32k here. 1B models are really not good with long context (yet), unfortunately.
Maxime Labonne PRO
mlabonne
AI & ML interests
Post-training, model editing, quantization
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liked
a model
about 2 hours ago
LiquidAI/LFM2-2.6B-Transcript-GGUF
updated
a model
about 5 hours ago
LiquidAI/LFM2.5-1.2B-JP-MLX-bf16
updated
a model
about 5 hours ago
LiquidAI/LFM2.5-1.2B-JP-MLX-8bit
Organizations
replied to
their
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1 day ago
reacted to
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1 day ago
Post
1395
New family of 1B models just dropped!
> LiquidAI/LFM2.5-1.2B-Base: 10T → 28T tokens
> LiquidAI/LFM2.5-1.2B-Instruct: new large-scale multi-stage RL
> LiquidAI/LFM2.5-1.2B-JP: our most polite model
> LiquidAI/LFM2.5-VL-1.6B: multi-image multilingual
> LiquidAI/LFM2.5-Audio-1.5B: 8x times faster, no quality loss
Super proud of this release 🤗
> LiquidAI/LFM2.5-1.2B-Base: 10T → 28T tokens
> LiquidAI/LFM2.5-1.2B-Instruct: new large-scale multi-stage RL
> LiquidAI/LFM2.5-1.2B-JP: our most polite model
> LiquidAI/LFM2.5-VL-1.6B: multi-image multilingual
> LiquidAI/LFM2.5-Audio-1.5B: 8x times faster, no quality loss
Super proud of this release 🤗
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- 3 replies
replied to
their
post
1 day ago
posted
an
update
1 day ago
Post
1395
New family of 1B models just dropped!
> LiquidAI/LFM2.5-1.2B-Base: 10T → 28T tokens
> LiquidAI/LFM2.5-1.2B-Instruct: new large-scale multi-stage RL
> LiquidAI/LFM2.5-1.2B-JP: our most polite model
> LiquidAI/LFM2.5-VL-1.6B: multi-image multilingual
> LiquidAI/LFM2.5-Audio-1.5B: 8x times faster, no quality loss
Super proud of this release 🤗
> LiquidAI/LFM2.5-1.2B-Base: 10T → 28T tokens
> LiquidAI/LFM2.5-1.2B-Instruct: new large-scale multi-stage RL
> LiquidAI/LFM2.5-1.2B-JP: our most polite model
> LiquidAI/LFM2.5-VL-1.6B: multi-image multilingual
> LiquidAI/LFM2.5-Audio-1.5B: 8x times faster, no quality loss
Super proud of this release 🤗
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- 3 replies
posted
an
update
3 months ago
Post
8258
LiquidAI/LFM2-8B-A1B just dropped!
8.3B params with only 1.5B active/token 🚀
> Quality ≈ 3–4B dense, yet faster than Qwen3-1.7B
> MoE designed to run on phones/laptops (llama.cpp / vLLM)
> Pre-trained on 12T tokens → strong math/code/IF
8.3B params with only 1.5B active/token 🚀
> Quality ≈ 3–4B dense, yet faster than Qwen3-1.7B
> MoE designed to run on phones/laptops (llama.cpp / vLLM)
> Pre-trained on 12T tokens → strong math/code/IF
- 1 reply
posted
an
update
3 months ago
Post
3751
⚛️ New drop of tiny task-specific models!
Want to do data extraction, translation, RAG, tool use, or math on a Raspberry Pi? We got you covered! ✅
These tiny models were fine-tuned to perform narrow tasks extremely well, making them competitive with much larger models.
You can deploy them today on-device or even on GPUs for big data operations!
LiquidAI/liquid-nanos-68b98d898414dd94d4d5f99a
Want to do data extraction, translation, RAG, tool use, or math on a Raspberry Pi? We got you covered! ✅
These tiny models were fine-tuned to perform narrow tasks extremely well, making them competitive with much larger models.
You can deploy them today on-device or even on GPUs for big data operations!
LiquidAI/liquid-nanos-68b98d898414dd94d4d5f99a
- 1 reply
posted
an
update
5 months ago
Post
6885
Liquid just released two 450M and 1.6B param VLMs!
They're super fast and leverage SigLIP2 NaFlex encoders to handle native resolutions without distortion. It's ideal for on-device deployment in constrained environments like phones.
It's available today on Hugging Face, with an inference and a fine-tuning Colab notebooks.
LiquidAI/LFM2-VL-450M
LiquidAI/LFM2-VL-1.6B
They're super fast and leverage SigLIP2 NaFlex encoders to handle native resolutions without distortion. It's ideal for on-device deployment in constrained environments like phones.
It's available today on Hugging Face, with an inference and a fine-tuning Colab notebooks.
LiquidAI/LFM2-VL-450M
LiquidAI/LFM2-VL-1.6B
reacted to
sergiopaniego's
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6 months ago
Post
1821
Test SmolLM3, the newest fully open model released by
@HuggingFaceTB
!
It's smol (3B), multilingual (6 languages), comes with dual mode reasoning (think/no_think modes) and supports long-context (128k).
Try it now in the notebook below!! ⬇️
Colab notebook: https://colab.research.google.com/github/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
notebook: https://github.com/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
blog: https://huggingface.co/blog/smollm3
It's smol (3B), multilingual (6 languages), comes with dual mode reasoning (think/no_think modes) and supports long-context (128k).
