modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF
|
mradermacher
| 2025-08-19T16:16:35Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:Tavernari/git-commit-message-splitter-Qwen3-4B",
"base_model:quantized:Tavernari/git-commit-message-splitter-Qwen3-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:09:02Z |
---
base_model: Tavernari/git-commit-message-splitter-Qwen3-4B
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## 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/Tavernari/git-commit-message-splitter-Qwen3-4B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#git-commit-message-splitter-Qwen3-4B-GGUF).***
weighted/imatrix quants are available at https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-4B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-4B.f16.gguf) | f16 | 8.2 | 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 -->
|
shopitalic/serene-towel-glacier-rafael
|
shopitalic
| 2025-08-19T16:10:46Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-19T16:10:37Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt:
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
---
# serene towel glacier rafael
<Gallery />
## Model description
## Trigger words
You should use `` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/shopitalic/serene-towel-glacier-rafael/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
mradermacher/sailor2-sft-GGUF
|
mradermacher
| 2025-08-19T16:04:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:hai2131/sailor2-sft",
"base_model:quantized:hai2131/sailor2-sft",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:55:44Z |
---
base_model: hai2131/sailor2-sft
language:
- en
library_name: transformers
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/hai2131/sailor2-sft
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#sailor2-sft-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/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.IQ4_XS.gguf) | IQ4_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_L.gguf) | Q3_K_L | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q4_K_S.gguf) | Q4_K_S | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q5_K_S.gguf) | Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q5_K_M.gguf) | Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q6_K.gguf) | Q6_K | 1.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q8_0.gguf) | Q8_0 | 1.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.f16.gguf) | f16 | 2.1 | 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 -->
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755617041
|
mang3dd
| 2025-08-19T15:52:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:52:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755618608
|
lqpl
| 2025-08-19T15:51:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:50:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
jacoboss/MyGemmaNPC
|
jacoboss
| 2025-08-19T15:48:33Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:google/gemma-3-270m-it",
"base_model:finetune:google/gemma-3-270m-it",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T21:28:50Z |
---
base_model: google/gemma-3-270m-it
library_name: transformers
model_name: MyGemmaNPC
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for MyGemmaNPC
This model is a fine-tuned version of [google/gemma-3-270m-it](https://huggingface.co/google/gemma-3-270m-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="jacoboss/MyGemmaNPC", 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: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
DeathGodlike/Rei-24B-KTO_EXL3
|
DeathGodlike
| 2025-08-19T15:46:54Z | 0 | 0 |
safetensors
|
[
"safetensors",
"KTO",
"roleplaying",
"prose",
"mistral",
"24B",
"exl3",
"4-bit",
"6-bit",
"8-bit",
"text-generation",
"base_model:Delta-Vector/Rei-24B-KTO",
"base_model:quantized:Delta-Vector/Rei-24B-KTO",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-19T15:46:53Z |
---
license: apache-2.0
base_model:
- Delta-Vector/Rei-24B-KTO
base_model_relation: quantized
pipeline_tag: text-generation
library_name: safetensors
tags:
- KTO
- roleplaying
- prose
- mistral
- 24B
- exl3
- 4-bit
- 6-bit
- 8-bit
---
## EXL3 quants: [ [H8-4.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-4.0BPW) | [H8-6.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-6.0BPW) | [H8-8.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-8.0BPW) ]
# Original model: [Rei-24B-KTO](https://huggingface.co/Delta-Vector/Rei-24B-KTO) by [Delta-Vector](https://huggingface.co/Delta-Vector)
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755616339
|
quantumxnode
| 2025-08-19T15:39:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:39:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
indoempatnol/blockassist-bc-fishy_wary_swan_1755615112
|
indoempatnol
| 2025-08-19T15:20:11Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fishy wary swan",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:20:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fishy wary swan
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/360-panorama-sd1.5-flux
|
Muapi
| 2025-08-19T15:15:35Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:15:24Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 360 panorama [SD1.5 / FLUX]
**Base model**: Flux.1 D
**Trained words**: 360, panorama, spherical panorama
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:118398@756096", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/ps1-style-flux
|
Muapi
| 2025-08-19T15:11:21Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T15:11:09Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# PS1 Style Flux
**Base model**: Flux.1 D
**Trained words**: ps1
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:648058@725031", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mradermacher/Nexa-Vector-11-Qwen-GGUF
|
mradermacher
| 2025-08-19T15:09:30Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:iversonzhou/Nexa-Vector-11-Qwen",
"base_model:quantized:iversonzhou/Nexa-Vector-11-Qwen",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T14:56:35Z |
---
base_model: iversonzhou/Nexa-Vector-11-Qwen
language:
- en
library_name: transformers
license: mit
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/iversonzhou/Nexa-Vector-11-Qwen
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#Nexa-Vector-11-Qwen-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/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q2_K.gguf) | Q2_K | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_S.gguf) | Q3_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_M.gguf) | Q3_K_M | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q3_K_L.gguf) | Q3_K_L | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.IQ4_XS.gguf) | IQ4_XS | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q4_K_S.gguf) | Q4_K_S | 1.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q4_K_M.gguf) | Q4_K_M | 1.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q5_K_S.gguf) | Q5_K_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q5_K_M.gguf) | Q5_K_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q6_K.gguf) | Q6_K | 1.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.Q8_0.gguf) | Q8_0 | 1.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Nexa-Vector-11-Qwen-GGUF/resolve/main/Nexa-Vector-11-Qwen.f16.gguf) | f16 | 3.2 | 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 -->
|
NadiaReula/Asistente-DEI
|
NadiaReula
| 2025-08-19T14:56:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | 2025-08-18T20:42:32Z |
---
base_model: google/gemma-2b
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 0.10.0
|
Azurastar2903/gemma-3-1b-pt-rk3588-1.2.1
|
Azurastar2903
| 2025-08-19T14:55:45Z | 0 | 0 |
transformers
|
[
"transformers",
"gemma3_text",
"text-generation",
"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",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T13:41:44Z |
---
library_name: transformers
license: gemma
pipeline_tag: text-generation
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
---
# gemma-3-1b-pt-RK3588-1.2.1
This version of gemma-3-1b-pt has been converted to run on the RK3588 NPU using ['w8a8_g256'] quantization.
This model has been optimized with the following LoRA:
Compatible with RKLLM version: 1.2.1
## Useful links:
[Official RKLLM GitHub](https://github.com/airockchip/rknn-llm)
[RockhipNPU Reddit](https://reddit.com/r/RockchipNPU)
[EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/)
Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531)
Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit
# Original Model Card for base model, gemma-3-1b-pt, below:
# 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.
### 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
```python
from transformers import pipeline
import torch
pipe = pipeline("text-generation", model="google/gemma-3-1b-pt", device="cuda", torch_dtype=torch.bfloat16)
output = pipe("Eiffel tower is located in", max_new_tokens=50)
```
#### Running the model on a single / multi GPU
```python
import torch
from transformers import AutoTokenizer, Gemma3ForCausalLM
ckpt = "google/gemma-3-1b-pt"
tokenizer = AutoTokenizer.from_pretrained(ckpt)
model = Gemma3ForCausalLM.from_pretrained(
ckpt,
torch_dtype=torch.bfloat16,
device_map="auto"
)
prompt = "Eiffel tower is located in"
model_inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
input_len = model_inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**model_inputs, max_new_tokens=50, do_sample=False)
generation = generation[0][input_len:]
decoded = tokenizer.decode(generation, skip_special_tokens=True)
print(decoded)
```
### 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
### 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
|
matheoqtb/EuroBertV2180M_pairs
|
matheoqtb
| 2025-08-19T14:55:16Z | 0 | 0 | null |
[
"safetensors",
"eurobert",
"custom_code",
"region:us"
] | null | 2025-08-19T14:55:03Z |
# Checkpoint exporté: 180M_pairs
Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV2_test2` (sous-dossier `180M_pairs`) et les fichiers de code nécessaires provenant de `EuroBERT/EuroBERT-610m`.
Chargement:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True)
mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True)
Tâche: feature-extraction (embeddings)
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755614706
|
lilTAT
| 2025-08-19T14:45:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T14:45:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Feruru/Classifier
|
Feruru
| 2025-08-19T14:36:48Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T14:35:49Z |
---
license: apache-2.0
---
|
saracandu/dummy
|
saracandu
| 2025-08-19T14:22:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stldec",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-08-19T14:21:52Z |
---
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]
|
Muapi/softserve-anime-flux
|
Muapi
| 2025-08-19T14:06:17Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T14:05:53Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Softserve Anime (Flux)
**Base model**: Flux.1 D
**Trained words**: sftsrv style illustration
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:657191@735293", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
cyt9772/my-bert-fine-tuned1
|
cyt9772
| 2025-08-19T13:51:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T13:50:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### 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]
|
lilTAT/blockassist-bc-gentle_rugged_hare_1755611269
|
lilTAT
| 2025-08-19T13:48:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"gentle rugged hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T13:48:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- gentle rugged hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
meme46/lora-financialqa
|
meme46
| 2025-08-19T13:42:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T13:41:53Z |
---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** meme46
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-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)
|
misterkissi/w2v2-lg-xls-r-300m-oromo
|
misterkissi
| 2025-08-19T13:11:31Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-18T14:29:15Z |
---
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]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755607019
|
Sayemahsjn
| 2025-08-19T12:55:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T12:55:47Z |
---
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).
|
LBST/t10_pick_and_place_smolvla_008000
|
LBST
| 2025-08-19T12:09:16Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"pick-and-place",
"smolvla",
"checkpoint-008000",
"region:us"
] |
robotics
| 2025-08-19T12:09:09Z |
---
library_name: lerobot
tags:
- robotics
- pick-and-place
- smolvla
- checkpoint-008000
---
# T08 Pick and Place Policy - Checkpoint 008000
This model is a checkpoint from the training of a pick-and-place policy using SmolVLA architecture.
