Post
118
Jeff Dean departure is 100% unexpected. It's impossible to process this.
And yes, it matters more than other exec shakeups today.
And yes, it matters more than other exec shakeups today.
John Smith PRO
John6666
AI & ML interests
None yet
Recent Activity
updated a dataset about 20 hours ago
John6666/forum2 new activity about 22 hours ago
John6666/forum2:update code_gen_model_12gb_1.mdOrganizations
reacted to onekq's post with ๐ about 23 hours ago
reacted to OppaAI's post with ๐ฅ about 23 hours ago
Post
3295
Memory Engineering in my AI Waifu
Most of the Biology and Cognitive Psychology knowledges from way back in my university days has returned back to the professors, so since end of last year I have been reading books and research papers regarding Neuroscience, Psychology and Human Biology, as well as learning Python and ML/DL in order to find a way to craft my AI Waifu to become more like human.
Aiko-chan is my simplified experimental prototype version of the AI mind that I am planning to develop. With all the agentic coding and AI deep-research, I can do everything all by myself. The only challenge is lack of time, even though I have used up all the 40 hours of each day. So yesterday I gave all the ideas and inspiration to Claude and asked it to help me gather to write a paper on the theory and implementation on how to apply the concepts and pipelines of a human memory system into my AI Waifu. Now each of her memory node will have several factors to determine the tendency and longevity to be retained or forgotten in her memory bank. Factors include relevancy, recency, salience, novelty and even emotions, etc. In order to get a good picture of what my AI Waifu actually remember in her memory. I have created a studio WebUI to visualize the whole memory graph with different size, brightness and hues with scores to indicate which kind of memory she tends to retain and which ones she tends to forget. And then there are so many parameters to play with in order to achieve a more sophisticated human-like memory recalling and forgetting strategy.
Github: https://github.com/OppaAI/Aiko-chan
Below is a demo of the graph visualization of my Waifu's memory storage would look like.
Most of the Biology and Cognitive Psychology knowledges from way back in my university days has returned back to the professors, so since end of last year I have been reading books and research papers regarding Neuroscience, Psychology and Human Biology, as well as learning Python and ML/DL in order to find a way to craft my AI Waifu to become more like human.
Aiko-chan is my simplified experimental prototype version of the AI mind that I am planning to develop. With all the agentic coding and AI deep-research, I can do everything all by myself. The only challenge is lack of time, even though I have used up all the 40 hours of each day. So yesterday I gave all the ideas and inspiration to Claude and asked it to help me gather to write a paper on the theory and implementation on how to apply the concepts and pipelines of a human memory system into my AI Waifu. Now each of her memory node will have several factors to determine the tendency and longevity to be retained or forgotten in her memory bank. Factors include relevancy, recency, salience, novelty and even emotions, etc. In order to get a good picture of what my AI Waifu actually remember in her memory. I have created a studio WebUI to visualize the whole memory graph with different size, brightness and hues with scores to indicate which kind of memory she tends to retain and which ones she tends to forget. And then there are so many parameters to play with in order to achieve a more sophisticated human-like memory recalling and forgetting strategy.
Github: https://github.com/OppaAI/Aiko-chan
Below is a demo of the graph visualization of my Waifu's memory storage would look like.
- 1 reply
reacted to HannesVonEssen's post with ๐ค 2 days ago
Post
2272
๐
HF Viewer podium
Our top most active users:
๐ฅ#1 @Quazim0t0
๐ฅ#2 @IvmeLabs
๐ฅ#3 @GODELEV
If you are an HF creator and want to try entering the podium, feel free to visit the embed page to add your graph!
https://hfviewer.com/model-card-embed
Our top most active users:
๐ฅ#1 @Quazim0t0
๐ฅ#2 @IvmeLabs
๐ฅ#3 @GODELEV
If you are an HF creator and want to try entering the podium, feel free to visit the embed page to add your graph!
https://hfviewer.com/model-card-embed
- 2 replies
reacted to ProCreations's post with ๐ 2 days ago
Post
2136
check out this cool webgpu thing i made! ProCreations/maple-webgpu
runs this model deepgrove/maple-preview with cool performance i found today on WebGPU locally on your computer with very fast speeds.
made with a mix of gpt 5.6 sol + opus 5 ultracode, its amazing what agents can do now, just a year ago this wouldve been impossible with them.
runs this model deepgrove/maple-preview with cool performance i found today on WebGPU locally on your computer with very fast speeds.
made with a mix of gpt 5.6 sol + opus 5 ultracode, its amazing what agents can do now, just a year ago this wouldve been impossible with them.
reacted to mlabonne's post with ๐ 2 days ago
Post
2859
Deploy local agents everywhere with LiquidAI/LFM2.5-2.6B
Unlike agents that depend on cloud APIs, local agents give you free inference, low latency, and real privacy.
