arcee-ai/Trinity-Mini-FP8-Block
Text Generation • 26B • Updated
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Recent Activity
anneketh-vij
updated a
model about 1 hour ago
anneketh-vij
updated a
model about 14 hours ago
danielhanchen
posted an update 5 days ago
Post
2945
Introducing Unsloth Studio ✨
A new open-source web UI to train and run LLMs.
• Run models locally on Mac, Windows, Linux
• Train 500+ models 2x faster with 70% less VRAM
• Supports GGUF, vision, audio, embedding models
• Auto-create datasets from PDF, CSV, DOCX
• Self-healing tool calling and code execution
• Compare models side by side + export to GGUF
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/new/studio
Available now on Hugging Face, NVIDIA, Docker and Colab.
A new open-source web UI to train and run LLMs.
• Run models locally on Mac, Windows, Linux
• Train 500+ models 2x faster with 70% less VRAM
• Supports GGUF, vision, audio, embedding models
• Auto-create datasets from PDF, CSV, DOCX
• Self-healing tool calling and code execution
• Compare models side by side + export to GGUF
GitHub: https://github.com/unslothai/unsloth
Blog and Guide: https://unsloth.ai/docs/new/studio
Available now on Hugging Face, NVIDIA, Docker and Colab.
danielhanchen
posted an update 9 days ago
Post
3767
We collaborated with NVIDIA to teach you about Reinforcement Learning and RL environments. 💚 Learn:
• Why RL environments matter + how to build them
• When RL is better than SFT
• GRPO and RL best practices
• How verifiable rewards and RLVR work
Blog: https://unsloth.ai/blog/rl-environments
• Why RL environments matter + how to build them
• When RL is better than SFT
• GRPO and RL best practices
• How verifiable rewards and RLVR work
Blog: https://unsloth.ai/blog/rl-environments
- 4 replies
danielhanchen
posted an update 17 days ago
Post
2933
You can now fine-tune Qwen3.5 for free with our notebook! 🔥
You just need 5GB VRAM to train Qwen3.5-2B LoRA locally!
Unsloth trains Qwen3.5 1.5x faster with 50% less VRAM.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/models/qwen3.5/fine-tune
Qwen3.5-4B Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb
You just need 5GB VRAM to train Qwen3.5-2B LoRA locally!
Unsloth trains Qwen3.5 1.5x faster with 50% less VRAM.
GitHub: https://github.com/unslothai/unsloth
Guide: https://unsloth.ai/docs/models/qwen3.5/fine-tune
Qwen3.5-4B Colab: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen3_5_(4B)_Vision.ipynb
MaziyarPanahi
posted an update 19 days ago
Post
4618
DNA, mRNA, proteins, AI. I spent the last year going deep into computational biology as an ML engineer. This is Part I of what I found. 🧬
In 2024, AlphaFold won the Nobel Prize in Chemistry.
By 2026, the open-source community had built alternatives that outperform it.
That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.
I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.
New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape
Hope you all enjoy! 🤗
In 2024, AlphaFold won the Nobel Prize in Chemistry.
By 2026, the open-source community had built alternatives that outperform it.
That's the story I find most interesting about protein AI right now. Not just the science (which is incredible), but the speed at which open-source caught up. Multiple teams, independently, reproduced and then exceeded AlphaFold 3's accuracy with permissive licenses. The field went from prediction to generation: we're not just modeling known proteins anymore, we're designing new ones.
I spent months mapping this landscape for ML engineers. What the architectures actually are (spoiler: transformers and diffusion models), which tools to use for what, and which ones you can actually ship commercially.
New post on the Hugging Face blog: https://huggingface.co/blog/MaziyarPanahi/protein-ai-landscape
Hope you all enjoy! 🤗
- 2 replies
danielhanchen
posted an update 20 days ago
Post
5259
Qwen releases 4 new Qwen3.5 Small models: 0.8B • 2B • 4B • 9B!
Run Qwen3.5-0.8B, 2B and 4B on your phone. Run 9B on 6GB RAM.
The vision reasoning LLMs perform better than models 4x their size.
GGUFs to run: https://huggingface.co/collections/unsloth/qwen35
Guide: https://unsloth.ai/docs/models/qwen3.5
Run Qwen3.5-0.8B, 2B and 4B on your phone. Run 9B on 6GB RAM.
The vision reasoning LLMs perform better than models 4x their size.
