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ik_llama.cpp/github-data/pull_requests/328 - imatrix_ collect layer influence statistics.md
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### 🔀 [#328](https://github.com/ikawrakow/ik_llama.cpp/pull/328) - imatrix: collect layer influence statistics
| **Author** | `ikawrakow` |
| :--- | :--- |
| **State** | ❌ **Closed** |
| **Created** | 2025-04-14 |
| **Updated** | 2025-04-14 |
---
#### Description
@ubergarm
Here is how one can collect statistics about the activations change caused by a layer using cosine similarity.
---
#### 💬 Conversation
👤 **ubergarm** commented the **2025-04-14** at **14:39:20**:<br>
Holy smokes, amazing! I'm out for a couple nights, but going to pull this and try quick before leaving the house haha... Thanks!
---
👤 **ikawrakow** commented the **2025-04-14** at **16:02:02**:<br>
Does the last commit fix it? I had forgotten about having to strip the tensor name (and for whatever reason I didn't have the issue even though running on CUDA).
---
👤 **ubergarm** commented the **2025-04-14** at **16:10:14**:<br>
Yep, that did the trick! Thanks! I have a chart I just graphed, will put it here with logs before heading out.
---
👤 **ikawrakow** commented the **2025-04-14** at **16:13:51**:<br>
Using this on LLaMA-4-Scout, I get this as the layers sorted by importance (most important first):
```
======================== sorted layer importances
0: Layer 0, <cos_sim> = 0.147234
1: Layer 2, <cos_sim> = 0.338908
2: Layer 47, <cos_sim> = 0.413196
3: Layer 1, <cos_sim> = 0.626674
4: Layer 7, <cos_sim> = 0.835974
5: Layer 6, <cos_sim> = 0.841949
6: Layer 4, <cos_sim> = 0.844908
7: Layer 3, <cos_sim> = 0.849444
8: Layer 10, <cos_sim> = 0.869448
9: Layer 34, <cos_sim> = 0.875514
10: Layer 22, <cos_sim> = 0.880165
11: Layer 46, <cos_sim> = 0.881091
12: Layer 11, <cos_sim> = 0.887115
13: Layer 31, <cos_sim> = 0.889579
14: Layer 35, <cos_sim> = 0.893048
15: Layer 26, <cos_sim> = 0.897382
16: Layer 18, <cos_sim> = 0.898017
17: Layer 23, <cos_sim> = 0.898672
18: Layer 21, <cos_sim> = 0.900372
19: Layer 14, <cos_sim> = 0.902133
20: Layer 43, <cos_sim> = 0.908545
21: Layer 44, <cos_sim> = 0.908824
22: Layer 38, <cos_sim> = 0.909535
23: Layer 45, <cos_sim> = 0.909808
24: Layer 19, <cos_sim> = 0.911718
25: Layer 8, <cos_sim> = 0.911922
26: Layer 30, <cos_sim> = 0.913816
27: Layer 13, <cos_sim> = 0.916391
28: Layer 39, <cos_sim> = 0.917897
29: Layer 25, <cos_sim> = 0.917991
30: Layer 24, <cos_sim> = 0.918002
31: Layer 27, <cos_sim> = 0.918821
32: Layer 5, <cos_sim> = 0.920709
33: Layer 15, <cos_sim> = 0.921429
34: Layer 9, <cos_sim> = 0.922202
35: Layer 29, <cos_sim> = 0.923448
36: Layer 16, <cos_sim> = 0.924396
37: Layer 17, <cos_sim> = 0.925231
38: Layer 42, <cos_sim> = 0.925237
39: Layer 12, <cos_sim> = 0.926379
40: Layer 37, <cos_sim> = 0.926797
41: Layer 20, <cos_sim> = 0.92796
42: Layer 28, <cos_sim> = 0.933169
43: Layer 36, <cos_sim> = 0.936506
44: Layer 32, <cos_sim> = 0.936671
45: Layer 41, <cos_sim> = 0.939215
46: Layer 33, <cos_sim> = 0.940524
47: Layer 40, <cos_sim> = 0.948523
```
I had a pretty good L4-Scout recipe for `IQ2_K`
```
./bin/llama-quantize --imatrix l4_scout_imat_512.out --custom-q "ffn_gate_shexp=iq4_ks,ffn_up_shexp=iq4_ks,ffn_down_shexp=iq5_k,attn=iq4_ks,token_embd.weight=q4_K,output.weight=q6_K,blk\.[0-5]\.ffn_down_exps=iq4_ks,ffn_down_exps=iq3_k,ffn_up_exps=iq2_k,ffn_gate_exps=iq2_k" ../../iquants/models/l4_109B/Llama4-Scout-16x17B-BF16.gguf junk1.bin iq2_k
```
It arrived at a `PPL = 9.7545`, so nearly on par with Unsloth's `UD-Q2_K_XL`, despite being 2.6 GB smaller. The recipe uses `IQ4_KS` for the first 6 layers of `ffn_down_exps`. If instead I use layers `0,1,2,4,6,7`, PPL becomes `9.7066`, so we do get a small improvement from that (but using layer 47 instead of layer 4, which according to the metric would be the right thing to do, results in a worse outcome)
---
👤 **ubergarm** commented the **2025-04-14** at **16:28:24**:<br>
> (but using layer 47 instead of layer 4, which according to the metric would be the right thing to do, results in a worse outcome)
Very interesting. Yeah, I'm curious how much the input text for imatrix effects these cosine similarities as well.