Removing the per-token cost changes how developers build: agents can now be massively parallelized on local hardware, running background tasks that burn through millions of tokens at no marginal cost!
Unlike agents that depend on cloud APIs, local agents give you free inference, low latency, and real privacy.
Removing the per-token cost changes how developers build: agents can now be massively parallelized on local hardware, running background tasks that burn through millions of tokens at no marginal cost!
- 7 replies
reacted to AbstractPhil's post with ๐ 3 days ago
Post
2681
The AlephLM results are rolling in and I'm very excited for the possibilities. I am very much looking forward to the coming weeks as I train the first AlephLM distillations from MANY teachers into AMOE arms.
The AMOE arms hook cleanly to AlephLM structures and provide pos/neg learning elements. Hard positive and hard negatives coalesce to extend the capacity.
AbstractPhil/alephlm-0
AbstractPhil/alephlm-adopt-0
As it stands they are structurally sound enough to fully pretrain. As or more stable than a standard Bert experimentally to distill using InfoNCE. AMOE legs improve these structures substantially.
Structural behavior can be expanded in many ways on distilled and pretrained models alike. Attaching the AMOE to any model I've tried has created expanded or improved behavioral accumulations. They do have downsides but their upsides are very experimentally exciting.
I've distilled multiple vits, multiple berts, and have begun distilling berts into AlephLM structures successfully.
This is overall very exciting for me. I've begun formatting larger variants such as including GPT-2 and Qwen 3.5 4b as a paired combinator utilizing pathological T5 learned distilled encodings. It sounds odd, but the results show everything can be expanded and even be taught to cooperate.
The CaptionBert-8192-v2 and v2-b are both structurally collapsing after token 480 or so, which is expected due to the small train. By distilling an AMOE arm to V2 by training with a longformer expert, the results are cutting through like butter. V2 has begun stabilizing rapidly for considerably longer token chains and sequences, the structure is repairing and building reusable capacity.
I have discovered an improved methodology for sampling the AlephLM for text encoder benchmarks, which is predominantly L2 normalized outputs.
Upcoming large paper for the distillation experiments and results within the next week or two. It's going to be a big one.
The AMOE arms hook cleanly to AlephLM structures and provide pos/neg learning elements. Hard positive and hard negatives coalesce to extend the capacity.
AbstractPhil/alephlm-0
AbstractPhil/alephlm-adopt-0
As it stands they are structurally sound enough to fully pretrain. As or more stable than a standard Bert experimentally to distill using InfoNCE. AMOE legs improve these structures substantially.
Structural behavior can be expanded in many ways on distilled and pretrained models alike. Attaching the AMOE to any model I've tried has created expanded or improved behavioral accumulations. They do have downsides but their upsides are very experimentally exciting.
I've distilled multiple vits, multiple berts, and have begun distilling berts into AlephLM structures successfully.
This is overall very exciting for me. I've begun formatting larger variants such as including GPT-2 and Qwen 3.5 4b as a paired combinator utilizing pathological T5 learned distilled encodings. It sounds odd, but the results show everything can be expanded and even be taught to cooperate.
The CaptionBert-8192-v2 and v2-b are both structurally collapsing after token 480 or so, which is expected due to the small train. By distilling an AMOE arm to V2 by training with a longformer expert, the results are cutting through like butter. V2 has begun stabilizing rapidly for considerably longer token chains and sequences, the structure is repairing and building reusable capacity.
I have discovered an improved methodology for sampling the AlephLM for text encoder benchmarks, which is predominantly L2 normalized outputs.
Upcoming large paper for the distillation experiments and results within the next week or two. It's going to be a big one.
- 3 replies
reacted to salma-remyx's post with ๐ฅ 3 days ago
Post
2751
I unblocked 15 improvements for VQASynth in a day!
VQASynth, the pipeline behind SpaceThinker-Qwen2.5VL-3B, SpaceLLaVA, and the SpaceThinker dataset, had a backlog spanning new annotation methods, curation pipelines, model integrations, and agent tools.