## Model Details
- **Checkpoint**: 008000
- **Architecture**: SmolVLA
- **Task**: Pick and Place (T08)
- **Training Step**: 008000
## Usage
You can evaluate this model using LeRobot:
```bash
python -m lerobot.scripts.eval \
--policy.path=LBST/t10_pick_and_place_smolvla_008000 \
--env.type=<your_environment> \
--eval.n_episodes=10 \
--policy.device=cuda
```
## Files
- `config.json`: Policy configuration
- `model.safetensors`: Model weights in SafeTensors format
- `train_config.json`: Complete training configuration for reproducibility
## Parent Repository
This checkpoint was extracted from: [LBST/t10_pick_and_place_files](https://huggingface.co/LBST/t10_pick_and_place_files)
---
*Generated automatically from checkpoint 008000*
|
VoilaRaj/80_cGooIB
|
VoilaRaj
| 2025-08-19T12:06:45Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T12:02:53Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
mohammadmahdinouri/moa-vanilla-init
|
mohammadmahdinouri
| 2025-08-19T11:31:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"ModernALBERT",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-19T11:31:23Z |
---
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]
|
SP4ND4N/Qwen3-0.6B-2025-08-19_15-15-49-fp8-merged
|
SP4ND4N
| 2025-08-19T11:24:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-0.6B",
"base_model:finetune:unsloth/Qwen3-0.6B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T11:18:55Z |
---
base_model: unsloth/Qwen3-0.6B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** SP4ND4N
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Denn231/external_clf_v_0.67
|
Denn231
| 2025-08-19T10:32:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T09:12:32Z |
---
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]
|
pyronear/yolo11s_mighty-mongoose_v5.1.0
|
pyronear
| 2025-08-19T10:31:40Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T10:20:22Z |
---
license: apache-2.0
---
|
Team-Atom/act_record_pp_red001_96_100000
|
Team-Atom
| 2025-08-19T10:28:13Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:Team-Atom/PiPl_red_001",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-19T10:28:00Z |
---
datasets: Team-Atom/PiPl_red_001
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- act
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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
|
ssenos/lantern_fine-tuning-v1
|
ssenos
| 2025-08-19T10:27:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:savasy/bert-base-turkish-sentiment-cased",
"base_model:finetune:savasy/bert-base-turkish-sentiment-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T10:14:31Z |
---
library_name: transformers
base_model: savasy/bert-base-turkish-sentiment-cased
tags:
- generated_from_trainer
model-index:
- name: lantern_fine-tuning-v1
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. -->
# lantern_fine-tuning-v1
This model is a fine-tuned version of [savasy/bert-base-turkish-sentiment-cased](https://huggingface.co/savasy/bert-base-turkish-sentiment-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
lqpl/blockassist-bc-hairy_insectivorous_antelope_1755596805
|
lqpl
| 2025-08-19T09:49:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"hairy insectivorous antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:47:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- hairy insectivorous antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/mystic-enchantress-detail
|
Muapi
| 2025-08-19T09:48:31Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:48:05Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Mystic Enchantress Detail++
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:678808@759829", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
0xaoyama/blockassist-bc-muscular_zealous_gorilla_1755596679
|
0xaoyama
| 2025-08-19T09:45:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"muscular zealous gorilla",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T09:45:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- muscular zealous gorilla
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/old-paper-ce
|
Muapi
| 2025-08-19T09:44:52Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:44:36Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Old Paper - CE
**Base model**: Flux.1 D
**Trained words**: oldpprCE style
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:683184@821195", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Azurastar2903/Llama-3.2-3B-rk3588-1.2.1
|
Azurastar2903
| 2025-08-19T09:28:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T09:27:53Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
license: llama3.2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
extra_gated_prompt: "### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT\n\nLlama 3.2 Version\
\ Release Date: September 25, 2024\n\n“Agreement” means the terms and conditions\
\ for use, reproduction, distribution and modification of the Llama Materials set\
\ forth herein.\n\n“Documentation” means the specifications, manuals and documentation\
\ accompanying Llama 3.2 distributed by Meta at https://llama.meta.com/doc/overview.\n\
\n“Licensee” or “you” means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entity’s behalf),\
\ of the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\n“Llama 3.2”\
\ means the foundational large language models and software and algorithms, including\
\ machine-learning model code, trained model weights, inference-enabling code, training-enabling\
\ code, fine-tuning enabling code and other elements of the foregoing distributed\
\ by Meta at https://www.llama.com/llama-downloads.\n\n“Llama Materials” means,\
\ collectively, Meta’s proprietary Llama 3.2 and Documentation (and any portion\
\ thereof) made available under this Agreement.\n\n“Meta” or “we” means Meta Platforms\
\ Ireland Limited (if you are located in or, if you are an entity, your principal\
\ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if\
\ you are located outside of the EEA or Switzerland). \n\nBy clicking “I Accept”\
\ below or by using or distributing any portion or element of the Llama Materials,\
\ you agree to be bound by this Agreement.\n\n1. License Rights and Redistribution.\n\
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\
\ and royalty-free limited license under Meta’s intellectual property or other rights\
\ owned by Meta embodied in the Llama Materials to use, reproduce, distribute,\
\ copy, create derivative works of, and make modifications to the Llama Materials.\
\ \nb. Redistribution and Use. \ni. If you distribute or make available the Llama\
\ Materials (or any derivative works thereof), or a product or service (including\
\ another AI model) that contains any of them, you shall (A) provide a copy of this\
\ Agreement with any such Llama Materials; and (B) prominently display “Built with\
\ Llama” on a related website, user interface, blogpost, about page, or product\
\ documentation. If you use the Llama Materials or any outputs or results of the\
\ Llama Materials to create, train, fine tune, or otherwise improve an AI model,\
\ which is distributed or made available, you shall also include “Llama” at the\
\ beginning of any such AI model name.\nii. If you receive Llama Materials, or any\
\ derivative works thereof, from a Licensee as part of an integrated end user product,\
\ then Section 2 of this Agreement will not apply to you. \niii. You must retain\
\ in all copies of the Llama Materials that you distribute the following attribution\
\ notice within a “Notice” text file distributed as a part of such copies: “Llama\
\ 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms,\
\ Inc. All Rights Reserved.”\niv. Your use of the Llama Materials must comply with\
\ applicable laws and regulations (including trade compliance laws and regulations)\
\ and adhere to the Acceptable Use Policy for the Llama Materials (available at\
\ https://www.llama.com/llama3_2/use-policy), which is hereby incorporated by reference\
\ into this Agreement.\n \n2. Additional Commercial Terms. If, on the Llama 3.2\
\ version release date, the monthly active users of the products or services made\
\ available by or for Licensee, or Licensee’s affiliates, is greater than 700 million\
\ monthly active users in the preceding calendar month, you must request a license\
\ from Meta, which Meta may grant to you in its sole discretion, and you are not\
\ authorized to exercise any of the rights under this Agreement unless or until\
\ Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS\
\ REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM\
\ ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS\
\ ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION,\
\ ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR\
\ PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING\
\ OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR\
\ USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability.\
\ IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY,\
\ WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING\
\ OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL,\
\ INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE\
\ BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\n\
a. No trademark licenses are granted under this Agreement, and in connection with\
\ the Llama Materials, neither Meta nor Licensee may use any name or mark owned\
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\ alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion\
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\ other rights owned or licensable by you, then any licenses granted to you under\
\ this Agreement shall terminate as of the date such litigation or claim is filed\
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\ claim by any third party arising out of or related to your use or distribution\
\ of the Llama Materials.\n6. Term and Termination. The term of this Agreement will\
\ commence upon your acceptance of this Agreement or access to the Llama Materials\
\ and will continue in full force and effect until terminated in accordance with\
\ the terms and conditions herein. Meta may terminate this Agreement if you are\
\ in breach of any term or condition of this Agreement. Upon termination of this\
\ Agreement, you shall delete and cease use of the Llama Materials. Sections 3,\
\ 4 and 7 shall survive the termination of this Agreement. \n7. Governing Law and\
\ Jurisdiction. This Agreement will be governed and construed under the laws of\
\ the State of California without regard to choice of law principles, and the UN\
\ Convention on Contracts for the International Sale of Goods does not apply to\
\ this Agreement. The courts of California shall have exclusive jurisdiction of\
\ any dispute arising out of this Agreement. \n### Llama 3.2 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.2. If you access or use Llama 3.2, you agree to this Acceptable Use Policy\
\ (“**Policy**”). The most recent copy of this policy can be found at [https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).\n\
#### Prohibited Uses\nWe want everyone to use Llama 3.2 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.2 to:\n1. Violate\
\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\
\ contribute to, encourage, plan, incite, or further illegal or unlawful activity\
\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\
\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
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\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 1. Engage in, promote, incite, or facilitate the\
\ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
\ 2. Engage in, promote, incite, or facilitate discrimination or other unlawful\
\ or harmful conduct in the provision of employment, employment benefits, credit,\
\ housing, other economic benefits, or other essential goods and services\n 3.\
\ Engage in the unauthorized or unlicensed practice of any profession including,\
\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 4. Collect, process, disclose, generate, or infer private or sensitive\
\ information about individuals, including information about individuals’ identity,\
\ health, or demographic information, unless you have obtained the right to do so\
\ in accordance with applicable law\n 5. Engage in or facilitate any action or\
\ generate any content that infringes, misappropriates, or otherwise violates any\
\ third-party rights, including the outputs or results of any products or services\
\ using the Llama Materials\n 6. Create, generate, or facilitate the creation\
\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system\n 7. Engage in any action, or\
\ facilitate any action, to intentionally circumvent or remove usage restrictions\
\ or other safety measures, or to enable functionality disabled by Meta \n2. Engage\
\ in, promote, incite, facilitate, or assist in the planning or development of activities\
\ that present a risk of death or bodily harm to individuals, including use of Llama\
\ 3.2 related to the following:\n 8. Military, warfare, nuclear industries or\
\ applications, espionage, use for materials or activities that are subject to the\
\ International Traffic Arms Regulations (ITAR) maintained by the United States\
\ Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989\
\ or the Chemical Weapons Convention Implementation Act of 1997\n 9. Guns and\
\ illegal weapons (including weapon development)\n 10. Illegal drugs and regulated/controlled\
\ substances\n 11. Operation of critical infrastructure, transportation technologies,\
\ or heavy machinery\n 12. Self-harm or harm to others, including suicide, cutting,\
\ and eating disorders\n 13. Any content intended to incite or promote violence,\
\ abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive\
\ or mislead others, including use of Llama 3.2 related to the following:\n 14.\
\ Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n\
\ 15. Generating, promoting, or furthering defamatory content, including the\
\ creation of defamatory statements, images, or other content\n 16. Generating,\
\ promoting, or further distributing spam\n 17. Impersonating another individual\
\ without consent, authorization, or legal right\n 18. Representing that the\
\ use of Llama 3.2 or outputs are human-generated\n 19. Generating or facilitating\
\ false online engagement, including fake reviews and other means of fake online\
\ engagement \n4. Fail to appropriately disclose to end users any known dangers\
\ of your AI system 5. Interact with third party tools, models, or software designed\
\ to generate unlawful content or engage in unlawful or harmful conduct and/or represent\
\ that the outputs of such tools, models, or software are associated with Meta or\
\ Llama 3.2\n\nWith respect to any multimodal models included in Llama 3.2, the\
\ rights granted under Section 1(a) of the Llama 3.2 Community License Agreement\
\ are not being granted to you if you are an individual domiciled in, or a company\
\ with a principal place of business in, the European Union. This restriction does\
\ not apply to end users of a product or service that incorporates any such multimodal\
\ models.\n\nPlease report any violation of this Policy, software “bug,” or other\
\ problems that could lead to a violation of this Policy through one of the following\
\ means:\n\n* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)\n\
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# Llama-3.2-3B-RK3588-1.2.1
This version of Llama-3.2-3B has been converted to run on the RK3588 NPU using w8a8 quantization.