GGUFs to run: https://huggingface.co/collections/unsloth/qwen35
Guide: https://unsloth.ai/docs/models/qwen3.5
- 5 replies
danielhanchen
posted an update 26 days ago
Post
3428
100,000+ models trained with Unsloth have now been open-sourced on 🤗Hugging Face! 🦥
Here are the most popular ones you can run local:
1. TeichAI - GLM-4.7-Flash distilled from Claude 4.5 Opus (high)
2. Zed - Qwen Coder 7B fine-tuned for stronger coding
3. DavidAU - Llama-3.3-8B distilled from Claude 4.5 Opus (high)
4. huihui - gpt-oss made “abliberated”
Links to models:
1. TeichAI: TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF
2. Zed: zed-industries/zeta
3. DavidAU: DavidAU/Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning
4. huihui: huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
See all the 100K latest models fine-tuned with Unsloth here: https://huggingface.co/models?other=u
Here are the most popular ones you can run local:
1. TeichAI - GLM-4.7-Flash distilled from Claude 4.5 Opus (high)
2. Zed - Qwen Coder 7B fine-tuned for stronger coding
3. DavidAU - Llama-3.3-8B distilled from Claude 4.5 Opus (high)
4. huihui - gpt-oss made “abliberated”
Links to models:
1. TeichAI: TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF
2. Zed: zed-industries/zeta
3. DavidAU: DavidAU/Llama3.3-8B-Instruct-Thinking-Claude-4.5-Opus-High-Reasoning
4. huihui: huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
See all the 100K latest models fine-tuned with Unsloth here: https://huggingface.co/models?other=u
- 2 replies
anneketh-vij
authored 2
papers 28 days ago
Alyosha11
authored a
paper about 1 month ago
pranavkr
authored a
paper about 1 month ago
arthurbresnu
authored a
paper about 1 month ago
MaziyarPanahi
authored a
paper about 1 month ago
Jackmin108
authored a
paper about 1 month ago
chargoddard
authored a
paper about 1 month ago
danielhanchen
posted an update about 1 month ago
Post
2684
We collabed with HF on showing how you can use HF Jobs and Unsloth! https://huggingface.co/blog/unsloth-jobs
danielhanchen
posted an update about 1 month ago
Post
5669
You can now run Qwen3.5 locally! 💜
Qwen3.5-397B-A17B is an open MoE vision reasoning LLM for agentic coding & chat. It performs on par with Gemini 3 Pro, Claude Opus 4.5 & GPT-5.2.
GGUF: unsloth/Qwen3.5-397B-A17B-GGUF
Run Dynamic 3-bit on a 192GB Mac for 20 tokens/s.
Guide: https://unsloth.ai/docs/models/qwen3.5
Qwen3.5-397B-A17B is an open MoE vision reasoning LLM for agentic coding & chat. It performs on par with Gemini 3 Pro, Claude Opus 4.5 & GPT-5.2.
GGUF: unsloth/Qwen3.5-397B-A17B-GGUF
Run Dynamic 3-bit on a 192GB Mac for 20 tokens/s.
Guide: https://unsloth.ai/docs/models/qwen3.5
- 9 replies
danielhanchen
posted an update about 1 month ago
Post
8501
You can now run MiniMax-2.5 locally! 🚀
At 230B parameters, MiniMax-2.5 is the strongest LLM under 700B params, delivering SOTA agentic coding & chat.
Run Dynamic 3/4-bit on a 128GB Mac for 20 tokens/s.
Guide: https://unsloth.ai/docs/models/minimax-2.5
GGUF: unsloth/MiniMax-M2.5-GGUF
At 230B parameters, MiniMax-2.5 is the strongest LLM under 700B params, delivering SOTA agentic coding & chat.
Run Dynamic 3/4-bit on a 128GB Mac for 20 tokens/s.
Guide: https://unsloth.ai/docs/models/minimax-2.5
GGUF: unsloth/MiniMax-M2.5-GGUF
- 1 reply
MaziyarPanahi
posted an update about 1 month ago
Post
2288
Announcing: OpenMed Multilingual PII Detection Models
Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.
All Apache 2.0 licensed. Free for commercial use. No restrictions.
Performance:
- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)
All top-10 models per language exceed 96% F1
Coverage:
55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns
Training Data:
French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples
Why Multilingual?
European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.
Effective de-identification requires:
- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.
HIPAA & GDPR Compliance
Built for US and European privacy regulations:
- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.
Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across
https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
Today I am releasing 105 open-source models for Personally Identifiable Information (PII) detection in French, German, and Italian.
All Apache 2.0 licensed. Free for commercial use. No restrictions.
Performance:
- French: 97.97% F1 (top model)
- German: 97.61% F1 (top model)
- Italian: 97.28% F1 (top model)
All top-10 models per language exceed 96% F1
Coverage:
55+ PII entity types per language
Native ID formats: NSS (French), Sozialversicherungsnummer (German), Codice Fiscale (Italian)
Language-specific address, phone, and name patterns
Training Data:
French: 49,580 samples
German: 42,250 samples
Italian: 40,944 samples
Why Multilingual?
European healthcare operates in European languages. Clinical notes, patient records, and medical documents are generated in French, German, Italian, and other languages.
Effective de-identification requires:
- Native language understanding — not translation
- Local ID format recognition — each country has unique patterns
- Cultural context awareness — names, addresses, and formats vary
- These models deliver production-ready accuracy without requiring data to leave your infrastructure or language.
HIPAA & GDPR Compliance
Built for US and European privacy regulations:
- On-premise deployment: Process data locally with zero external dependencies
- Data sovereignty: No API calls, no cloud services, no cross-border transfers
- Air-gapped capable: Deploy in fully isolated environments if required
- Regulatory-grade accuracy: Supporting Expert Determination standards
- HIPAA and GDPR compliance across languages, without compliance gaps.
Use Cases
- Hospital EHR systems: Automated patient record de-identification
- Clinical research: Multilingual dataset preparation for studies
- Insurance companies: Claims processing across
https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
- 1 reply