I scoped each item in a short design brief. In GitHub Actions, Outrider mapped them to the repoโs existing modules and interfaces, implemented the changes, added tests, ran the repo checks, and opened draft PRs.
The 15 PRs covered:
* object orientation and 3D bounding boxes
* SAM2 regional captioning and multi-view matching
* LLaMA-Mesh tokenization
* Qwen2.5-VL fine-tuning
* spatial-reasoning data generation
* CLIP, SigLIP, and LLM2CLIP backends
and more
The batch added 12,275 lines, nearly doubling the codebase.
SpatialAnnotator went from two tools to seven, creating combinatorially more possible annotation pipelines for each image.
Outrider handled the implementation, tests, and integration. At about $1 per branch, I spent my time reviewing 15 concrete changes, fixing what needed fixing, and deciding what to ship.
Outrider is open source!
Point it at a paper, issue, or design brief and review the PR: https://github.com/remyxai/outrider
Check out the updates on VQASynth: https://github.com/remyxai/VQASynth
VQASynth, the pipeline behind SpaceThinker-Qwen2.5VL-3B, SpaceLLaVA, and the SpaceThinker dataset, had a backlog spanning new annotation methods, curation pipelines, model integrations, and agent tools.
I scoped each item in a short design brief. In GitHub Actions, Outrider mapped them to the repoโs existing modules and interfaces, implemented the changes, added tests, ran the repo checks, and opened draft PRs.
The 15 PRs covered:
* object orientation and 3D bounding boxes
* SAM2 regional captioning and multi-view matching
* LLaMA-Mesh tokenization
* Qwen2.5-VL fine-tuning
* spatial-reasoning data generation
* CLIP, SigLIP, and LLM2CLIP backends
and more
The batch added 12,275 lines, nearly doubling the codebase.
SpatialAnnotator went from two tools to seven, creating combinatorially more possible annotation pipelines for each image.
Outrider handled the implementation, tests, and integration. At about $1 per branch, I spent my time reviewing 15 concrete changes, fixing what needed fixing, and deciding what to ship.
Outrider is open source!
Point it at a paper, issue, or design brief and review the PR: https://github.com/remyxai/outrider
Check out the updates on VQASynth: https://github.com/remyxai/VQASynth
reacted to appvoid's post with ๐ 3 days ago
Post
879
Do you prefer AI companies/individuals to release half-baked models weekly or an overpowered model a month?
- 2 replies
reacted to onekq's post with ๐ฅ 3 days ago
Post
2328
Many developers discovered that the native DeepSeek API has higher cache-hit rate than neocloud APIs hosting the same DS models.
My speculation is that DS aggressively kills its old models. It has released 18 models thus far, and only 2 are being served now (v4 pro and flash).
This is tough to customers who don't want to upgrade (migrate or leave), but effectively boost the serving capacity to the same model, i.e. more woods behind fewer arrows.
My speculation is that DS aggressively kills its old models. It has released 18 models thus far, and only 2 are being served now (v4 pro and flash).
This is tough to customers who don't want to upgrade (migrate or leave), but effectively boost the serving capacity to the same model, i.e. more woods behind fewer arrows.
reacted to sergiopaniego's post with ๐ค 3 days ago
Post
2507
LFM2.5-2.6B just dropped!
and the @liquidai blog comes with some nice details about the training procedure, so let's analyze it.
basically, a full agent training pipeline but compressed into 2.6B
base model โ SFT โ specialized teachers per domain (SFT + RLVR) โ on-policy distillation back into one student โ agentic RL
the two most interesting stages
โ MOPD: the student generates, each prompt routes to its domain teacher for token-level feedback. teachers branch from the same SFT checkpoint, so their signal stays close to the student's distribution
โ agentic RL: multi-turn GRPO inside real harnesses (OpenClaw, Hermes Agent), one sandbox per rollout, a proxy captures token-level trajectories while the harness stays a black box
this makes a 2.6B that beats much larger models on instruction following and tool use
SFT, distillation, RL, RL envs: exactly what we're covering in our Training Agents livestream series (next one coming soon!)