This model has been optimized with the following LoRA:
Compatible with RKLLM version: 1.2.1
## Useful links:
[Official RKLLM GitHub](https://github.com/airockchip/rknn-llm)
[RockhipNPU Reddit](https://reddit.com/r/RockchipNPU)
[EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/)
Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531)
Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit
# Original Model Card for base model, Llama-3.2-3B, below:
## Model Information
The 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.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**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 Model Family:** 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).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-3B, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via pip install --upgrade transformers.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-3B"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
pipe("The key to life is")
```
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-3B --include "original/*" --local-dir Llama-3.2-3B
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
Muapi/flux-futuristic-portraits-lora
|
Muapi
| 2025-08-19T09:22:25Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-19T09:22:04Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Flux Futuristic Portraits LoRA
**Base model**: Flux.1 D
**Trained words**: futuristicportrait
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:716982@801785", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
kiendt/phobert-ner-address
|
kiendt
| 2025-08-19T09:10:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"token-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base",
"base_model:finetune:vinai/phobert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-08-19T09:02:55Z |
---
library_name: transformers
license: mit
base_model: vinai/phobert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: ner_phobert
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. -->
# phobert-ner-address
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on an vietnamese address datasets.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Precision: 0.9363
- Recall: 0.9453
- F1: 0.9407
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
rk2357281/llama32-bhojpuri-translator
|
rk2357281
| 2025-08-19T08:58:40Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T08:51:20Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** rk2357281
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AXERA-TECH/RAG.axera
|
AXERA-TECH
| 2025-08-19T08:56:11Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-08-19T08:24:55Z |
---
license: mit
---
# RAG.AXERA DEMO
## 项目说明
```sh
(hf) ➜ rag.axera git:(main) ✗ tree -L 2
.
├── assets
│ └── demo.png
├── config.py # 配置 axmodel, tokenizer 文件路径
├── data
├── gui.py # RAG 交互式程序
├── index # 文档编码向量索引保存位置
│ ├── docs.index
│ └── docs.pkl
├── llm_api.py # llm 主程序
├── models # axmodel 模型存储位置
│ ├── Qwen2.5-1.5B-Instruct_axmodel
│ └── Qwen3-Embedding-0.6B_axmodel
├── pdf_sample # 示例 pdf 文件
│ └── introduction.pdf
├── rag_engine.py # 文档向量编码程序
├── README.md
├── requirements.txt
├── tokenizer
│ ├── Qwen2.5-1.5B-Instruct
│ └── Qwen3-Embedding-0.6B
└── utils
└── infer_func.py
11 directories, 11 files
```
## 运行
在 `AXCL` 机器或 `AX650` 开发板上启动两个终端界面, 分别运行下面的命令:
```sh
python3 llm_api.py # 在 AX650 或 AXCL 开发板启动 llm 服务
python3 gui.py # 启动交互式界面
```
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755591017
|
hakimjustbao
| 2025-08-19T08:37:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:37:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
IvanJAjebu/blockassist-bc-thorny_slender_capybara_1755592441
|
IvanJAjebu
| 2025-08-19T08:35:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"thorny slender capybara",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T08:35:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- thorny slender capybara
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
donoway/ARC-Easy_Llama-3.2-1B-ufd34f01
|
donoway
| 2025-08-19T07:41:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T07:28:27Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ARC-Easy_Llama-3.2-1B-ufd34f01
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. -->
# ARC-Easy_Llama-3.2-1B-ufd34f01
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0193
- Model Preparation Time: 0.0061
- Mdl: 2482.8831
- Accumulated Loss: 1721.0034
- Correct Preds: 359.0
- Total Preds: 570.0
- Accuracy: 0.6298
- Correct Gen Preds: 352.0
- Gen Accuracy: 0.6175
- Correct Gen Preds 32: 124.0
- Correct Preds 32: 126.0
- Total Labels 32: 158.0
- Accuracy 32: 0.7975
- Gen Accuracy 32: 0.7848
- Correct Gen Preds 33: 110.0
- Correct Preds 33: 110.0
- Total Labels 33: 152.0
- Accuracy 33: 0.7237
- Gen Accuracy 33: 0.7237
- Correct Gen Preds 34: 81.0
- Correct Preds 34: 85.0
- Total Labels 34: 142.0
- Accuracy 34: 0.5986
- Gen Accuracy 34: 0.5704
- Correct Gen Preds 35: 37.0
- Correct Preds 35: 38.0
- Total Labels 35: 118.0
- Accuracy 35: 0.3220
- Gen Accuracy 35: 0.3136
- Correct Gen Preds 36: 0.0
- Correct Preds 36: 0.0
- Total Labels 36: 0.0
- Accuracy 36: 0.0
- Gen Accuracy 36: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 112
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|
| No log | 0 | 0 | 1.5354 | 0.0061 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5057 | 1.0 | 1 | 1.5354 | 0.0061 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.5112 | 2.0 | 2 | 2.5224 | 0.0061 | 2074.2721 | 1437.7759 | 226.0 | 570.0 | 0.3965 | 226.0 | 0.3965 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 123.0 | 123.0 | 152.0 | 0.8092 | 0.8092 | 103.0 | 103.0 | 142.0 | 0.7254 | 0.7254 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.7603 | 3.0 | 3 | 1.3081 | 0.0061 | 1075.7123 | 745.6270 | 227.0 | 570.0 | 0.3982 | 227.0 | 0.3982 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 140.0 | 140.0 | 152.0 | 0.9211 | 0.9211 | 49.0 | 49.0 | 142.0 | 0.3451 | 0.3451 | 38.0 | 38.0 | 118.0 | 0.3220 | 0.3220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.5467 | 4.0 | 4 | 1.3336 | 0.0061 | 1096.7059 | 760.1786 | 334.0 | 570.0 | 0.5860 | 332.0 | 0.5825 | 74.0 | 76.0 | 158.0 | 0.4810 | 0.4684 | 128.0 | 128.0 | 152.0 | 0.8421 | 0.8421 | 89.0 | 89.0 | 142.0 | 0.6268 | 0.6268 | 41.0 | 41.0 | 118.0 | 0.3475 | 0.3475 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0251 | 5.0 | 5 | 2.4343 | 0.0061 | 2001.8424 | 1387.5714 | 348.0 | 570.0 | 0.6105 | 340.0 | 0.5965 | 110.0 | 116.0 | 158.0 | 0.7342 | 0.6962 | 111.0 | 111.0 | 152.0 | 0.7303 | 0.7303 | 81.0 | 83.0 | 142.0 | 0.5845 | 0.5704 | 38.0 | 38.0 | 118.0 | 0.3220 | 0.3220 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 6.0 | 6 | 3.0193 | 0.0061 | 2482.8831 | 1721.0034 | 359.0 | 570.0 | 0.6298 | 352.0 | 0.6175 | 124.0 | 126.0 | 158.0 | 0.7975 | 0.7848 | 110.0 | 110.0 | 152.0 | 0.7237 | 0.7237 | 81.0 | 85.0 | 142.0 | 0.5986 | 0.5704 | 37.0 | 38.0 | 118.0 | 0.3220 | 0.3136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 7.0 | 7 | 3.2734 | 0.0061 | 2691.8214 | 1865.8284 | 358.0 | 570.0 | 0.6281 | 346.0 | 0.6070 | 124.0 | 126.0 | 158.0 | 0.7975 | 0.7848 | 110.0 | 110.0 | 152.0 | 0.7237 | 0.7237 | 75.0 | 83.0 | 142.0 | 0.5845 | 0.5282 | 37.0 | 39.0 | 118.0 | 0.3305 | 0.3136 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 8.0 | 8 | 3.4379 | 0.0061 | 2827.1193 | 1959.6098 | 353.0 | 570.0 | 0.6193 | 338.0 | 0.5930 | 124.0 | 126.0 | 158.0 | 0.7975 | 0.7848 | 105.0 | 108.0 | 152.0 | 0.7105 | 0.6908 | 73.0 | 82.0 | 142.0 | 0.5775 | 0.5141 | 36.0 | 37.0 | 118.0 | 0.3136 | 0.3051 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 