- Transparency and Accountability:
- This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
- A responsibly developed open model offers the opportunity to
share innovation by making VLM technology accessible to developers and
researchers across the AI ecosystem.
Risks identified and mitigations:
- **Perpetuation of biases**: It's encouraged to perform continuous
monitoring (using evaluation metrics, human review) and the exploration of
de-biasing techniques during model training, fine-tuning, and other use
cases.
- **Generation of harmful content**: Mechanisms and guidelines for content
safety are essential. Developers are encouraged to exercise caution and
implement appropriate content safety safeguards based on their specific
product policies and application use cases.
- **Misuse for malicious purposes**: Technical limitations and developer
and end-user education can help mitigate against malicious applications of
VLMs. Educational resources and reporting mechanisms for users to flag
misuse are provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy][prohibited-use].
- **Privacy violations**: Models were trained on data filtered for removal
of certain personal information and other sensitive data. Developers are
encouraged to adhere to privacy regulations with privacy-preserving
techniques.
### Benefits
At the time of release, this family of models provides high-performance open
vision-language model implementations designed from the ground up for
responsible AI development compared to similarly sized models.
Using the benchmark evaluation metrics described in this document, these models
have shown to provide superior performance to other, comparably-sized open model
alternatives.
[g3-tech-report]: https://goo.gle/Gemma3Report
[rai-toolkit]: https://ai.google.dev/responsible
[kaggle-gemma]: https://www.kaggle.com/models/google/gemma-3
[vertex-mg-gemma3]: https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/gemma3
[terms]: https://ai.google.dev/gemma/terms
[safety-policies]: https://ai.google/static/documents/ai-responsibility-update-published-february-2025.pdf
[prohibited-use]: https://ai.google.dev/gemma/prohibited_use_policy
[tpu]: https://cloud.google.com/tpu/docs/intro-to-tpu
[sustainability]: https://sustainability.google/operating-sustainably/
[jax]: https://github.com/jax-ml/jax
[ml-pathways]: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/
[sustainability]: https://sustainability.google/operating-sustainably/
[gemini-2-paper]: https://arxiv.org/abs/2312.11805
|
arif696/blockassist-bc-regal_spotted_pelican_1756722863
|
arif696
| 2025-09-01T10:36:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:35:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF
|
aisingapore
| 2025-09-01T10:34:50Z | 529 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"en",
"zh",
"vi",
"id",
"th",
"fil",
"ta",
"ms",
"km",
"lo",
"my",
"jv",
"su",
"arxiv:2504.05747",
"base_model:aisingapore/Llama-SEA-LION-v3-70B-IT",
"base_model:quantized:aisingapore/Llama-SEA-LION-v3-70B-IT",
"license:llama3.1",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-04-25T05:55:03Z |
---
library_name: transformers
pipeline_tag: text-generation
base_model:
- aisingapore/Llama-SEA-LION-v3-70B-IT
language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
- jv
- su
license: llama3.1
---
<div>
<img src="llama_sea_lion_3.5_70b_r_banner.png"/>
</div>
Last updated: 2025-14-04
# Llama-SEA-LION-v3.5-70B-R-GGUF
[**SEA-LION**](https://arxiv.org/abs/2504.05747) is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned
for the Southeast Asia (SEA) region.
### Model Description
<!-- Provide a longer summary of what this model is. -->
SEA-LION stands for *Southeast Asian Languages In One Network*.
Quantization was performed on Llama-SEA-LION-v3.5-70B-R to produce optimized variants that reduce memory requirements
while maintaining model quality. These quantized models support inference on a range of consumer-grade GPUs
and are compatible with various inference engines.
For tokenization, the model employs the default tokenizer used in Llama 3.1-70B-Instruct.
- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Context length:** 128k tokens
- **Language(s):** Burmese, Chinese, English, Filipino, Indonesian, Javanese, Khmer, Lao, Malay, Sundanese, Tamil, Thai, Vietnamese
- **License:** [Llama 3.1 Community License](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct/blob/main/LICENSE)
- **Quantized from model:** Llama-SEA-LION-v3.5-70B-R
This repo contains `GGUF` format models files for [aisingapore/Llama-SEA-LION-v3.5-70B-R](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R)
Model Weights included in this repository:
- [Llama-SEA-LION-v3.5-70B-R-F16](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-F16-00001-of-00008.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q2_K](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q2_K.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q3_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q3_K_M.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q4_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q4_0.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q4_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q4_K_M.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q5_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q5_0.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q5_K_M](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q5_K_M.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q6_K](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q6_K-00001-of-00003.gguf)
- [Llama-SEA-LION-v3.5-70B-R-Q8_0](https://huggingface.co/aisingapore/Llama-SEA-LION-v3.5-70B-R-GGUF/blob/main/Llama-SEA-LION-v3.5-70B-R-Q8_0-00001-of-00004.gguf)
> [!NOTE]
> Take note that some GGUFs are split into parts. Most tools such as llama.cpp and those built on it do support split GGUFs,
> pointing the platform to the first split will be sufficient for it to function. In the event where a merge is necessary,
> it can be done using llama.cpp's gguf-split: ./gguf-split --merge ./path/to/first-split ./path/to/output-gguf More details:
> gguf-split guide & [README](https://github.com/ggerganov/llama.cpp/tree/master/examples/gguf-split)
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Test Results
For details on Llama-SEA-LION-v3.5-70B-R performance, please refer to the SEA-HELM leaderboard, [Leaderboard results on SEA-HELM](https://leaderboard.sea-lion.ai/).