โ model: LiquidAI/LFM2.5-2.6B
โ blog: https://www.liquid.ai/blog/lfm2-5-2-6b
โ live series: https://www.youtube.com/playlist?list=PLo2EIpI_JMQvQZm-kVlz4wY1vWF0LBcf5
and the @liquidai blog comes with some nice details about the training procedure, so let's analyze it.
basically, a full agent training pipeline but compressed into 2.6B
base model โ SFT โ specialized teachers per domain (SFT + RLVR) โ on-policy distillation back into one student โ agentic RL
the two most interesting stages
โ MOPD: the student generates, each prompt routes to its domain teacher for token-level feedback. teachers branch from the same SFT checkpoint, so their signal stays close to the student's distribution
โ agentic RL: multi-turn GRPO inside real harnesses (OpenClaw, Hermes Agent), one sandbox per rollout, a proxy captures token-level trajectories while the harness stays a black box
this makes a 2.6B that beats much larger models on instruction following and tool use
SFT, distillation, RL, RL envs: exactly what we're covering in our Training Agents livestream series (next one coming soon!)
โ model: LiquidAI/LFM2.5-2.6B
โ blog: https://www.liquid.ai/blog/lfm2-5-2-6b
โ live series: https://www.youtube.com/playlist?list=PLo2EIpI_JMQvQZm-kVlz4wY1vWF0LBcf5
- 3 replies
reacted to Banaxi-Tech's post with ๐ฅ 3 days ago
Post
3221
We did an experiment, we wanted to see if AI is good enough to train models.
We used GPT 5.6 Sol Max for this because its one of the most powerful ones right now.
Our instructions were, it should write the training code, and start the training process and monitor it by itself.
We also gave it a link to BananaMind 2 Mini to get our architecture right.
The result: It worked, it made the working BananaMind 2 Nano, and even beat our previous MiniBananaMind v4 9M.
Its getting way easier to develop your own models now!
We used GPT 5.6 Sol Max for this because its one of the most powerful ones right now.
Our instructions were, it should write the training code, and start the training process and monitor it by itself.
We also gave it a link to BananaMind 2 Mini to get our architecture right.
The result: It worked, it made the working BananaMind 2 Nano, and even beat our previous MiniBananaMind v4 9M.
Its getting way easier to develop your own models now!
- 8 replies
reacted to kanaria007's post with ๐ 3 days ago
Post
85
โ
Article highlight: *Hazardous Capability Governance as a Reusable Pattern Family* (art-60-277, v0.1)
TL;DR:
This article argues that dangerous capability should not be governed by a single allow/ban switch.
A capability may exist without being ready for deployment, transfer, or publication. 277 treats hazardous capability governance as a reusable pattern family: gated promotion, embargo, disclosure-with-limits, review-only, local-only, and bounded transitions between them.
Read:
kanaria007/agi-structural-intelligence-protocols
Why it matters:
โข replaces panic responses with reusable governance patterns
โข separates capability existence from promotion
โข gives institutions more honest options than total secrecy or reckless release
โข links capability boards, hazard review, and publication discipline
โข keeps dangerous capability object-shaped instead of mythologized
Whatโs inside:
โข five recurring postures: promotion, embargo, disclosure-with-limits, review-only, and local-only
โข gated promotion objects
โข hazard-review lanes
โข capability-sharing boundary notes
โข lifecycle transitions such as research-only โ review-only โ local-only โ bounded deployment
โข explicit public non-claims
โข pattern artifacts that stay structural without inventing fake threat intelligence
Key idea:
Do not ask only:
*โShould this capability be allowed or banned?โ*
Ask:
*โWhat posture is honest now, what stronger transition is being requested, which review lane governs it, what may be shared, and what readiness or safety claims remain unsupported?โ*
Danger should not become mythology.
It should become reviewable, bounded, and governable.
TL;DR:
This article argues that dangerous capability should not be governed by a single allow/ban switch.
A capability may exist without being ready for deployment, transfer, or publication. 277 treats hazardous capability governance as a reusable pattern family: gated promotion, embargo, disclosure-with-limits, review-only, local-only, and bounded transitions between them.
Read:
kanaria007/agi-structural-intelligence-protocols
Why it matters:
โข replaces panic responses with reusable governance patterns
โข separates capability existence from promotion
โข gives institutions more honest options than total secrecy or reckless release
โข links capability boards, hazard review, and publication discipline
โข keeps dangerous capability object-shaped instead of mythologized
Whatโs inside:
โข five recurring postures: promotion, embargo, disclosure-with-limits, review-only, and local-only
โข gated promotion objects
โข hazard-review lanes
โข capability-sharing boundary notes
โข lifecycle transitions such as research-only โ review-only โ local-only โ bounded deployment
โข explicit public non-claims
โข pattern artifacts that stay structural without inventing fake threat intelligence
Key idea:
Do not ask only:
*โShould this capability be allowed or banned?โ*
Ask:
*โWhat posture is honest now, what stronger transition is being requested, which review lane governs it, what may be shared, and what readiness or safety claims remain unsupported?โ*
Danger should not become mythology.