9.0 | 9 | 3.5233 | 0.0061 | 2897.3586 | 2008.2960 | 350.0 | 570.0 | 0.6140 | 330.0 | 0.5789 | 124.0 | 126.0 | 158.0 | 0.7975 | 0.7848 | 102.0 | 105.0 | 152.0 | 0.6908 | 0.6711 | 69.0 | 82.0 | 142.0 | 0.5775 | 0.4859 | 35.0 | 37.0 | 118.0 | 0.3136 | 0.2966 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 10 | 3.6239 | 0.0061 | 2980.0968 | 2065.6457 | 342.0 | 570.0 | 0.6 | 321.0 | 0.5632 | 125.0 | 127.0 | 158.0 | 0.8038 | 0.7911 | 101.0 | 103.0 | 152.0 | 0.6776 | 0.6645 | 67.0 | 79.0 | 142.0 | 0.5563 | 0.4718 | 28.0 | 33.0 | 118.0 | 0.2797 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 11 | 3.6994 | 0.0061 | 3042.1240 | 2108.6397 | 343.0 | 570.0 | 0.6018 | 321.0 | 0.5632 | 126.0 | 128.0 | 158.0 | 0.8101 | 0.7975 | 101.0 | 104.0 | 152.0 | 0.6842 | 0.6645 | 64.0 | 77.0 | 142.0 | 0.5423 | 0.4507 | 30.0 | 34.0 | 118.0 | 0.2881 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 12 | 3.7327 | 0.0061 | 3069.5408 | 2127.6436 | 340.0 | 570.0 | 0.5965 | 314.0 | 0.5509 | 125.0 | 128.0 | 158.0 | 0.8101 | 0.7911 | 95.0 | 101.0 | 152.0 | 0.6645 | 0.625 | 65.0 | 77.0 | 142.0 | 0.5423 | 0.4577 | 29.0 | 34.0 | 118.0 | 0.2881 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 13 | 3.7788 | 0.0061 | 3107.4349 | 2153.9098 | 337.0 | 570.0 | 0.5912 | 310.0 | 0.5439 | 124.0 | 128.0 | 158.0 | 0.8101 | 0.7848 | 95.0 | 99.0 | 152.0 | 0.6513 | 0.625 | 62.0 | 75.0 | 142.0 | 0.5282 | 0.4366 | 29.0 | 35.0 | 118.0 | 0.2966 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 14 | 3.8434 | 0.0061 | 3160.5389 | 2190.7186 | 333.0 | 570.0 | 0.5842 | 306.0 | 0.5368 | 123.0 | 127.0 | 158.0 | 0.8038 | 0.7785 | 94.0 | 99.0 | 152.0 | 0.6513 | 0.6184 | 62.0 | 75.0 | 142.0 | 0.5282 | 0.4366 | 27.0 | 32.0 | 118.0 | 0.2712 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 15 | 3.8505 | 0.0061 | 3166.4407 | 2194.8094 | 335.0 | 570.0 | 0.5877 | 310.0 | 0.5439 | 123.0 | 127.0 | 158.0 | 0.8038 | 0.7785 | 96.0 | 99.0 | 152.0 | 0.6513 | 0.6316 | 63.0 | 77.0 | 142.0 | 0.5423 | 0.4437 | 28.0 | 32.0 | 118.0 | 0.2712 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 16.0 | 16 | 3.8673 | 0.0061 | 3180.2457 | 2204.3783 | 336.0 | 570.0 | 0.5895 | 307.0 | 0.5386 | 124.0 | 128.0 | 158.0 | 0.8101 | 0.7848 | 93.0 | 98.0 | 152.0 | 0.6447 | 0.6118 | 62.0 | 77.0 | 142.0 | 0.5423 | 0.4366 | 28.0 | 33.0 | 118.0 | 0.2797 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 17.0 | 17 | 3.8947 | 0.0061 | 3202.7656 | 2219.9880 | 337.0 | 570.0 | 0.5912 | 309.0 | 0.5421 | 124.0 | 129.0 | 158.0 | 0.8165 | 0.7848 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 64.0 | 77.0 | 142.0 | 0.5423 | 0.4507 | 27.0 | 33.0 | 118.0 | 0.2797 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 18.0 | 18 | 3.9091 | 0.0061 | 3214.5717 | 2228.1713 | 336.0 | 570.0 | 0.5895 | 307.0 | 0.5386 | 124.0 | 129.0 | 158.0 | 0.8165 | 0.7848 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 63.0 | 77.0 | 142.0 | 0.5423 | 0.4437 | 26.0 | 32.0 | 118.0 | 0.2712 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 19.0 | 19 | 3.9319 | 0.0061 | 3233.3715 | 2241.2023 | 338.0 | 570.0 | 0.5930 | 312.0 | 0.5474 | 124.0 | 130.0 | 158.0 | 0.8228 | 0.7848 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 67.0 | 78.0 | 142.0 | 0.5493 | 0.4718 | 27.0 | 32.0 | 118.0 | 0.2712 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 20.0 | 20 | 3.9430 | 0.0061 | 3242.4962 | 2247.5271 | 338.0 | 570.0 | 0.5930 | 312.0 | 0.5474 | 124.0 | 130.0 | 158.0 | 0.8228 | 0.7848 | 95.0 | 98.0 | 152.0 | 0.6447 | 0.625 | 64.0 | 76.0 | 142.0 | 0.5352 | 0.4507 | 29.0 | 34.0 | 118.0 | 0.2881 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 21.0 | 21 | 3.9561 | 0.0061 | 3253.2053 | 2254.9501 | 335.0 | 570.0 | 0.5877 | 311.0 | 0.5456 | 125.0 | 130.0 | 158.0 | 0.8228 | 0.7911 | 95.0 | 98.0 | 152.0 | 0.6447 | 0.625 | 64.0 | 75.0 | 142.0 | 0.5282 | 0.4507 | 27.0 | 32.0 | 118.0 | 0.2712 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 22.0 | 22 | 3.9402 | 0.0061 | 3240.1303 | 2245.8872 | 341.0 | 570.0 | 0.5982 | 314.0 | 0.5509 | 127.0 | 133.0 | 158.0 | 0.8418 | 0.8038 | 94.0 | 97.0 | 152.0 | 0.6382 | 0.6184 | 66.0 | 78.0 | 142.0 | 0.5493 | 0.4648 | 27.0 | 33.0 | 118.0 | 0.2797 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 23.0 | 23 | 3.9841 | 0.0061 | 3276.2708 | 2270.9379 | 337.0 | 570.0 | 0.5912 | 309.0 | 0.5421 | 123.0 | 131.0 | 158.0 | 0.8291 | 0.7785 | 93.0 | 97.0 | 152.0 | 0.6382 | 0.6118 | 65.0 | 76.0 | 142.0 | 0.5352 | 0.4577 | 28.0 | 33.0 | 118.0 | 0.2797 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 24.0 | 24 | 3.9801 | 0.0061 | 3272.9577 | 2268.6414 | 341.0 | 570.0 | 0.5982 | 314.0 | 0.5509 | 126.0 | 132.0 | 158.0 | 0.8354 | 0.7975 | 93.0 | 97.0 | 152.0 | 0.6382 | 0.6118 | 65.0 | 78.0 | 142.0 | 0.5493 | 0.4577 | 30.0 | 34.0 | 118.0 | 0.2881 | 0.2542 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 25.0 | 25 | 3.9653 | 0.0061 | 3260.8094 | 2260.2208 | 336.0 | 570.0 | 0.5895 | 308.0 | 0.5404 | 123.0 | 131.0 | 158.0 | 0.8291 | 0.7785 | 94.0 | 97.0 | 152.0 | 0.6382 | 0.6184 | 64.0 | 75.0 | 142.0 | 0.5282 | 0.4507 | 27.0 | 33.0 | 118.0 | 0.2797 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 26.0 | 26 | 3.9697 | 0.0061 | 3264.4225 | 2262.7253 | 339.0 | 570.0 | 0.5947 | 312.0 | 0.5474 | 126.0 | 133.0 | 158.0 | 0.8418 | 0.7975 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 63.0 | 74.0 | 142.0 | 0.5211 | 0.4437 | 29.0 | 34.0 | 118.0 | 0.2881 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 27.0 | 27 | 3.9600 | 0.0061 | 3256.4563 | 2257.2035 | 340.0 | 570.0 | 0.5965 | 312.0 | 0.5474 | 122.0 | 129.0 | 158.0 | 0.8165 | 0.7722 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 67.0 | 79.0 | 142.0 | 0.5563 | 0.4718 | 29.0 | 34.0 | 118.0 | 0.2881 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 28.0 | 28 | 3.9808 | 0.0061 | 3273.5887 | 2269.0788 | 340.0 | 570.0 | 0.5965 | 312.0 | 0.5474 | 125.0 | 132.0 | 158.0 | 0.8354 | 0.7911 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 65.0 | 76.0 | 142.0 | 0.5352 | 0.4577 | 28.0 | 34.0 | 118.0 | 0.2881 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 29.0 | 29 | 3.9802 | 0.0061 | 3273.0894 | 2268.7327 | 335.0 | 570.0 | 0.5877 | 307.0 | 0.5386 | 124.0 | 132.0 | 158.0 | 0.8354 | 0.7848 | 93.0 | 97.0 | 152.0 | 0.6382 | 0.6118 | 63.0 | 74.0 | 142.0 | 0.5211 | 0.4437 | 27.0 | 32.0 | 118.0 | 0.2712 | 0.2288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 30.0 | 30 | 3.9740 | 0.0061 | 3267.9798 | 2265.1910 | 338.0 | 570.0 | 0.5930 | 309.0 | 0.5421 | 124.0 | 130.0 | 158.0 | 0.8228 | 0.7848 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 65.0 | 77.0 | 142.0 | 0.5423 | 0.4577 | 26.0 | 33.0 | 118.0 | 0.2797 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 31.0 | 31 | 3.9859 | 0.0061 | 3277.7667 | 2271.9748 | 339.0 | 570.0 | 0.5947 | 310.0 | 0.5439 | 123.0 | 131.0 | 158.0 | 0.8291 | 0.7785 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 65.0 | 76.0 | 142.0 | 0.5352 | 0.4577 | 28.0 | 34.0 | 118.0 | 0.2881 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 32.0 | 32 | 3.9724 | 0.0061 | 3266.6221 | 2264.2499 | 339.0 | 570.0 | 0.5947 | 310.0 | 0.5439 | 124.0 | 131.0 | 158.0 | 0.8291 | 0.7848 | 93.0 | 97.0 | 152.0 | 0.6382 | 0.6118 | 65.0 | 77.0 | 142.0 | 0.5423 | 0.4577 | 28.0 | 34.0 | 118.0 | 0.2881 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 33.0 | 33 | 3.9885 | 0.0061 | 3279.8737 | 2273.4352 | 340.0 | 570.0 | 0.5965 | 309.0 | 0.5421 | 124.0 | 131.0 | 158.0 | 0.8291 | 0.7848 | 93.0 | 97.0 | 152.0 | 0.6382 | 0.6118 | 66.0 | 79.0 | 142.0 | 0.5563 | 0.4648 | 26.0 | 33.0 | 118.0 | 0.2797 | 0.2203 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 34.0 | 34 | 3.9903 | 0.0061 | 3281.3424 | 2274.4533 | 339.0 | 570.0 | 0.5947 | 313.0 | 0.5491 | 124.0 | 130.0 | 158.0 | 0.8228 | 0.7848 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 66.0 | 77.0 | 142.0 | 0.5423 | 0.4648 | 29.0 | 34.0 | 118.0 | 0.2881 | 0.2458 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 35.0 | 35 | 3.9920 | 0.0061 | 3282.7598 | 2275.4357 | 339.0 | 570.0 | 0.5947 | 311.0 | 0.5456 | 124.0 | 131.0 | 158.0 | 0.8291 | 0.7848 | 94.0 | 98.0 | 152.0 | 0.6447 | 0.6184 | 65.0 | 77.0 | 142.0 | 0.5423 | 0.4577 | 28.0 | 33.0 | 118.0 | 0.2797 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 36.0 | 36 | 3.9818 | 0.0061 | 3274.3896 | 2269.6339 | 339.0 | 570.0 | 0.5947 | 310.0 | 0.5439 | 123.0 | 131.0 | 158.0 | 0.8291 | 0.7785 | 93.0 | 97.0 | 152.0 | 0.6382 | 0.6118 | 66.0 | 78.0 | 142.0 | 0.5493 | 0.4648 | 28.0 | 33.0 | 118.0 | 0.2797 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Suprim003/a2c-PandaReachDense-v3
