microsoft/bitnet-b1.58-2B-4T
Text Generation
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Jack Voide
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π·πΊ Russian Forum Messages Dataset -
nyuuzyou/ruforum
Collection of approximately 58 million Russian forum messages featuring:
- Complete message content from Russian online forums spanning 2010-2025
- Comprehensive metadata including unique message IDs and timestamps
- Full text content preserving original user discussions and interactions
- Monolingual dataset focused exclusively on Russian language content
This dataset offers a unique textual archive of Russian online conversations suitable for text generation, sentiment analysis, and language modeling research. Released to the public domain under CC0 1.0 license.
Collection of approximately 58 million Russian forum messages featuring:
- Complete message content from Russian online forums spanning 2010-2025
- Comprehensive metadata including unique message IDs and timestamps
- Full text content preserving original user discussions and interactions
- Monolingual dataset focused exclusively on Russian language content
This dataset offers a unique textual archive of Russian online conversations suitable for text generation, sentiment analysis, and language modeling research. Released to the public domain under CC0 1.0 license.
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MonsterMMORPG's
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Extending Wan 2.1 generated video - First 14b 720p text to video, then using last frame automatically to to generate a video with 14b 720p image to video - with RIFE 32 FPS 10 second 1280x720p video
Our app has this fully automated :Β https://www.patreon.com/posts/123105403
Here how it works image : https://ibb.co/b582z3R6
Workflow is easy
Use your favorite app to generate initial video.
Get last frame
Give last frame to image to video model - with matching model and resolution
Generate
And merge
Then use MMAudio to add sound
I made it automated in my Wan 2.1 app but can be made with ComfyUI easily as well . I can extend as many as times i want :)
Here initial video
Prompt: Close-up shot of a Roman gladiator, wearing a leather loincloth and armored gloves, standing confidently with a determined expression, holding a sword and shield. The lighting highlights his muscular build and the textures of his worn armor.
Negative Prompt: Overexposure, static, blurred details, subtitles, paintings, pictures, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, mutilated, redundant fingers, poorly painted hands, poorly painted faces, deformed, disfigured, deformed limbs, fused fingers, cluttered background, three legs, a lot of people in the background, upside down
Used Model: WAN 2.1 14B Text-to-Video
Number of Inference Steps: 20
CFG Scale: 6
Sigma Shift: 10
Seed: 224866642
Number of Frames: 81
Denoising Strength: N/A
LoRA Model: None
TeaCache Enabled: True
TeaCache L1 Threshold: 0.15
TeaCache Model ID: Wan2.1-T2V-14B
Precision: BF16
Auto Crop: Enabled
Final Resolution: 1280x720
Generation Duration: 770.66 seconds
Our app has this fully automated :Β https://www.patreon.com/posts/123105403
Here how it works image : https://ibb.co/b582z3R6
Workflow is easy
Use your favorite app to generate initial video.
Get last frame
Give last frame to image to video model - with matching model and resolution
Generate
And merge
Then use MMAudio to add sound
I made it automated in my Wan 2.1 app but can be made with ComfyUI easily as well . I can extend as many as times i want :)
Here initial video
Prompt: Close-up shot of a Roman gladiator, wearing a leather loincloth and armored gloves, standing confidently with a determined expression, holding a sword and shield. The lighting highlights his muscular build and the textures of his worn armor.
Negative Prompt: Overexposure, static, blurred details, subtitles, paintings, pictures, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, mutilated, redundant fingers, poorly painted hands, poorly painted faces, deformed, disfigured, deformed limbs, fused fingers, cluttered background, three legs, a lot of people in the background, upside down
Used Model: WAN 2.1 14B Text-to-Video
Number of Inference Steps: 20
CFG Scale: 6
Sigma Shift: 10
Seed: 224866642
Number of Frames: 81
Denoising Strength: N/A
LoRA Model: None
TeaCache Enabled: True
TeaCache L1 Threshold: 0.15
TeaCache Model ID: Wan2.1-T2V-14B
Precision: BF16
Auto Crop: Enabled
Final Resolution: 1280x720
Generation Duration: 770.66 seconds
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βοΈ AutoAbliteration
I made a Colab notebook to automatically abliterate models.
It's quite general, so you can do interesting stuff like blocking a given language in the model outputs.
π» Colab: https://colab.research.google.com/drive/1RmLv-pCMBBsQGXQIM8yF-OdCNyoylUR1?usp=sharing
I made a Colab notebook to automatically abliterate models.
It's quite general, so you can do interesting stuff like blocking a given language in the model outputs.
π» Colab: https://colab.research.google.com/drive/1RmLv-pCMBBsQGXQIM8yF-OdCNyoylUR1?usp=sharing
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