How to Pair with Larger Models
This model is very popular. Congratulations to you, and thank you for your help. I have a question for you:
How should this model work with larger models? Could you provide a specific textual description of the process?
For example, should SmallThinker-3B be instructed to write out its thought processes (without writing down the answer) first, then let the larger model reference these thoughts to generate an answer?
Alternatively, could we not give any additional instructions and instead have SmallThinker-3B directly respond to the questions, followed by the larger model referencing those responses before generating the final answer?
I would appreciate it if you could outline two possible workflows or more specific prompts.
This is a very nice question. I believe one of the most straightforward approaches is to use the smaller model as a "draft model" for the larger model. This could directly improve inference speed by draft "easy" token using smallthinker. You can try this method with llama.cpp.
Additionally, if we’re exploring how smaller and larger models can collaborate effectively, one possible method might be to package the smaller model’s response along with the original question and send them together to the larger model. This approach could allow the larger model to leverage the smaller model’s preliminary reasoning while refining and expanding upon it for the final output.
So far, my focus has primarily been on using speculative decoding to accelerate the larger model’s inference process. I haven’t yet experimented with other methods of collaboration. Thank you for raising such an interesting question—it’s definitely worth exploring further.
If you use VLLM and QWQ models, an error message will be displayed, indicating that the size of the vocabulary is inconsistent
This model is very popular. Congratulations to you, and thank you for your help. I have a question for you:
How should this model work with larger models? Could you provide a specific textual description of the process?
For example, should SmallThinker-3B be instructed to write out its thought processes (without writing down the answer) first, then let the larger model reference these thoughts to generate an answer?Alternatively, could we not give any additional instructions and instead have SmallThinker-3B directly respond to the questions, followed by the larger model referencing those responses before generating the final answer?
I would appreciate it if you could outline two possible workflows or more specific prompts.
Hombre , necesitaria mas tiempo para orientar. y explicar la realidad pero bueno poco a poco . y gracias
ok if you speak ingles or chinesse no problem when you use diferent languaje big problem similar speak gribraltar becuase any words ingles or chiniese, its no real thinks, or cadena de pensamientos, this is consecucion de pensamiento, diferent. its no realy inteligence, its blucle when real problem becuase "consecucion de pensamiento"