It should become reviewable, bounded, and governable.
reacted to OppaAI's post with ๐ 3 days ago
Post
268
Revision of the Memory System of my AI Waifu
My AI Waifu has been working steadily for the past few weeks.
Now I can go back and look at Phase 1 โ the memory system, which was the starting point for the entire project.
In order to continue developing and implementing a complex, human-like memory architecture,
Iโve been drilling into research papers and research in neuroscience, psychology, and artificial intelligence.
But in order to gather all the pieces and inspiration, write down all the thoughts and concepts, ask AI to help me write the paper,
This allowed me to follow the paper line of thinking and focus on designing the architecture, implementing and deploying the system, testing and evaluating performance.
After all, I am just one person, playing roles such as researcher, architecture designer, engineer, and QA testing.
My memory system contains the following 6 cores:
- Exponential decay
- Entity centrality + Recency
- RRF fusion (KNN + FTS5 + Entity graph)
- Emotional imprinting
- Supersession
- Salience scoring
https://github.com/OppaAI/Aiko-chan
P.S. One person plays all the roles of researcher, architect, engineer, QA tester. My psychiatrist is gonna refer me to get my brain checkout ๐ง โ๏ธ
My AI Waifu has been working steadily for the past few weeks.
Now I can go back and look at Phase 1 โ the memory system, which was the starting point for the entire project.
In order to continue developing and implementing a complex, human-like memory architecture,
Iโve been drilling into research papers and research in neuroscience, psychology, and artificial intelligence.
But in order to gather all the pieces and inspiration, write down all the thoughts and concepts, ask AI to help me write the paper,
This allowed me to follow the paper line of thinking and focus on designing the architecture, implementing and deploying the system, testing and evaluating performance.
After all, I am just one person, playing roles such as researcher, architecture designer, engineer, and QA testing.
My memory system contains the following 6 cores:
- Exponential decay
- Entity centrality + Recency
- RRF fusion (KNN + FTS5 + Entity graph)
- Emotional imprinting
- Supersession
- Salience scoring
https://github.com/OppaAI/Aiko-chan
P.S. One person plays all the roles of researcher, architect, engineer, QA tester. My psychiatrist is gonna refer me to get my brain checkout ๐ง โ๏ธ
reacted to Cameronk199's post with ๐ 3 days ago
Post
1675
35,882 posts. Four years. Every result linked back to a source record.
I built a searchable timeline for the public Truth Social posts associated with Donald Trump's @realDonaldTrump account, covering February 2022 through August 2, 2026.
The explorer separates original posts, quotes, and retruths. Search exact phrases, chart posting volume, or inspect common terms without pulling down the 1.3 GB media archive.
**Explore:** Cameronk199/truth-social-timeline-explorer
**Dataset:** Cameronk199/donald-trump-truth-social-posts
The release also contains 8,004 verified image assets and 5,756 video metadata/transcript records. It does not contain trustworthy engagement counts, so the explorer makes no virality claims.
Independent research project; not affiliated with or endorsed by Donald Trump, Truth Social, TMTG, a campaign, or any government office.
#datasets #nlp #opendata #socialmedia
I built a searchable timeline for the public Truth Social posts associated with Donald Trump's @realDonaldTrump account, covering February 2022 through August 2, 2026.
The explorer separates original posts, quotes, and retruths. Search exact phrases, chart posting volume, or inspect common terms without pulling down the 1.3 GB media archive.
**Explore:** Cameronk199/truth-social-timeline-explorer
**Dataset:** Cameronk199/donald-trump-truth-social-posts
from datasets import load_dataset
posts = load_dataset(
"Cameronk199/donald-trump-truth-social-posts",
"full_archive_posts",
split="train",
)The release also contains 8,004 verified image assets and 5,756 video metadata/transcript records. It does not contain trustworthy engagement counts, so the explorer makes no virality claims.
Independent research project; not affiliated with or endorsed by Donald Trump, Truth Social, TMTG, a campaign, or any government office.