I did a quick run with `llama-2-13b-chat.Q8_0.gguf` and plotted the results to compare against that [Layer-wise Quantization](https://arxiv.org/pdf/2406.17415) paper which suggests for this model the three most important layers would be 1, 2, and 40 while the least important would be 32, 33, and 34. Though I'm not sure how they got that final layer 40 cosine similarity.
<details>
<summary>Results Graph and Log of modified llama-imatrix -lsim</summary>
![lsim](https://github.com/user-attachments/assets/757081ca-8251-4405-a0df-3b4299caef54)
```bash
$ git branch | grep '*'
* ik/imatrix_lsim
$ git rev-parse --short HEAD
8bff04c9
$ ./build/bin/llama-imatrix --version
version: 3638 (8bff04c9)
built with cc (GCC) 14.2.1 20250128 for x86_64-pc-linux-gnu
$ ./build/bin/llama-imatrix \
--verbosity 1 \
-m /mnt/astrodata/llm/models/TheBloke/Llama-2-13B-chat-GGUF/llama-2-13b-chat.Q8_0.gguf \
-f calibration_data_v5_rc.txt \
-o imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat \
--layer-similarity \
--output-tensor-name ffn_down.weight \
--ctx-size 512 \
--threads 16
llama_model_loader: loaded meta data with 19 key-value pairs and 363 tensors from /mnt/astrodata/llm/models/TheBloke/Llama-2-13B-chat-GGUF/llama-2-13b-chat.Q8_0.gguf (version GGUF V2)
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = LLaMA v2
llama_model_loader: - kv 2: llama.context_length u32 = 4096
llama_model_loader: - kv 3: llama.embedding_length u32 = 5120
llama_model_loader: - kv 4: llama.block_count u32 = 40
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 13824
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 40
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 40
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 7
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.tokens arr[str,32000] = [
llama_model_loader: - kv 13: tokenizer.ggml.scores arr[f32,32000] = [
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,32000] = [
llama_model_loader: - kv 15: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 16: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 17: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 18: general.quantization_version u32 = 2
llama_model_loader: - type f32: 81 tensors
llama_model_loader: - type q8_0: 282 tensors
llm_load_vocab: special tokens cache size = 3
llm_load_vocab: token to piece cache size = 0.1684 MB
llm_load_print_meta: format = GGUF V2
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_embd = 5120
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_head = 40
llm_load_print_meta: n_head_kv = 40
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 5120
llm_load_print_meta: n_embd_v_gqa = 5120
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 13824
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 4096
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 13B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 13.016 B
llm_load_print_meta: model size = 12.881 GiB (8.501 BPW)
llm_load_print_meta: repeating layers = 12.556 GiB (8.501 BPW, 12.688 B parameters)
llm_load_print_meta: general.name = LLaMA v2
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_print_meta: max token length = 48
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090 Ti, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.17 MiB
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/41 layers to GPU
llm_load_tensors: CPU buffer size = 13189.86 MiB
....................................................................................................
llama_new_context_with_model: n_ctx = 512
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: mla_attn = 0
llama_new_context_with_model: attn_max_b = 0
llama_new_context_with_model: fused_moe = 0
llama_new_context_with_model: ser = -1, 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA_Host KV buffer size = 400.00 MiB
llama_new_context_with_model: KV self size = 400.00 MiB, K (f16): 200.00 MiB, V (f16): 200.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.12 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 248.54 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 21.01 MiB
llama_new_context_with_model: graph nodes = 1165
llama_new_context_with_model: graph splits = 443
system_info: n_threads = 16 / 32 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
compute_imatrix: tokenizing the input ..