|
Suprim003
| 2025-08-19T07:19:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-19T07:10:36Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.19 +/- 0.08
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Soham711/blenderbot-400M-friendly-chatmodel
|
Soham711
| 2025-08-19T07:05:17Z | 0 | 1 | null |
[
"safetensors",
"blenderbot",
"text2text-generation",
"conversational",
"en",
"base_model:facebook/blenderbot-400M-distill",
"base_model:finetune:facebook/blenderbot-400M-distill",
"license:mit",
"region:us"
] | null | 2025-08-19T06:48:35Z |
---
license: mit
language:
- en
base_model:
- facebook/blenderbot-400M-distill
tags:
- text2text-generation
- conversational
---
|
jtekt-physical-ai/lerobot_actv2
|
jtekt-physical-ai
| 2025-08-19T06:59:15Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"robotics",
"act",
"dataset:yurayuray/retainer_mizoguchi3",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-19T05:57:00Z |
---
datasets: yurayuray/retainer_mizoguchi3
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- lerobot
- robotics
- act
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
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
lerobot-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
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
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755584966
|
mang3dd
| 2025-08-19T06:57:47Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T06:57:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
resistz/sft_Qwen3-4B-Base_ultra200k
|
resistz
| 2025-08-19T06:53:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T06:49:25Z |
---
library_name: transformers
model_name: sft_Qwen3-4B-Base_ultra200k
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for sft_Qwen3-4B-Base_ultra200k
This model is a fine-tuned version of [None](https://huggingface.co/None).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", 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/resistzzz97/Alignment_Influence/runs/eswkk8st)
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.7.1
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
kyoukarawattsu/blockassist-bc-tenacious_arctic_manatee_1755584802
|
kyoukarawattsu
| 2025-08-19T06:28:24Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tenacious arctic manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T06:28:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tenacious arctic manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF
|
gmonsoon
| 2025-08-19T05:56:15Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:gmonsoon/Qwen3-4b-REnewbie-NEXT",
"base_model:quantized:gmonsoon/Qwen3-4b-REnewbie-NEXT",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T05:56:02Z |
---
base_model: gmonsoon/Qwen3-4b-REnewbie-NEXT
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF
This model was converted to GGUF format from [`gmonsoon/Qwen3-4b-REnewbie-NEXT`](https://huggingface.co/gmonsoon/Qwen3-4b-REnewbie-NEXT) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/gmonsoon/Qwen3-4b-REnewbie-NEXT) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo gmonsoon/Qwen3-4b-REnewbie-NEXT-Q4_K_M-GGUF --hf-file qwen3-4b-renewbie-next-q4_k_m.gguf -c 2048
```
|
jasminekitty328/full_3000_intentconan
|
jasminekitty328
| 2025-08-19T03:23:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T03:23:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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|
JWHaHa/exaone3.5-7.8B-Llamafied-inst-SCGF
|
JWHaHa
| 2025-08-19T02:01:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:beomi/EXAONE-3.5-7.8B-Instruct-Llamafied",
"base_model:finetune:beomi/EXAONE-3.5-7.8B-Instruct-Llamafied",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T02:01:04Z |
---
base_model: beomi/EXAONE-3.5-7.8B-Instruct-Llamafied
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** JWHaHa
- **License:** apache-2.0
- **Finetuned from model :** beomi/EXAONE-3.5-7.8B-Instruct-Llamafied
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)
|
finalform/foamQwen2.5-Coder-7B-Instruct
|
finalform
| 2025-08-19T01:57:09Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"lora",
"sft",
"transformers",
"trl",
"text-generation",
"conversational",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"region:us"
] |
text-generation
| 2025-08-18T22:40:16Z |
---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
- lora
- sft
- transformers
- trl
---
# 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 0.17.0
|
oddadmix/arabic-summarization
|
oddadmix
| 2025-08-19T01:52:34Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"lfm2",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"ar",
"dataset:oddadmix/arabic-news-summarization",
"base_model:LiquidAI/LFM2-350M",
"base_model:finetune:LiquidAI/LFM2-350M",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T23:26:07Z |
---
base_model: LiquidAI/LFM2-350M
library_name: transformers
model_name: lfm2-sft-summary
tags:
- generated_from_trainer
- sft
- trl
licence: license
datasets:
- oddadmix/arabic-news-summarization
language:
- ar
---
# 📝 نموذج التلخيص العربي
هذا المشروع يقدّم نموذج **تلخيص نصوص باللغة العربية** مبني على النموذج الأساسي [LiquidAI/LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M)، وتمت إعادة تدريبه (Fine-tuning) على **مجموعة بيانات مكوّنة من 17,000 سجل** لتلخيص النصوص بدقة وكفاءة عالية.
---
## ⚡ المميزات
* ✅ أداء قوي جدًا في تلخيص النصوص العربية.
* ✅ يحافظ على المعنى العام للنص مع اختصار الحجم.
* ✅ يمكن استخدامه في تلخيص المقالات، الأخبار، الأبحاث، والمستندات الطويلة.
* ✅ مبني على نموذج قوي مفتوح المصدر مع إعادة ضبط دقيقة (Fine-tuning).
---
## 🛠️ البيانات
تم تدريب النموذج باستخدام **17,000 صف** من البيانات عالية الجودة التي تحتوي على نصوص عربية وأهداف التلخيص المقابلة لها.
هذا ساعد في تحسين دقة النموذج وجعله قادرًا على إنتاج **ملخصات متماسكة وسلسة**.
---
## 🚀 كيفية الاستخدام
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# تحميل النموذج والمحول
model_name = "اسم-المستخدم/arabic-summarization-model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# إدخال نص للتلخيص
text = """النص العربي المراد تلخيصه ..."""
inputs = tokenizer(text, return_tensors="pt", max_length=1024, truncation=True)
summary_ids = model.generate(inputs["input_ids"], max_length=150, min_length=40, length_penalty=2.0, num_beams=4)
# عرض الملخص
print(tokenizer.decode(summary_ids[0], skip_special_tokens=True))
```
---
## 📊 الأداء
النموذج أظهر نتائج ممتازة في التجارب الداخلية على مقاييس **الدقة، التماسك، والمحافظة على المعنى**.
أداؤه يُعتبر **جيد جدًا مقارنة بالنماذج المشابهة** في مجال تلخيص النصوص العربية.
---
## 📌 ملاحظات
* النموذج ما زال قابلًا للتطوير عبر تدريبه على بيانات إضافية.
* يُفضّل استخدامه مع نصوص عربية فصيحة، مع أنه يعمل بشكل جيد أيضًا مع بعض اللهجات.
