deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
Text Generation
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Robert collins
robbiemu
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5 days ago
deepseek-ai/DeepSeek-R1-Distill-Qwen-32B:YaRN block required?
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7 days ago
deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
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11 days ago
jinaai/ReaderLM-v2
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robbiemu's activity
YaRN block required?
3
#5 opened 7 days ago
by
robbiemu
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7 days ago
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11 days ago
reacted to
burtenshaw's
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12 days ago
Post
37999
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We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
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- 22 replies
Question on target-language quantization
7
#1 opened about 1 month ago
by
robbiemu
context-size is 32k right?
5
#2 opened 21 days ago
by
robbiemu
Language samples
4
#1 opened 21 days ago
by
robbiemu
reacted to
bartowski's
post with 👍
28 days ago
Post
25413
Looks like Q4_0_N_M file types are going away
Before you panic, there's a new "preferred" method which is online (I prefer the term on-the-fly) repacking, so if you download Q4_0 and your setup can benefit from repacking the weights into interleaved rows (what Q4_0_4_4 was doing), it will do that automatically and give you similar performance (minor losses I think due to using intrinsics instead of assembly, but intrinsics are more maintainable)
You can see the reference PR here:
https://github.com/ggerganov/llama.cpp/pull/10446
So if you update your llama.cpp past that point, you won't be able to run Q4_0_4_4 (unless they add backwards compatibility back), but Q4_0 should be the same speeds (though it may currently be bugged on some platforms)
As such, I'll stop making those newer model formats soon, probably end of this week unless something changes, but you should be safe to download and Q4_0 quants and use those !
Also IQ4_NL supports repacking though not in as many shapes yet, but should get a respectable speed up on ARM chips, PR for that can be found here: https://github.com/ggerganov/llama.cpp/pull/10541
Remember, these are not meant for Apple silicon since those use the GPU and don't benefit from the repacking of weights
Before you panic, there's a new "preferred" method which is online (I prefer the term on-the-fly) repacking, so if you download Q4_0 and your setup can benefit from repacking the weights into interleaved rows (what Q4_0_4_4 was doing), it will do that automatically and give you similar performance (minor losses I think due to using intrinsics instead of assembly, but intrinsics are more maintainable)
You can see the reference PR here:
https://github.com/ggerganov/llama.cpp/pull/10446
So if you update your llama.cpp past that point, you won't be able to run Q4_0_4_4 (unless they add backwards compatibility back), but Q4_0 should be the same speeds (though it may currently be bugged on some platforms)
As such, I'll stop making those newer model formats soon, probably end of this week unless something changes, but you should be safe to download and Q4_0 quants and use those !
Also IQ4_NL supports repacking though not in as many shapes yet, but should get a respectable speed up on ARM chips, PR for that can be found here: https://github.com/ggerganov/llama.cpp/pull/10541
Remember, these are not meant for Apple silicon since those use the GPU and don't benefit from the repacking of weights
- 16 replies
Hi Sail team! a request for data for quantization of your model
1
#2 opened 28 days ago
by
robbiemu
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a
model
about 2 months ago
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a
Space
2 months ago
liked
2
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2 months ago
liked
a
Space
2 months ago
liked
3
models
3 months ago
possible issue with tokenizer
5
#2 opened 3 months ago
by
robbiemu
Fine tuning question
1
#2 opened 3 months ago
by
robbiemu
liked
a
dataset
3 months ago