compute_imatrix: tokenization took 102.865 ms
compute_imatrix: computing over 277 chunks with batch_size 512
compute_imatrix: 1.61 seconds per pass - ETA 7.40 minutes
[1]8.4429,[2]9.0054,[3]6.0236,[4]5.1203,[5]5.4399,[6]4.1193,[7]3.4893,[8]3.0374,[9]2.7789,
save_imatrix: stored collected data after 10 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[10]2.5790,[11]2.4134,[12]2.2756,[13]2.2770,[14]2.1808,[15]2.3420,[16]2.5229,[17]2.6719,[18]2.6744,[19]2.7924,
save_imatrix: stored collected data after 20 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[20]2.8546,[21]2.8265,[22]2.8108,[23]2.8379,[24]2.8256,[25]2.8160,[26]2.7995,[27]2.8185,[28]2.8211,[29]2.7253,
save_imatrix: stored collected data after 30 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[30]2.7067,[31]2.7767,[32]2.8058,[33]2.8023,[34]2.8008,[35]2.8470,[36]2.9396,[37]2.9690,[38]3.0215,[39]3.0308,
save_imatrix: stored collected data after 40 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[40]3.0702,[41]3.1383,[42]3.1813,[43]3.2972,[44]3.3731,[45]3.3880,[46]3.3992,[47]3.4017,[48]3.4401,[49]3.4639,
save_imatrix: stored collected data after 50 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[50]3.5158,[51]3.5482,[52]3.5576,[53]3.5790,[54]3.6064,[55]3.6440,[56]3.6855,[57]3.6976,[58]3.7151,[59]3.7365,
save_imatrix: stored collected data after 60 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[60]3.7198,[61]3.7105,[62]3.6868,[63]3.6517,[64]3.6643,[65]3.6569,[66]3.6335,[67]3.6463,[68]3.6364,[69]3.6098,
save_imatrix: stored collected data after 70 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[70]3.5796,[71]3.5663,[72]3.5423,[73]3.5180,[74]3.4853,[75]3.4602,[76]3.4389,[77]3.4079,[78]3.4590,[79]3.4885,
save_imatrix: stored collected data after 80 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[80]3.5384,[81]3.5655,[82]3.5703,[83]3.6146,[84]3.6383,[85]3.6433,[86]3.6712,[87]3.6529,[88]3.6616,[89]3.6659,
save_imatrix: stored collected data after 90 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[90]3.6578,[91]3.6563,[92]3.7242,[93]3.7772,[94]3.8348,[95]3.8650,[96]3.9093,[97]3.9215,[98]3.9316,[99]3.9614,
save_imatrix: stored collected data after 100 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[100]3.9870,[101]3.9926,[102]4.0022,[103]4.0078,[104]4.0241,[105]4.0021,[106]4.0216,[107]4.0284,[108]4.0321,[109]4.0764,
save_imatrix: stored collected data after 110 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[110]4.1078,[111]4.1195,[112]4.1347,[113]4.1305,[114]4.1078,[115]4.1262,[116]4.1317,[117]4.1305,[118]4.1626,[119]4.1574,
save_imatrix: stored collected data after 120 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[120]4.1461,[121]4.1457,[122]4.1450,[123]4.1398,[124]4.1433,[125]4.1565,[126]4.1668,[127]4.1812,[128]4.1865,[129]4.1768,
save_imatrix: stored collected data after 130 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[130]4.1734,[131]4.2002,[132]4.2067,[133]4.2000,[134]4.1810,[135]4.2081,[136]4.2197,[137]4.2454,[138]4.2620,[139]4.2528,
save_imatrix: stored collected data after 140 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[140]4.2720,[141]4.2953,[142]4.3222,[143]4.3403,[144]4.3690,[145]4.3883,[146]4.4270,[147]4.4502,[148]4.4468,[149]4.4334,
save_imatrix: stored collected data after 150 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[150]4.4587,[151]4.4729,[152]4.4900,[153]4.4847,[154]4.5262,[155]4.5341,[156]4.5600,[157]4.5479,[158]4.5551,[159]4.5649,
save_imatrix: stored collected data after 160 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[160]4.5906,[161]4.5990,[162]4.6071,[163]4.5763,[164]4.5561,[165]4.5295,[166]4.5200,[167]4.5107,[168]4.5148,[169]4.5286,
save_imatrix: stored collected data after 170 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[170]4.5453,[171]4.5400,[172]4.5458,[173]4.5576,[174]4.5648,[175]4.5852,[176]4.6067,[177]4.6488,[178]4.6855,[179]4.7140,