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
The model has not been aligned for safety. Developers and users should perform their own safety
fine-tuning and related security measures. In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
*The model was not tested for robustness against adversarial prompting.* It is important for users to be aware that our model exhibits certain limitations that warrant consideration.
Like many LLMs, the model can hallucinate and occasionally generates irrelevant content,
introducing fictional elements that are not grounded in the provided context.
Users should also exercise caution in interpreting and validating the model's responses
due to the potential inconsistencies.
## More Information
This is the repository for the commercial instruction-tuned model.
The model has not been aligned for safety. Developers and users should perform their own safety
fine-tuning and related security measures. In no event shall the authors be held liable
for any claims, damages, or other liabilities arising from the use of the released weights and codes.
[AI Singapore](https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and
do not reflect the views of the National Research Foundation or the National University of Singapore.
[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
For more info, please contact us at sealion@aisingapore.org
## Team
Antonyrex Sajeban, Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Liew Rachel, Limkonchotiwat Peerat, Liu Bing Jie Darius,
Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David,
Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teng Walter,
Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Yeo Yeow Tong, Yong Xianbin
## Contact
sealion@aisingapore.org
|
pepijn223/rlearn_siglip8
|
pepijn223
| 2025-09-01T10:33:51Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"rlearn",
"robotics",
"dataset:pepijn223/phone_pipeline_pickup1",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-09-01T10:33:42Z |
---
datasets: pepijn223/phone_pipeline_pickup1
library_name: lerobot
license: apache-2.0
model_name: rlearn
pipeline_tag: robotics
tags:
- lerobot
- rlearn
- robotics
---
# Model Card for rlearn
<!-- Provide a quick summary of what the model is/does. -->
_Model type not recognized — please update this template._
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
python -m lerobot.scripts.train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
python -m lerobot.record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1756721275
|
vwzyrraz7l
| 2025-09-01T10:33:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:33:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1756721222
|
kojeklollipop
| 2025-09-01T10:33:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:33:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
JYK0820/Qwen3-8B-Base-train-formula-knowledgepoint
|
JYK0820
| 2025-09-01T10:32:53Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"llama-factory",
"license:unknown",
"region:us"
] | null | 2025-09-01T02:38:05Z |
---
license: unknown
tags:
- llama-factory
---
|
zuruyu/blockassist-bc-endangered_pesty_chinchilla_1756722633
|
zuruyu
| 2025-09-01T10:32:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"endangered pesty chinchilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:31:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- endangered pesty chinchilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
arif696/blockassist-bc-regal_spotted_pelican_1756722600
|
arif696
| 2025-09-01T10:31:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:31:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Rahulwale12/SLM
|
Rahulwale12
| 2025-09-01T10:29:38Z | 0 | 0 | null |
[
"pytorch",
"transformer_lite",
"region:us"
] | null | 2025-09-01T10:20:15Z |
# CPU-Optimized Small Language Model (SLM)
## 🚀 Revolutionary CPU-First Conversational AI
This is a **blazing-fast, CPU-optimized Small Language Model** that achieves unprecedented speed and efficiency:
### ⚡ Performance Highlights
- **893 tokens/sec** on CPU (fast production speed)
- **3.7MB model size** (76.6% smaller than original)
- **3.7M parameters** (tiny but powerful)
- **Q&A specialized** (learned conversation patterns)
### 🎯 Training Speed
- **2.35 minutes** for fine-tuning (unheard of!)