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755566416
|
mang3dd
| 2025-08-19T01:46:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T01:46:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lordvader2009/medgemma-4b-it-oasisii-aggregate-x1
|
lordvader2009
| 2025-08-19T01:07:22Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T23:16:41Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-oasisii-aggregate-x1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-oasisii-aggregate-x1
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="lordvader2009/medgemma-4b-it-oasisii-aggregate-x1", 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: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## 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}}
}
```
|
unitova/blockassist-bc-zealous_sneaky_raven_1755559911
|
unitova
| 2025-08-18T23:56:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T23:56:43Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Kelvin1616/Akuka
|
Kelvin1616
| 2025-08-18T23:02:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-18T23:02:15Z |
---
license: apache-2.0
---
|
seraphimzzzz/1392416
|
seraphimzzzz
| 2025-08-18T22:58:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T22:58:39Z |
[View on Civ Archive](https://civarchive.com/models/1144518?modelVersionId=1492404)
|
crystalline7/1415608
|
crystalline7
| 2025-08-18T22:36:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T22:35:58Z |
[View on Civ Archive](https://civarchive.com/models/1341796?modelVersionId=1515323)
|
ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3
|
ArtusDev
| 2025-08-18T21:08:13Z | 0 | 0 | null |
[
"exl3",
"base_model:TheDrummer/Cydonia-24B-v4.1",
"base_model:quantized:TheDrummer/Cydonia-24B-v4.1",
"region:us"
] | null | 2025-08-18T18:11:32Z |
---
base_model: TheDrummer/Cydonia-24B-v4.1
base_model_relation: quantized
quantized_by: ArtusDev
tags:
- exl3
---
## EXL3 Quants of TheDrummer/Cydonia-24B-v4.1
EXL3 quants of [TheDrummer/Cydonia-24B-v4.1](https://huggingface.co/TheDrummer/Cydonia-24B-v4.1) using <a href="https://github.com/turboderp-org/exllamav3/">exllamav3</a> for quantization.
### Quants
| Quant(Revision) | Bits per Weight | Head Bits |
| -------- | ---------- | --------- |
| [2.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/2.5bpw_H6) | 2.5 | 6 |
| [3.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/3.0bpw_H6) | 3.0 | 6 |
| [3.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/3.5bpw_H6) | 3.5 | 6 |
| [4.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/4.0bpw_H6) | 4.0 | 6 |
| [4.5_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/4.5bpw_H6) | 4.5 | 6 |
| [5.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/5.0bpw_H6) | 5.0 | 6 |
| [6.0_H6](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/6.0bpw_H6) | 6.0 | 6 |
| [8.0_H8](https://huggingface.co/ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3/tree/8.0bpw_H8) | 8.0 | 8 |
### Downloading quants with huggingface-cli
<details>
<summary>Click to view download instructions</summary>
Install hugginface-cli:
```bash
pip install -U "huggingface_hub[cli]"
```
Download quant by targeting the specific quant revision (branch):
```
huggingface-cli download ArtusDev/TheDrummer_Cydonia-24B-v4.1-EXL3 --revision "5.0bpw_H6" --local-dir ./
```
</details>
|
seraphimzzzz/1505136
|
seraphimzzzz
| 2025-08-18T21:07:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T21:07:22Z |
[View on Civ Archive](https://civarchive.com/models/1420035?modelVersionId=1605085)
|
tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF
|
tensorblock
| 2025-08-18T17:15:07Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"TensorBlock",
"GGUF",
"text-generation",
"base_model:inclusionAI/AReaL-boba-2-8B",
"base_model:quantized:inclusionAI/AReaL-boba-2-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-08-18T15:42:33Z |
---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model: inclusionAI/AReaL-boba-2-8B
tags:
- TensorBlock
- GGUF
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](https://t.me/TensorBlock)
## inclusionAI/AReaL-boba-2-8B - GGUF
<div style="text-align: left; margin: 20px 0;">
<a href="https://discord.com/invite/Ej5NmeHFf2" style="display: inline-block; padding: 10px 20px; background-color: #5865F2; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
Join our Discord to learn more about what we're building ↗
</a>
</div>
This repo contains GGUF format model files for [inclusionAI/AReaL-boba-2-8B](https://huggingface.co/inclusionAI/AReaL-boba-2-8B).
The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5753](https://github.com/ggml-org/llama.cpp/commit/73e53dc834c0a2336cd104473af6897197b96277).
## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
<th colspan="2" style="font-size: 25px;">Forge</th>
</tr>
<tr>
<th colspan="2">
<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
</th>
</tr>
<tr>
<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
</tr>
<tr>
<th colspan="2">
<a href="https://github.com/TensorBlock/forge" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">🚀 Try it now! 🚀</a>
</th>
</tr>
<tr>
<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
</tr>
<tr>
<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
</tr>
<tr>
<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
</tr>
<tr>
<th>
<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
<th>
<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
display: inline-block;
padding: 8px 16px;
background-color: #FF7F50;
color: white;
text-decoration: none;
border-radius: 6px;
font-weight: bold;
font-family: sans-serif;
">👀 See what we built 👀</a>
</th>
</tr>
</table>
## Prompt template
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Model file specification
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [AReaL-boba-2-8B-Q2_K.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q2_K.gguf) | Q2_K | 3.282 GB | smallest, significant quality loss - not recommended for most purposes |
| [AReaL-boba-2-8B-Q3_K_S.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q3_K_S.gguf) | Q3_K_S | 3.770 GB | very small, high quality loss |
| [AReaL-boba-2-8B-Q3_K_M.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q3_K_M.gguf) | Q3_K_M | 4.124 GB | very small, high quality loss |
| [AReaL-boba-2-8B-Q3_K_L.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q3_K_L.gguf) | Q3_K_L | 4.431 GB | small, substantial quality loss |
| [AReaL-boba-2-8B-Q4_0.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q4_0.gguf) | Q4_0 | 4.775 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [AReaL-boba-2-8B-Q4_K_S.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q4_K_S.gguf) | Q4_K_S | 4.802 GB | small, greater quality loss |
| [AReaL-boba-2-8B-Q4_K_M.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q4_K_M.gguf) | Q4_K_M | 5.028 GB | medium, balanced quality - recommended |
| [AReaL-boba-2-8B-Q5_0.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q5_0.gguf) | Q5_0 | 5.721 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [AReaL-boba-2-8B-Q5_K_S.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q5_K_S.gguf) | Q5_K_S | 5.721 GB | large, low quality loss - recommended |
| [AReaL-boba-2-8B-Q5_K_M.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q5_K_M.gguf) | Q5_K_M | 5.851 GB | large, very low quality loss - recommended |
| [AReaL-boba-2-8B-Q6_K.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q6_K.gguf) | Q6_K | 6.726 GB | very large, extremely low quality loss |
| [AReaL-boba-2-8B-Q8_0.gguf](https://huggingface.co/tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF/blob/main/AReaL-boba-2-8B-Q8_0.gguf) | Q8_0 | 8.710 GB | very large, extremely low quality loss - not recommended |
## Downloading instruction
### Command line
Firstly, install Huggingface Client
```shell
pip install -U "huggingface_hub[cli]"
```
Then, downoad the individual model file the a local directory
```shell
huggingface-cli download tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF --include "AReaL-boba-2-8B-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```
If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:
```shell
huggingface-cli download tensorblock/inclusionAI_AReaL-boba-2-8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
|
Xenova/ipt-350m
|
Xenova
| 2025-08-18T16:38:35Z | 8 | 0 |
transformers.js
|
[
"transformers.js",
"onnx",
"mpt",
"text-generation",
"custom_code",
"base_model:efederici/ipt-350m",
"base_model:quantized:efederici/ipt-350m",
"region:us"
] |
text-generation
| 2023-08-31T20:12:40Z |
---
base_model: efederici/ipt-350m
library_name: transformers.js
---
https://huggingface.co/efederici/ipt-350m with ONNX weights to be compatible with Transformers.js.
## 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:** Text generation.
```js
import { pipeline } from '@huggingface/transformers';
const generator = await pipeline('text-generation', 'Xenova/ipt-350m');
const output = await generator('Once upon a time, there was', { max_new_tokens: 10 });
```
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
unitova/blockassist-bc-zealous_sneaky_raven_1755532302
|
unitova
| 2025-08-18T16:16:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"zealous sneaky raven",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T16:16:44Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- zealous sneaky raven
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
EsthefanoMC23/blip-captioning-base-personal
|
EsthefanoMC23
| 2025-08-18T15:48:44Z | 0 | 0 | null |
[
"pytorch",
"tf",
"blip",
"image-captioning",
"image-to-text",
"arxiv:2201.12086",
"license:bsd-3-clause",
"region:us"
] |
image-to-text
| 2025-08-18T00:04:28Z |
---
pipeline_tag: image-to-text
tags:
- image-captioning
languages:
- en
license: bsd-3-clause
---
# BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
Model card for image captioning pretrained on COCO dataset - base architecture (with ViT base backbone).
