Quantization imrovements for k_quants (#2707)

Quantization imrovements for k_quants (#2707)

* Improve LLaMA-2 2-, 3- and 4-bit quantization

* Q3_K_S: use Q5_K for 1st 2 layers of attention.wv and feed_forward.w2
* Q4_K_S: use Q6_K for 1st 2 layers of attention.wv and feed_forward.w2
* Q2_K and Q3_K_M: use Q5_K instead of Q4_K for 1st 2 layers of
  attention.wv and feed_forward.w2

This leads to a slight model sized increase as follows:
Q2_K  : 2.684G vs 2.670G
Q3_K_S: 2.775G vs 2.745G
Q3_K_M: 3.071G vs 3.057G
Q4_K_S: 3.592G vs 3.563G

LLaMA-2 PPL for context 512 changes as follows:
Q2_K  : 6.6691 vs 6.8201
Q3_K_S: 6.2129 vs 6.2584
Q3_K_M: 6.0387 vs 6.1371
Q4_K_S: 5.9138 vs 6.0041

There are improvements for LLaMA-1 as well, but they are
way smaller than the above.

* Minor 4-bit quantization improvement

For the same model size as previus commit, we get
PPL = 5.9069 vs 5.9138.

* Some more fine tuning

* Adding make_qkx2_quants

With it, we get PPL = 5.8828 for L2-7B Q4_K_S.

* Another minor improvement

* Q2_K improvement

Smaller model, lower perplexity.
 7B: file size = 2.632G, PPL = 6.3772 vs original 2.670G PPL = 6.8201
12B: file size = 5.056G, PPL = 5.4577 vs original 5.130G PPL = 5.7178

It is mostly Q3_K except for tok_embeddings, attention.wq, attention.wk,
which are Q2_K

* Iterating

* Revert Q5_K back to make_qkx1_quants

* Better Q6_K

* make_qkx2_quants is better for Q5_K after all

* Fix after rebasing on master

* Fix for changed tensor names

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Co-authored-by: Iwan Kawrakow <redacted>