IntelligentEstate/Thoth_Warding-Llama-3B-IQ5_K_S-GGUF
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Intelligent Estate
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Connecting individuals with innovation: Emancipating and Truly Federalizing Private Intelligence and other Open Source Tech
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fuzzy-mittenz
updated
a
model
about 10 hours ago
fuzzy-mittenz
updated
a
collection
1 day ago
fuzzy-mittenz
updated
a
Space
1 day ago
fuzzy-mittenz
updated
a
model
1 day ago
Sri-Vigneshwar-DJ
posted
an
update
2 days ago
Post
543
Checkout phi-4 from Microsoft, dropped a day ago... If you ❤️ the Phi series, then here is the GGUF -
Sri-Vigneshwar-DJ/phi-4-GGUF. phi-4 is a 14B highly efficient open LLM that beats much larger models at math and reasoning - check out evaluations on the Open LLM.
Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting
Technical paper - https://arxiv.org/pdf/2412.08905 ; The Data Synthesis approach is interesting
Sri-Vigneshwar-DJ
posted
an
update
5 days ago
Post
2010
Just sharing a thought: I started using DeepSeek V3 a lot, and an idea struck me about agents "orchestrating during inference" on a test-time compute model like DeepSeek V3 or the O1 series.
Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
Agents (Instruction + Function Calls + Memory) execute during inference, and based on the output decision, a decision is made to scale the time to reason or perform other tasks.
fuzzy-mittenz
updated
3
models
6 days ago
fuzzy-mittenz
updated
2
models
7 days ago
fuzzy-mittenz
updated
2
collections
7 days ago
Sri-Vigneshwar-DJ
posted
an
update
7 days ago
Post
2313
Combining smolagents with Anthropic’s best practices simplifies building powerful AI agents:
1. Code-Based Agents: Write actions as Python code, reducing steps by 30%.
2. Prompt Chaining: Break tasks into sequential subtasks with validation gates.
3. Routing: Classify inputs and direct them to specialized handlers.
4. Fallback: Handle tasks even if classification fails.
https://huggingface.co./blog/Sri-Vigneshwar-DJ/building-effective-agents-with-anthropics-best-pra
1. Code-Based Agents: Write actions as Python code, reducing steps by 30%.
2. Prompt Chaining: Break tasks into sequential subtasks with validation gates.
3. Routing: Classify inputs and direct them to specialized handlers.
4. Fallback: Handle tasks even if classification fails.
https://huggingface.co./blog/Sri-Vigneshwar-DJ/building-effective-agents-with-anthropics-best-pra
fuzzy-mittenz
updated
2
collections
8 days ago
csabakecskemeti
posted
an
update
8 days ago
Post
525
Managed to run the Q2 quantized Deepseek V3 base locally
The quants are uploading (probably ~10-12hrs) here: DevQuasar/deepseek-ai.DeepSeek-V3-Base-GGUF
The quants are uploading (probably ~10-12hrs) here: DevQuasar/deepseek-ai.DeepSeek-V3-Base-GGUF
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- 1 reply
csabakecskemeti
posted
an
update
9 days ago
Post
590
Just wondering why the number of parameters changed in the model attributes/Model size from 685B to 684B after converting
deepseek-ai/DeepSeek-V3-Base from FP8 to BF16:
DevQuasar/deepseek-ai.DeepSeek-V3-Base-bf16
and not just for me:
opensourcerelease/DeepSeek-V3-Base-bf16
??
DevQuasar/deepseek-ai.DeepSeek-V3-Base-bf16
and not just for me:
opensourcerelease/DeepSeek-V3-Base-bf16
??