Try it now in the notebook below!! ⬇️
Colab notebook: https://colab.research.google.com/github/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
notebook: https://github.com/sergiopaniego/samples/blob/main/smollm3_3b_inference.ipynb
blog: https://huggingface.co/blog/smollm3
- 1 reply
posted
an
update
6 months ago
Post
5694
Based on a new hybrid architecture, these 350M, 700M, and 1.2B models are both fast and performant, ideal for on-device deployment.
I recommend fine-tuning them to power your next edge application. We already provide Colab notebooks to guide you. More to come soon!
📝 Blog post: https://www.liquid.ai/blog/liquid-foundation-models-v2-our-second-series-of-generative-ai-models
🤗 Models: LiquidAI/lfm2-686d721927015b2ad73eaa38
- 1 reply
That's super cool, congrats! :)
reacted to
drwlf's
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7 months ago
Post
5788
Having an insanely good medical LLM is pointless if it won’t answer your questions!
So we’ve made 2 notebook for abliterating any model in order to achieve a good model that will actually help you!
The notebooks are made using @mlabonne ‘s abliteration logic and datasets!
Feel free to use them and happy training 😊
https://github.com/dralexlup/LLM-Abliteration
So we’ve made 2 notebook for abliterating any model in order to achieve a good model that will actually help you!
The notebooks are made using @mlabonne ‘s abliteration logic and datasets!
Feel free to use them and happy training 😊
https://github.com/dralexlup/LLM-Abliteration
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- 3 replies
replied to
their
post
7 months ago
Will do at some point, but I don't have time to write this down at the moment.
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burtenshaw's
post with 🚀❤️🤗
9 months ago
Post
3498
NEW UNIT in the Hugging Face Reasoning course. We dive deep into the algorithm behind DeepSeek R1 with an advanced and hands-on guide to interpreting GRPO.
🔗
reasoning-course
This unit is super useful if you’re tuning models with reinforcement learning. It will help with:
- interpreting loss and reward progression during training runs
- selecting effective parameters for training
- reviewing and defining effective reward functions
This unit also works up smoothly toward the existing practical exercises form @mlabonne and Unsloth.
📣 Shout out to @ShirinYamani who wrote the unit. Follow for more great content.
🔗
This unit is super useful if you’re tuning models with reinforcement learning. It will help with:
- interpreting loss and reward progression during training runs
- selecting effective parameters for training
- reviewing and defining effective reward functions
This unit also works up smoothly toward the existing practical exercises form @mlabonne and Unsloth.
📣 Shout out to @ShirinYamani who wrote the unit. Follow for more great content.
- 1 reply
posted
an
update
10 months ago
Post
18414
✂️ 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.
💻 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.
💻 Colab: https://colab.research.google.com/drive/1RmLv-pCMBBsQGXQIM8yF-OdCNyoylUR1?usp=sharing
- 1 reply
posted
an
update
10 months ago
Post
6546
✂️ Gemma 3 Abliterated
I noticed that Gemma 3 was much more resilient to refusal removal than other models like Qwen 2.5.
I experimented with different recipes and improved the abliteration technique I wrote about last year.
It's still experimental but the refusal rate is super low in my tests. Enjoy!
mlabonne/gemma-3-4b-it-abliterated
mlabonne/gemma-3-12b-it-abliterated
mlabonne/gemma-3-27b-it-abliterated
I noticed that Gemma 3 was much more resilient to refusal removal than other models like Qwen 2.5.
I experimented with different recipes and improved the abliteration technique I wrote about last year.
It's still experimental but the refusal rate is super low in my tests. Enjoy!
mlabonne/gemma-3-4b-it-abliterated
mlabonne/gemma-3-12b-it-abliterated
mlabonne/gemma-3-27b-it-abliterated
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- 4 replies
reacted to
burtenshaw's
post with 🤗❤️
10 months ago
Post
4020
I’m super excited to work with
@mlabonne
to build the first practical example in the reasoning course.
🔗
reasoning-course
Here's a quick walk through of the first drop of material that works toward the use case:
- a fundamental introduction to reinforcement learning. Answering questions like, ‘what is a reward?’ and ‘how do we create an environment for a language model?’
- Then it focuses on Deepseek R1 by walking through the paper and highlighting key aspects. This is an old school way to learn ML topics, but it always works.
- Next, it takes to you Transformers Reinforcement Learning and demonstrates potential reward functions you could use. This is cool because it uses Marimo notebooks to visualise the reward.
- Finally, Maxime walks us through a real training notebook that uses GRPO to reduce generation length. I’m really into this because it works and Maxime took the time to validate it share assets and logging from his own runs for you to compare with.
Maxime’s work and notebooks have been a major part of the open source community over the last few years. I, like everyone, have learnt so much from them.
🔗
Here's a quick walk through of the first drop of material that works toward the use case:
- a fundamental introduction to reinforcement learning. Answering questions like, ‘what is a reward?’ and ‘how do we create an environment for a language model?’
- Then it focuses on Deepseek R1 by walking through the paper and highlighting key aspects. This is an old school way to learn ML topics, but it always works.
- Next, it takes to you Transformers Reinforcement Learning and demonstrates potential reward functions you could use. This is cool because it uses Marimo notebooks to visualise the reward.
- Finally, Maxime walks us through a real training notebook that uses GRPO to reduce generation length. I’m really into this because it works and Maxime took the time to validate it share assets and logging from his own runs for you to compare with.
Maxime’s work and notebooks have been a major part of the open source community over the last few years. I, like everyone, have learnt so much from them.