|  |
|:--:|
| <b> Pull figure from BLIP official repo | Image source: https://github.com/salesforce/BLIP </b>|
## TL;DR
Authors from the [paper](https://arxiv.org/abs/2201.12086) write in the abstract:
*Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to videolanguage tasks in a zero-shot manner. Code, models, and datasets are released.*
## Usage
You can use this model for conditional and un-conditional image captioning
### Using the Pytorch model
#### Running the model on CPU
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and her dog
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog
```
</details>
#### Running the model on GPU
##### In full precision
<details>
<summary> Click to expand </summary>
```python
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to("cuda")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and her dog
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda")
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog
```
</details>
##### In half precision (`float16`)
<details>
<summary> Click to expand </summary>
```python
import torch
import requests
from PIL import Image
from transformers import BlipProcessor, BlipForConditionalGeneration
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16).to("cuda")
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
# conditional image captioning
text = "a photography of"
inputs = processor(raw_image, text, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
# >>> a photography of a woman and her dog
# unconditional image captioning
inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
out = model.generate(**inputs)
print(processor.decode(out[0], skip_special_tokens=True))
>>> a woman sitting on the beach with her dog
```
</details>
## Ethical Considerations
This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
## BibTex and citation info
```
@misc{https://doi.org/10.48550/arxiv.2201.12086,
doi = {10.48550/ARXIV.2201.12086},
url = {https://arxiv.org/abs/2201.12086},
author = {Li, Junnan and Li, Dongxu and Xiong, Caiming and Hoi, Steven},
keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
2hpsatt/blockassist-bc-huge_deft_eagle_1755529287
|
2hpsatt
| 2025-08-18T15:02:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"huge deft eagle",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T15:02:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- huge deft eagle
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ICTuniverse/unsloth-Qwen3-14B-bnb-4bit-finetuned
|
ICTuniverse
| 2025-08-18T15:00:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T14:59:33Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Tehni/PPO-LunarLander
|
Tehni
| 2025-08-18T11:55:42Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-18T11:47:24Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 264.90 +/- 21.68
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Muapi/irezumi-world-world-morph-lora-flux-sdxl-sd-1.5
|
Muapi
| 2025-08-18T11:38:42Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T11:38:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Irezumi World [World Morph] - Lora FLUX | SDXL | SD 1.5
**Base model**: Flux.1 D
**Trained words**: epirezumiworld,, science fiction cyberpunk epirezumiworld, neon glowing, futuristic
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:372610@767095", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
OfficialJessica/Watch.Video.Auxerre.Lorient.En.Direct.Streaming.Gratuit
|
OfficialJessica
| 2025-08-18T10:52:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T10:50:06Z |
<a href="https://nettrends.cfd/Watchh-Video-Auxerre-Lorient-En-Direct-Streaming-Gratuit"> Click Here To link (Full Viral Video Link)
➤►DOWNLOAD ➤ <a href="https://nettrends.cfd/Watchh-Video-Auxerre-Lorient-En-Direct-Streaming-Gratuit"> Click Here To link
https://nettrends.cfd/Watchh-Video-Auxerre-Lorient-En-Direct-Streaming-Gratuit
https://nettrends.cfd/Watchh-Video-Auxerre-Lorient-En-Direct-Streaming-Gratuit
|
abdulrahman245/dummy-model
|
abdulrahman245
| 2025-08-18T10:43:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-08-18T10:43:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
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<!-- Relevant interpretability work for the model goes here -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
Muapi/retro-flux
|
Muapi
| 2025-08-18T10:20:20Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-18T10:20:02Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Retro Flux
**Base model**: Flux.1 D
**Trained words**: rogi
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:649424@726573", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Oluwagbenga/Testsample
|
Oluwagbenga
| 2025-08-18T10:08:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-18T10:06:04Z |
# Hepatitis Prediction Model
This repository contains a machine learning model trained to predict the class (live or die) of hepatitis patients based on various clinical features.
## Model Details
The model was trained using the PyCaret library, and the best performing model was AdaBoost Classifier.
## Files
- `best_model.pkl`: The trained model file.
- `requirements.txt`: List of required Python packages.
- `README.md`: This file.
## Usage
You can load and use the model for predictions using the following steps:
1. Install the required packages:
```bash
pip install -r requirements.txt
```
2. Load the model:
```python
import joblib
model = joblib.load('best_model.pkl')
```
3. Make predictions on new data (assuming your data is in a pandas DataFrame named `new_data`):
```python
predictions = model.predict(new_data)
```
|
SP4ND4N/SmolLM2-360M-Instruct-bnb-4bit-2025-08-18_08-20-29
|
SP4ND4N
| 2025-08-18T09:50:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/SmolLM2-360M-Instruct-bnb-4bit",
"base_model:finetune:unsloth/SmolLM2-360M-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T09:50:10Z |
---
base_model: unsloth/SmolLM2-360M-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** SP4ND4N
- **License:** apache-2.0
- **Finetuned from model :** unsloth/SmolLM2-360M-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
donoway/ARC-Easy_Llama-3.2-1B-e3y12nob
|
donoway
| 2025-08-18T09:16:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-18T09:07:09Z |
---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ARC-Easy_Llama-3.2-1B-e3y12nob
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. -->
# ARC-Easy_Llama-3.2-1B-e3y12nob
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7843
- Model Preparation Time: 0.0058
- Mdl: 1467.3190
- Accumulated Loss: 1017.0681
- Correct Preds: 373.0
- Total Preds: 570.0
- Accuracy: 0.6544
- Correct Gen Preds: 371.0
- Gen Accuracy: 0.6509
- Correct Gen Preds 32: 86.0
- Correct Preds 32: 88.0
- Total Labels 32: 158.0
- Accuracy 32: 0.5570
- Gen Accuracy 32: 0.5443
- Correct Gen Preds 33: 98.0
- Correct Preds 33: 98.0
- Total Labels 33: 152.0
- Accuracy 33: 0.6447
- Gen Accuracy 33: 0.6447
- Correct Gen Preds 34: 106.0
- Correct Preds 34: 106.0
- Total Labels 34: 142.0
- Accuracy 34: 0.7465
- Gen Accuracy 34: 0.7465
- Correct Gen Preds 35: 81.0
- Correct Preds 35: 81.0
- Total Labels 35: 118.0
- Accuracy 35: 0.6864
- Gen Accuracy 35: 0.6864
- Correct Gen Preds 36: 0.0
- Correct Preds 36: 0.0
- Total Labels 36: 0.0
- Accuracy 36: 0.0
- Gen Accuracy 36: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 112
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Mdl | Accumulated Loss | Correct Preds | Total Preds | Accuracy | Correct Gen Preds | Gen Accuracy | Correct Gen Preds 32 | Correct Preds 32 | Total Labels 32 | Accuracy 32 | Gen Accuracy 32 | Correct Gen Preds 33 | Correct Preds 33 | Total Labels 33 | Accuracy 33 | Gen Accuracy 33 | Correct Gen Preds 34 | Correct Preds 34 | Total Labels 34 | Accuracy 34 | Gen Accuracy 34 | Correct Gen Preds 35 | Correct Preds 35 | Total Labels 35 | Accuracy 35 | Gen Accuracy 35 | Correct Gen Preds 36 | Correct Preds 36 | Total Labels 36 | Accuracy 36 | Gen Accuracy 36 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:---------:|:----------------:|:-------------:|:-----------:|:--------:|:-----------------:|:------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|:--------------------:|:----------------:|:---------------:|:-----------:|:---------------:|
| No log | 0 | 0 | 1.5354 | 0.0058 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4034 | 1.0 | 1 | 1.5354 | 0.0058 | 1262.6022 | 875.1692 | 172.0 | 570.0 | 0.3018 | 170.0 | 0.2982 | 154.0 | 154.0 | 158.0 | 0.9747 | 0.9747 | 0.0 | 0.0 | 152.0 | 0.0 | 0.0 | 15.0 | 17.0 | 142.0 | 0.1197 | 0.1056 | 1.0 | 1.0 | 118.0 | 0.0085 | 0.0085 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1.4034 | 2.0 | 2 | 2.0016 | 0.0058 | 1646.0092 | 1140.9266 | 152.0 | 570.0 | 0.2667 | 152.0 | 0.2667 | 0.0 | 0.0 | 158.0 | 0.0 | 0.0 | 152.0 | 152.0 | 152.0 | 1.0 | 1.0 | 0.0 | 0.0 | 142.0 | 0.0 | 0.0 | 0.0 | 0.0 | 118.