HPI-MML/cerrora
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JOtholt
updated
a
model
23 days ago
JOtholt
published
a
model
24 days ago
yanghaojin
posted
an
update
4 months ago
Post
264
I’m excited to share this article https://huggingface.co/blog/yanghaojin/greenbitais-4-bit-models-redefine-ai-efficiency, showcasing the power of low-bit small models in programming tasks. 🚀 Stay tuned—the next post will dive into test cases for small model–driven agentic applications!
yanghaojin
posted
an
update
4 months ago
Post
176
Our new blog post Smaller Models, Smarter Agents 🚀 https://huggingface.co/blog/yanghaojin/greenbit-3-bit-stronger-reasoning
DeepSeek’s R1-0528 proved that 8B can reason like 235B. Anthropic showed that multi-agent systems boost performance by 90%. The challenge? Both approaches burn massive compute and tokens.
💡 GreenBitAI cracked the code:
We launched the first 3-bit deployable reasoning model — DeepSeek-R1-0528-Qwen3-8B (3.2-bit).
✅ Runs complex multi-agent research tasks (e.g. Pop Mart market analysis)
✅ Executes flawlessly on an Apple M3 laptop in under 5 minutes
✅ 1351 tokens/s prefill, 105 tokens/s decode
✅ Near-FP16 reasoning quality with just 30–40% token usage
This is how extreme compression meets collaborative intelligence — making advanced reasoning practical on edge devices.
DeepSeek’s R1-0528 proved that 8B can reason like 235B. Anthropic showed that multi-agent systems boost performance by 90%. The challenge? Both approaches burn massive compute and tokens.
💡 GreenBitAI cracked the code:
We launched the first 3-bit deployable reasoning model — DeepSeek-R1-0528-Qwen3-8B (3.2-bit).
✅ Runs complex multi-agent research tasks (e.g. Pop Mart market analysis)
✅ Executes flawlessly on an Apple M3 laptop in under 5 minutes
✅ 1351 tokens/s prefill, 105 tokens/s decode
✅ Near-FP16 reasoning quality with just 30–40% token usage
This is how extreme compression meets collaborative intelligence — making advanced reasoning practical on edge devices.
yanghaojin
posted
an
update
over 1 year ago
Post
923
Dear community,
Please check our recent blog post, "GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing". A cheaper and more efficient SFT scheme for quantized LLMs is provided.
https://huggingface.co/blog/NicoNico/green-bit-llm
Please check our recent blog post, "GPU Poor Savior: Revolutionizing Low-Bit Open Source LLMs and Cost-Effective Edge Computing". A cheaper and more efficient SFT scheme for quantized LLMs is provided.
https://huggingface.co/blog/NicoNico/green-bit-llm
yanghaojin
posted
an
update
over 1 year ago
Post
2315
Full parameter fine-tuning of the LLaMA-3 8B model using a single GTX 3090 GPU with 24GB of graphics memory?
Please check out our tool for fine-tuning, inferencing, and evaluating GreenBitAI's low-bit LLMs:
https://github.com/GreenBitAI/green-bit-llm
Model Zoo:
GreenBitAI
Please check out our tool for fine-tuning, inferencing, and evaluating GreenBitAI's low-bit LLMs:
https://github.com/GreenBitAI/green-bit-llm
Model Zoo:
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- 3 replies
yanghaojin
posted
an
update
over 1 year ago
Post
1356
Dear all,
We are happy to share that we have just open-sourced over 200 low-bit LLMs. For the MLX community, we have prepared 2-4 bit versions of mainstream LLMs. You can visit the following collection to access them: GreenBitAI/greenbitai-mlx-llm-6614eb6ceb8da657c2b4ed58.
These low-bit models can be conveniently used through our open-source tool at https://github.com/GreenBitAI/gbx-lm.
Compared to other open-source quantization algorithms, these models provide better accuracy retention. We have provided some model evaluation results here:
https://github.com/GreenBitAI/green-bit-llm/blob/main/green_bit_llm/evaluation/README.md.
You can also evaluate the models yourself using the evaluation script we provided.
We are happy to share that we have just open-sourced over 200 low-bit LLMs. For the MLX community, we have prepared 2-4 bit versions of mainstream LLMs. You can visit the following collection to access them: GreenBitAI/greenbitai-mlx-llm-6614eb6ceb8da657c2b4ed58.
These low-bit models can be conveniently used through our open-source tool at https://github.com/GreenBitAI/gbx-lm.
Compared to other open-source quantization algorithms, these models provide better accuracy retention. We have provided some model evaluation results here:
https://github.com/GreenBitAI/green-bit-llm/blob/main/green_bit_llm/evaluation/README.md.
You can also evaluate the models yourself using the evaluation script we provided.
- 1 reply
yanghaojin
authored
a
paper
about 2 years ago