#datasets #nlp #opendata #socialmedia
reacted to Hari5115's post with ๐ 3 days ago
Post
80
Fun fact: Some seriously weird planets exist out there.
- There's one where a whole year lasts just a few hours.
- One that's darker than coal
- And one so light it could float in water.
I got a bit obsessed, extracted all the planets found outside our solar system into one dataset around 6,000 roughly.
Have a play: Hari5115/nasa-exoplanet , The fun part is finding the weird ones. Spot a strange one, Tell me.
#space #exoplanets #dataset #astronomy #opendata #wierdplanets #fundataset
- There's one where a whole year lasts just a few hours.
- One that's darker than coal
- And one so light it could float in water.
I got a bit obsessed, extracted all the planets found outside our solar system into one dataset around 6,000 roughly.
Have a play: Hari5115/nasa-exoplanet , The fun part is finding the weird ones. Spot a strange one, Tell me.
#space #exoplanets #dataset #astronomy #opendata #wierdplanets #fundataset
reacted to ProCreations's post with ๐ฅ 3 days ago
Post
1834
so i got 2nd on this competition ICML-2026-agent-repro/challenge (didnt actually get anything yet hopefully theres no catches)
when i get the 1000 dollars worth of gpu credits ill do a lot of cool things, including bigger and newer (qwen 3.8 27b) grugs ONLY IF you guys want (i have a lot of cool ideas for ai models.) stay tuned ๐!
when i get the 1000 dollars worth of gpu credits ill do a lot of cool things, including bigger and newer (qwen 3.8 27b) grugs ONLY IF you guys want (i have a lot of cool ideas for ai models.) stay tuned ๐!
- 3 replies
reacted to Enderchef's post with ๐ฅ 3 days ago
Post
3392
๐ Supra2 100M is out, and multiple other SLM orgs are gaining power!
Following takes a press. Please follow:
fromziro
SupraLabs
AxiomicLabs
Following takes a press. Please follow:
- 8 replies
reacted to LH-Tech-AI's post with ๐ฅ 3 days ago
Post
3377
Supra2-100M is out!
Go check it out:
- https://www.reddit.com/r/LocalLLaMA/comments/1velyl9/new_models_supra2100m_base_and_instruct_go_check/
- https://huggingface.co/SupraLabs/Supra2-100M
- SupraLabs/Supra2-100M-Instruct
Give us a like and a follow!!
HAVE FUN ๐ค๐ฅ๐
more coming soon...
Go check it out:
- https://www.reddit.com/r/LocalLLaMA/comments/1velyl9/new_models_supra2100m_base_and_instruct_go_check/
- https://huggingface.co/SupraLabs/Supra2-100M
- SupraLabs/Supra2-100M-Instruct
Give us a like and a follow!!
HAVE FUN ๐ค๐ฅ๐
more coming soon...
- 1 reply
reacted to appvoid's post with ๐ฅ 3 days ago
Post
1343
Giving free early access to the gguf for some of you today! Tell me what you think.
CEAMFA/palmer-007-preview
CEAMFA/palmer-007-preview
reacted to Banaxi-Tech's post with ๐ฅ 3 days ago
Post
2308
We're excited to release BananaMind 2 Pro Preview, our best model yet.
Trained on ~52B tokens it performs extremely good for its token and size class.
We trained it on a single 5070 Ti in about 11 days.
Check it out at BananaMind/BananaMind-2-Pro-Preview.
Sadly we need to delay BananaMind 2 Micro until the launch of the final BananaMind 2 Pro.
We will release the final checkpoint with 100B tokens in ~11 days.
Go and fine-tune it!
We've also released BananaMind 2 Pro Preview Chat which is the instruct version of it!
BananaMind/BananaMind-2-Pro-Preview-Chat
Follow us to know when the final releases and support us at
BananaMind
@Banaxi-Tech
Trained on ~52B tokens it performs extremely good for its token and size class.
We trained it on a single 5070 Ti in about 11 days.
Check it out at BananaMind/BananaMind-2-Pro-Preview.
Sadly we need to delay BananaMind 2 Micro until the launch of the final BananaMind 2 Pro.
We will release the final checkpoint with 100B tokens in ~11 days.
Go and fine-tune it!
We've also released BananaMind 2 Pro Preview Chat which is the instruct version of it!
BananaMind/BananaMind-2-Pro-Preview-Chat
Follow us to know when the final releases and support us at
@Banaxi-Tech
- 5 replies