- **28 minutes** for base training (4 epochs)
- **Total time:** ~30 minutes from scratch to production
### 🔧 Technical Specs
- **Architecture:** Transformer-lite with RMSNorm, SwiGLU, Rotary embeddings
- **Quantization:** 8-bit post-training quantization
- **Optimization:** CPU-first with memory mapping and efficient batching
- **Framework:** PyTorch (CPU optimized)
### 📱 Deployment Ready
- **Mobile-friendly:** 3.7MB fits in any mobile app
- **No GPU required:** Pure CPU inference
- **Fast startup:** Instant model loading
- **Low memory:** Minimal RAM requirements
## Usage
### Quick Start
```python
from huggingface_hub import hf_hub_download
import torch
import sys
sys.path.append('src') # Add your model code path
from model import create_model_from_config
from tokenizer import BPETokenizer
from quantize import QuantizedModel
# Download model files
model_path = hf_hub_download(repo_id="Rahulwale12/SLM", filename="pytorch_model.bin")
config_path = hf_hub_download(repo_id="Rahulwale12/SLM", filename="config.json")
tokenizer_path = hf_hub_download(repo_id="Rahulwale12/SLM", filename="tokenizer.json")
# Load config
import json
with open(config_path, 'r') as f:
config = json.load(f)
# Create model
model_config = {
'model': {
'vocab_size': config['vocab_size'],
'd_model': config['hidden_size'],
'n_layers': config['num_hidden_layers'],
'n_heads': config['num_attention_heads'],
'd_ff': config['intermediate_size'],
'seq_len': config['max_position_embeddings'],
'dropout': 0.1,
'use_rmsnorm': True,
'use_rotary': True,
'use_swiglu': True
}
}
model = create_model_from_config({'model': model_config['model']})
# Load quantized weights
checkpoint = torch.load(model_path, map_location='cpu')
quantized_model = QuantizedModel(model, checkpoint['quantization_bits'])
quantized_model.quantized_weights = checkpoint['quantized_weights']
quantized_model.scales = checkpoint['scales']
quantized_model.zeros = checkpoint['zeros']
quantized_model.dequantize_weights()
# Load tokenizer
tokenizer = BPETokenizer()
tokenizer.load(tokenizer_path)
# Generate text
prompt = "Question: How are you? Answer:"
input_ids = tokenizer.encode(prompt, add_special_tokens=True)
input_ids = torch.tensor([input_ids], dtype=torch.long)
model.eval()
with torch.no_grad():
for _ in range(20):
logits = model(input_ids)[0, -1, :]
next_token = torch.argmax(logits, dim=-1).unsqueeze(0)
input_ids = torch.cat([input_ids, next_token.unsqueeze(0)], dim=1)
response = tokenizer.decode(input_ids[0].tolist(), skip_special_tokens=True)
print(response)
```
### Complete Usage Guide
Run the comprehensive usage guide:
```bash
python usage_guide.py
```
## Model Details
- **Base Model:** Trained on conversational data
- **Fine-tuning:** Specialized for Q&A conversations
- **Quantization:** 8-bit for optimal speed/size balance
- **License:** MIT
## Performance Comparison
| Model | Speed (tokens/sec) | Size | Training Time |
|-------|-------------------|------|---------------|
| Base | 942 | 45.2MB | 28 min |
| **Fine-tuned** | **893** | **3.7MB** | **2.35 min** |
This model represents a breakthrough in CPU-optimized language models, making conversational AI accessible on any device without requiring specialized hardware.
|
Sonic-man/blockassist-bc-poisonous_graceful_cow_1756720005
|
Sonic-man
| 2025-09-01T10:29:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"poisonous graceful cow",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:29:25Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- poisonous graceful cow
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sekirr/blockassist-bc-masked_tenacious_whale_1756722487
|
sekirr
| 2025-09-01T10:28:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"masked tenacious whale",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:28:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- masked tenacious whale
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
NahedDom/blockassist-bc-flapping_stocky_leopard_1756720195
|
NahedDom
| 2025-09-01T10:28:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flapping stocky leopard",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:28:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flapping stocky leopard
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
KHAMSAMAI/qwen25-3b-lao-dapt-lora
|
KHAMSAMAI
| 2025-09-01T10:28:12Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-3B",
"lora",
"transformers",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B",
"region:us"
] |
text-generation
| 2025-09-01T02:47:16Z |
---
base_model: Qwen/Qwen2.5-3B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-3B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
Satram/Base_QYA_300_Ej
|
Satram
| 2025-09-01T10:27:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T10:27:11Z |
---
base_model: unsloth/llama-3.2-3b-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-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)
|
arif696/blockassist-bc-regal_spotted_pelican_1756722329
|
arif696
| 2025-09-01T10:27:14Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"regal spotted pelican",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-01T10:27:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- regal spotted pelican
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
eternis/eternis_router_sft_0.6b_lora_lora_30Aug
|
eternis
| 2025-09-01T10:26:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"endpoints_compatible",
"region:us"
] | null | 2025-09-01T09:05:24Z |
---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
model_name: eternis_router_sft_0.6b_lora_lora_30Aug
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for eternis_router_sft_0.6b_lora_lora_30Aug
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
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="eternis/eternis_router_sft_0.6b_lora_lora_30Aug", 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/dmayboroda/router/runs/max9mmy8)
This model was trained with SFT.
### Framework versions
- TRL: 0.22.1
- Transformers: 4.56.0
- Pytorch: 2.7.0
- Datasets: 4.0.0
- Tokenizers: 0.22.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.