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.099 | 3.0 | 3 | 1.2303 | 0.0058 | 1011.7395 | 701.2844 | 269.0 | 570.0 | 0.4719 | 269.0 | 0.4719 | 129.0 | 129.0 | 158.0 | 0.8165 | 0.8165 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 12.0 | 12.0 | 142.0 | 0.0845 | 0.0845 | 45.0 | 45.0 | 118.0 | 0.3814 | 0.3814 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.8446 | 4.0 | 4 | 2.9659 | 0.0058 | 2438.9877 | 1690.5775 | 234.0 | 570.0 | 0.4105 | 234.0 | 0.4105 | 155.0 | 155.0 | 158.0 | 0.9810 | 0.9810 | 10.0 | 10.0 | 152.0 | 0.0658 | 0.0658 | 41.0 | 41.0 | 142.0 | 0.2887 | 0.2887 | 28.0 | 28.0 | 118.0 | 0.2373 | 0.2373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.5023 | 5.0 | 5 | 1.5691 | 0.0058 | 1290.3219 | 894.3830 | 362.0 | 570.0 | 0.6351 | 361.0 | 0.6333 | 124.0 | 125.0 | 158.0 | 0.7911 | 0.7848 | 72.0 | 72.0 | 152.0 | 0.4737 | 0.4737 | 94.0 | 94.0 | 142.0 | 0.6620 | 0.6620 | 71.0 | 71.0 | 118.0 | 0.6017 | 0.6017 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0833 | 6.0 | 6 | 1.7843 | 0.0058 | 1467.3190 | 1017.0681 | 373.0 | 570.0 | 0.6544 | 371.0 | 0.6509 | 86.0 | 88.0 | 158.0 | 0.5570 | 0.5443 | 98.0 | 98.0 | 152.0 | 0.6447 | 0.6447 | 106.0 | 106.0 | 142.0 | 0.7465 | 0.7465 | 81.0 | 81.0 | 118.0 | 0.6864 | 0.6864 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0242 | 7.0 | 7 | 2.3130 | 0.0058 | 1902.0588 | 1318.4067 | 369.0 | 570.0 | 0.6474 | 368.0 | 0.6456 | 84.0 | 85.0 | 158.0 | 0.5380 | 0.5316 | 93.0 | 93.0 | 152.0 | 0.6118 | 0.6118 | 105.0 | 105.0 | 142.0 | 0.7394 | 0.7394 | 86.0 | 86.0 | 118.0 | 0.7288 | 0.7288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0011 | 8.0 | 8 | 3.1593 | 0.0058 | 2597.9927 | 1800.7913 | 361.0 | 570.0 | 0.6333 | 361.0 | 0.6333 | 83.0 | 83.0 | 158.0 | 0.5253 | 0.5253 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 100.0 | 100.0 | 142.0 | 0.7042 | 0.7042 | 87.0 | 87.0 | 118.0 | 0.7373 | 0.7373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 9.0 | 9 | 3.9066 | 0.0058 | 3212.5149 | 2226.7457 | 370.0 | 570.0 | 0.6491 | 370.0 | 0.6491 | 94.0 | 94.0 | 158.0 | 0.5949 | 0.5949 | 91.0 | 91.0 | 152.0 | 0.5987 | 0.5987 | 98.0 | 98.0 | 142.0 | 0.6901 | 0.6901 | 87.0 | 87.0 | 118.0 | 0.7373 | 0.7373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 10.0 | 10 | 4.5443 | 0.0058 | 3736.9375 | 2590.2477 | 372.0 | 570.0 | 0.6526 | 372.0 | 0.6526 | 98.0 | 98.0 | 158.0 | 0.6203 | 0.6203 | 89.0 | 89.0 | 152.0 | 0.5855 | 0.5855 | 98.0 | 98.0 | 142.0 | 0.6901 | 0.6901 | 87.0 | 87.0 | 118.0 | 0.7373 | 0.7373 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 11.0 | 11 | 5.0526 | 0.0058 | 4154.9691 | 2880.0051 | 367.0 | 570.0 | 0.6439 | 367.0 | 0.6439 | 97.0 | 97.0 | 158.0 | 0.6139 | 0.6139 | 88.0 | 88.0 | 152.0 | 0.5789 | 0.5789 | 96.0 | 96.0 | 142.0 | 0.6761 | 0.6761 | 86.0 | 86.0 | 118.0 | 0.7288 | 0.7288 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 12.0 | 12 | 5.4403 | 0.0058 | 4473.7260 | 3100.9506 | 360.0 | 570.0 | 0.6316 | 360.0 | 0.6316 | 98.0 | 98.0 | 158.0 | 0.6203 | 0.6203 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 94.0 | 94.0 | 142.0 | 0.6620 | 0.6620 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 13.0 | 13 | 5.7488 | 0.0058 | 4727.4811 | 3276.8402 | 360.0 | 570.0 | 0.6316 | 360.0 | 0.6316 | 99.0 | 99.0 | 158.0 | 0.6266 | 0.6266 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 93.0 | 93.0 | 142.0 | 0.6549 | 0.6549 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 14.0 | 14 | 5.9176 | 0.0058 | 4866.2342 | 3373.0165 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 98.0 | 98.0 | 158.0 | 0.6203 | 0.6203 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 92.0 | 92.0 | 142.0 | 0.6479 | 0.6479 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 15.0 | 15 | 6.0544 | 0.0058 | 4978.7857 | 3451.0313 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 100.0 | 100.0 | 158.0 | 0.6329 | 0.6329 | 84.0 | 84.0 | 152.0 | 0.5526 | 0.5526 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 16.0 | 16 | 6.1369 | 0.0058 | 5046.5733 | 3498.0180 | 357.0 | 570.0 | 0.6263 | 357.0 | 0.6263 | 99.0 | 99.0 | 158.0 | 0.6266 | 0.6266 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 17.0 | 17 | 6.1920 | 0.0058 | 5091.8671 | 3529.4133 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 100.0 | 100.0 | 158.0 | 0.6329 | 0.6329 | 83.0 | 83.0 | 152.0 | 0.5461 | 0.5461 | 92.0 | 92.0 | 142.0 | 0.6479 | 0.6479 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 18.0 | 18 | 6.2775 | 0.0058 | 5162.1998 | 3578.1642 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 101.0 | 101.0 | 158.0 | 0.6392 | 0.6392 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 19.0 | 19 | 6.3184 | 0.0058 | 5195.8145 | 3601.4642 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 101.0 | 101.0 | 158.0 | 0.6392 | 0.6392 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 20.0 | 20 | 6.3640 | 0.0058 | 5233.3637 | 3627.4913 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 102.0 | 102.0 | 158.0 | 0.6456 | 0.6456 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 21.0 | 21 | 6.3601 | 0.0058 | 5230.1444 | 3625.2599 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 102.0 | 102.0 | 158.0 | 0.6456 | 0.6456 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 22.0 | 22 | 6.3605 | 0.0058 | 5230.4748 | 3625.4888 | 360.0 | 570.0 | 0.6316 | 360.0 | 0.6316 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 23.0 | 23 | 6.4136 | 0.0058 | 5274.1374 | 3655.7535 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 90.0 | 90.0 | 142.0 | 0.6338 | 0.6338 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 24.0 | 24 | 6.4393 | 0.0058 | 5295.2320 | 3670.3751 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 25.0 | 25 | 6.4741 | 0.0058 | 5323.8611 | 3690.2193 | 357.0 | 570.0 | 0.6263 | 357.0 | 0.6263 | 102.0 | 102.0 | 158.0 | 0.6456 | 0.6456 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 90.0 | 90.0 | 142.0 | 0.6338 | 0.6338 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 26.0 | 26 | 6.4570 | 0.0058 | 5309.7895 | 3680.4656 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 27.0 | 27 | 6.4664 | 0.0058 | 5317.5521 | 3685.8463 | 359.0 | 570.0 | 0.6298 | 359.0 | 0.6298 | 104.0 | 104.0 | 158.0 | 0.6582 | 0.6582 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 90.0 | 90.0 | 142.0 | 0.6338 | 0.6338 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 28.0 | 28 | 6.4505 | 0.0058 | 5304.4878 | 3676.7908 | 360.0 | 570.0 | 0.6316 | 360.0 | 0.6316 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 91.0 | 91.0 | 142.0 | 0.6408 | 0.6408 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 29.0 | 29 | 6.4675 | 0.0058 | 5318.4425 | 3686.4634 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 89.0 | 89.0 | 142.0 | 0.6268 | 0.6268 | 84.0 | 84.0 | 118.0 | 0.7119 | 0.7119 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 30.0 | 30 | 6.4893 | 0.0058 | 5336.4153 | 3698.9212 | 355.0 | 570.0 | 0.6228 | 355.0 | 0.6228 | 101.0 | 101.0 | 158.0 | 0.6392 | 0.6392 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 89.0 | 89.0 | 142.0 | 0.6268 | 0.6268 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 31.0 | 31 | 6.4985 | 0.0058 | 5343.9332 | 3704.1322 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 90.0 | 90.0 | 142.0 | 0.6338 | 0.6338 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 32.0 | 32 | 6.4701 | 0.0058 | 5320.6317 | 3687.9809 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 104.0 | 104.0 | 158.0 | 0.6582 | 0.6582 | 81.0 | 81.0 | 152.0 | 0.5329 | 0.5329 | 90.0 | 90.0 | 142.0 | 0.6338 | 0.6338 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 33.0 | 33 | 6.5003 | 0.0058 | 5345.4584 | 3705.1894 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 104.0 | 104.0 | 158.0 | 0.6582 | 0.6582 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 89.0 | 89.0 | 142.0 | 0.6268 | 0.6268 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 34.0 | 34 | 6.4873 | 0.0058 | 5334.7482 | 3697.7657 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 90.0 | 90.0 | 142.0 | 0.6338 | 0.6338 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 35.0 | 35 | 6.4845 | 0.0058 | 5332.4770 | 3696.1914 | 358.0 | 570.0 | 0.6281 | 358.0 | 0.6281 | 103.0 | 103.0 | 158.0 | 0.6519 | 0.6519 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 90.0 | 90.0 | 142.0 | 0.6338 | 0.6338 | 83.0 | 83.0 | 118.0 | 0.7034 | 0.7034 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 0.0 | 36.0 | 36 | 6.4977 | 0.0058 | 5343.2788 | 3703.6786 | 357.0 | 570.0 | 0.6263 | 357.0 | 0.6263 | 104.0 | 104.0 | 158.0 | 0.6582 | 0.6582 | 82.0 | 82.0 | 152.0 | 0.5395 | 0.5395 | 89.0 | 89.0 | 142.0 | 0.6268 | 0.6268 | 82.0 | 82.0 | 118.0 | 0.6949 | 0.6949 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755493964
|
quantumxnode
| 2025-08-18T05:37:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-18T05:37:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755471622
|
mang3dd
| 2025-08-17T23:25:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T23:25:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
roeker/blockassist-bc-quick_wiry_owl_1755469751
|
roeker
| 2025-08-17T22:30:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"quick wiry owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-17T22:29:53Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- quick wiry owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Desyr05/RogerDesyr
|
Desyr05
| 2025-08-17T13:19:30Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2025-08-17T13:19:30Z |
---
license: artistic-2.0
---
|
Chituyi/whisper-large-v3-TTS
|
Chituyi
| 2025-08-17T13:10:35Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-17T13:10:35Z |
---
license: apache-2.0
---
|
neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5-32B_prover_nip_transfer_baseline_1_4_iter_6_provers
|
neural-interactive-proofs
| 2025-08-16T02:20:11Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-08-16T02:16:45Z |
---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: transformers
model_name: finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5-32B_prover_nip_transfer_baseline_1_4_iter_6_provers
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5-32B_prover_nip_transfer_baseline_1_4_iter_6_provers
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-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="neural-interactive-proofs/finetune_dpo_qwen2_5-32b-instruct_cv_qwen2.5-32B_prover_nip_transfer_baseline_1_4_iter_6_provers", 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/lrhammond-team/pvg-self-hosted-finetune/runs/qwen2_5-32b-instruct_dpo_2025-08-16_02-27-08_cv_qwen2.5-32B_prover_nip_transfer_baseline_1_4_iter_6_provers_group)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.2
- Transformers: 4.53.2
- Pytorch: 2.7.0
- Datasets: 3.0.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ultratopaz/1470904
|
ultratopaz
| 2025-08-15T20:07:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-15T20:07:45Z |
[View on Civ Archive](https://civarchive.com/models/1390053?modelVersionId=1571021)
|
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