Confusion regarding operation/terminology of speculative decoding and sampling

# Summary

Speculative decoding not interfacing as expected.

My understanding is that a draft model should be specified, and that the draft model should itself be another llm (typically, smaller with similar/same token formatting). This understanding is derived from https://github.com/ggerganov/llama.cpp/pull/2926 which is referenced in some llama-cpp-python issues, specifically https://github.com/abetlen/llama-cpp-python/issues/675 . I've also come across https://github.com/abetlen/llama-cpp-python/pull/1120 .

There does not seem to be a direct interface to specify another llm, yet there is a "draft_model" argument in Llama() which instead points to a LlamaPromptLookupDecoding object. This is where my confusion arises from.

# Expected Behavior

Adjusting the current example on the main page, i would have expected speculative decoding to operate something like the following:

```
from llama_cpp import Llama

llama_draft = (
model_path="path/to/**small_draft_model.gguf**"
)

llama = Llama(
model_path="path/to/**big_primary_model**.gguf",
draft_model=llama_draft
)
```

# Current Behavior

this is the current suggested operation:

```
from llama_cpp import Llama
from llama_cpp.llama_speculative import LlamaPromptLookupDecoding

llama = Llama(
model_path="path/to/model.gguf",
draft_model=LlamaPromptLookupDecoding(num_pred_tokens=10) # num_pred_tokens is the number of tokens to predict 10 is the default and generally good for gpu, 2 performs better for cpu-only machines.
)
```

here, the draft_model is not another llm.

Just asking for some clarification and if it's currently possible or on the roadmap to implement the expected behaviour

cheers!