DavidAU/Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored-NM-DAU-NEO-MTP-GGUF
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Will the qwen3.8-27b version be produced?
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#54 opened 27 days ago
by 
cleverhack
reacted to branikita's post with 🔥🔥 8 days ago
Post
2211
SO-ARM 102 goes open source in the next few weeks.
What's new:
- A parallel gripper: the jaws stay parallel through the whole stroke instead of pivoting around the object as they close.
- PET-CF instead of PLA+ for a much stiffer frame.
- A topology-optimized structure.
- Wider joint rotation and folding range.
- STS3250 servos on the first shoulder joint.
Compared with the SO-ARM 101, that adds up to 2.5x the payload, roughly 2x better positioning accuracy, roughly 1.6x the movement speed, and about 36 mm more reach.
It runs on Hugging Face LeRobot, so the same tooling, training pipeline and tutorials for the SO-ARM 101 work on it from day one.
What's new:
- A parallel gripper: the jaws stay parallel through the whole stroke instead of pivoting around the object as they close.
- PET-CF instead of PLA+ for a much stiffer frame.
- A topology-optimized structure.
- Wider joint rotation and folding range.
- STS3250 servos on the first shoulder joint.
Compared with the SO-ARM 101, that adds up to 2.5x the payload, roughly 2x better positioning accuracy, roughly 1.6x the movement speed, and about 36 mm more reach.
It runs on Hugging Face LeRobot, so the same tooling, training pipeline and tutorials for the SO-ARM 101 work on it from day one.
- 1 reply
reacted to NitrAI's post with ❤️ 8 days ago
Post
1813
My (really ugly) thoughts on NVIDIA acquiring Hugging Face
I don’t care if you love me or hate me – something about one of the most open community efforts ever to achieve the tagline “The community building the future“ getting gobbled up by a company that arguably is the biggest hardware monopoly that has ever existed strikes me as deeply unsettling. I don’t like monopolies, and that is that. The whole appeal of HF for me personally was always having a neutral location where anyone could develop, deploy, and test a model on their silicon of choice without being pushed into a single “official“ proprietary infrastructure stack.
I am not going to pretend that I would believe NVIDIA “open and independent“ is ever going to happen – hell we have all heard the same lines dozens of times from every corporation that has ever uttered them before.
When the single biggest producer of compute also is one of the primary locations where all open weights live, it becomes very hard not to imagine where all of this is going to end up soon enough if we continue to let companies dictate the narrative. It might be the hyperbole but it is an absolute truth for me – open-sourced AI cannot be a slave to the whims of a trillion dollar company. It is high time we realize that open AI cannot live and breathe only on the goodwill of corporate entities.
I don’t care if you love me or hate me – something about one of the most open community efforts ever to achieve the tagline “The community building the future“ getting gobbled up by a company that arguably is the biggest hardware monopoly that has ever existed strikes me as deeply unsettling. I don’t like monopolies, and that is that. The whole appeal of HF for me personally was always having a neutral location where anyone could develop, deploy, and test a model on their silicon of choice without being pushed into a single “official“ proprietary infrastructure stack.
I am not going to pretend that I would believe NVIDIA “open and independent“ is ever going to happen – hell we have all heard the same lines dozens of times from every corporation that has ever uttered them before.
When the single biggest producer of compute also is one of the primary locations where all open weights live, it becomes very hard not to imagine where all of this is going to end up soon enough if we continue to let companies dictate the narrative. It might be the hyperbole but it is an absolute truth for me – open-sourced AI cannot be a slave to the whims of a trillion dollar company. It is high time we realize that open AI cannot live and breathe only on the goodwill of corporate entities.
- 10 replies
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reacted to FlameF0X's post with 👍 12 days ago
Post
2625
Hello HuggingFace!
I would like to share a small preview of a language model that I have been experiencing for a while. In the first attached image there is a small sample of the Myosotis-1, an attempt to make a small 100m parameter flagship model that is built on my bizarre architecture that is somewhat similar to an S4/S5 model with WKV added. I call it FWKV (Feed-Forward WKV). The model is currently still in training because of the nature of RNN-like models. It can also be seen that the model has insane prompt processing and token generation speed (evaluation done on a 2x Titan XP); even for its small size, some similar Transformer models do struggle to get the same results without custom kernels (some Transformer models can achieve this level of throughput on cheap hardware).
In the second image, you can see checkpoint 20k of the model in its next token prediction state (this means it can't chat), ranking in the top 100 on AxiomicLabs/Open_SLM_Leaderboard (the results have not been submitted since the model is not done training).
- Why not just use Transformers?
Have you seen any pure non-Transformers SLMs besides RWKV and Mamba?
- Should you expect this project to become the next LFM or another very fast language model thing on some Raspberry Pi?
No, the model is still an experiment; it's very sensible and prone to collapse (by the time of this post, it can be seen in the 1st image).
- Should you use it?
Maybe not yet; the architecture itself is still very "naive"—that's how I could call it at its current level. If you just want to play with it and see what you could do or how fast the model is on your hardware, then you can do it.
Once the training is finished and I feel satisfied with the model next token prediction (the base model) and "assistants" (the instruction-tuned model) capabilities, I will make open weights at
FWKV with full support of the 🤗 Transformers.
I would like to share a small preview of a language model that I have been experiencing for a while. In the first attached image there is a small sample of the Myosotis-1, an attempt to make a small 100m parameter flagship model that is built on my bizarre architecture that is somewhat similar to an S4/S5 model with WKV added. I call it FWKV (Feed-Forward WKV). The model is currently still in training because of the nature of RNN-like models. It can also be seen that the model has insane prompt processing and token generation speed (evaluation done on a 2x Titan XP); even for its small size, some similar Transformer models do struggle to get the same results without custom kernels (some Transformer models can achieve this level of throughput on cheap hardware).
In the second image, you can see checkpoint 20k of the model in its next token prediction state (this means it can't chat), ranking in the top 100 on AxiomicLabs/Open_SLM_Leaderboard (the results have not been submitted since the model is not done training).
- Why not just use Transformers?
Have you seen any pure non-Transformers SLMs besides RWKV and Mamba?
- Should you expect this project to become the next LFM or another very fast language model thing on some Raspberry Pi?
No, the model is still an experiment; it's very sensible and prone to collapse (by the time of this post, it can be seen in the 1st image).
- Should you use it?
Maybe not yet; the architecture itself is still very "naive"—that's how I could call it at its current level. If you just want to play with it and see what you could do or how fast the model is on your hardware, then you can do it.
Once the training is finished and I feel satisfied with the model next token prediction (the base model) and "assistants" (the instruction-tuned model) capabilities, I will make open weights at
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reacted to appvoid's post with 🔥 15 days ago
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2640
Nobody knows what is doing, when you train a model, you are experimenting to advance the frontier, so keep failing 🫵
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