save_imatrix: stored collected data after 180 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[180]4.7434,[181]4.7628,[182]4.7820,[183]4.7710,[184]4.7853,[185]4.8189,[186]4.8460,[187]4.8477,[188]4.8348,[189]4.8479,
save_imatrix: stored collected data after 190 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[190]4.8627,[191]4.8802,[192]4.9172,[193]4.9458,[194]4.9610,[195]4.9765,[196]4.9902,[197]5.0011,[198]4.9910,[199]4.9894,
save_imatrix: stored collected data after 200 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[200]4.9818,[201]4.9788,[202]4.9866,[203]4.9945,[204]4.9993,[205]5.0029,[206]5.0112,[207]5.0217,[208]5.0205,[209]5.0324,
save_imatrix: stored collected data after 210 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[210]5.0529,[211]5.0635,[212]5.0756,[213]5.0723,[214]5.0873,[215]5.0975,[216]5.1073,[217]5.1171,[218]5.1213,[219]5.1426,
save_imatrix: stored collected data after 220 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[220]5.1445,[221]5.1374,[222]5.1562,[223]5.1764,[224]5.1933,[225]5.1982,[226]5.2087,[227]5.2195,[228]5.2394,[229]5.2261,
save_imatrix: stored collected data after 230 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[230]5.2269,[231]5.2197,[232]5.2361,[233]5.2403,[234]5.2375,[235]5.2346,[236]5.2321,[237]5.2252,[238]5.2216,[239]5.2124,
save_imatrix: stored collected data after 240 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[240]5.2077,[241]5.2022,[242]5.1967,[243]5.1920,[244]5.1865,[245]5.1891,[246]5.1968,[247]5.2214,[248]5.2460,[249]5.2682,
save_imatrix: stored collected data after 250 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[250]5.2993,[251]5.3283,[252]5.3306,[253]5.3429,[254]5.3461,[255]5.3590,[256]5.3653,[257]5.3726,[258]5.3645,[259]5.3569,
save_imatrix: stored collected data after 260 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[260]5.3674,[261]5.3848,[262]5.3862,[263]5.3887,[264]5.3941,[265]5.4030,[266]5.4143,[267]5.4201,[268]5.4234,[269]5.4354,
save_imatrix: stored collected data after 270 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
[270]5.4386,[271]5.4424,[272]5.4521,[273]5.4564,[274]5.4639,[275]5.4791,[276]5.4830,[277]5.4973,
save_imatrix: stored collected data after 277 chunks in imatrix-calibration_data_v5_rc-llama-2-13b-chat.dat
Final estimate: PPL = 5.4973 +/- 0.05449
llama_print_timings: load time = 2147.62 ms
llama_print_timings: sample time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_print_timings: prompt eval time = 424676.05 ms / 141824 tokens ( 2.99 ms per token, 333.96 tokens per second)
llama_print_timings: eval time = 0.00 ms / 1 runs ( 0.00 ms per token, inf tokens per second)
llama_print_timings: total time = 428646.74 ms / 141825 tokens
======================== sorted layer importances
0: Layer 0, <cos_sim> = 0.0804587
1: Layer 1, <cos_sim> = 0.816333
2: Layer 3, <cos_sim> = 0.855579
3: Layer 2, <cos_sim> = 0.870939
4: Layer 5, <cos_sim> = 0.882884
5: Layer 7, <cos_sim> = 0.886822
6: Layer 6, <cos_sim> = 0.891157
7: Layer 4, <cos_sim> = 0.897281
8: Layer 8, <cos_sim> = 0.898462
9: Layer 9, <cos_sim> = 0.900521
10: Layer 10, <cos_sim> = 0.910075
11: Layer 11, <cos_sim> = 0.912746
12: Layer 12, <cos_sim> = 0.916058
13: Layer 13, <cos_sim> = 0.918256
14: Layer 15, <cos_sim> = 0.921156
15: Layer 16, <cos_sim> = 0.922013
16: Layer 17, <cos_sim> = 0.923089
17: Layer 14, <cos_sim> = 0.923667
18: Layer 18, <cos_sim> = 0.935129
19: Layer 21, <cos_sim> = 0.935497
20: Layer 38, <cos_sim> = 0.938946
21: Layer 20, <cos_sim> = 0.939555
22: Layer 19, <cos_sim> = 0.939993
23: Layer 22, <cos_sim> = 0.949833
24: Layer 37, <cos_sim> = 0.952011
25: Layer 23, <cos_sim> = 0.955484
26: Layer 36, <cos_sim> = 0.956569
27: Layer 24, <cos_sim> = 0.96045
28: Layer 25, <cos_sim> = 0.963482
29: Layer 35, <cos_sim> = 0.96357
30: Layer 26, <cos_sim> = 0.963717
31: Layer 34, <cos_sim> = 0.966742
32: Layer 27, <cos_sim> = 0.967312
33: Layer 33, <cos_sim> = 0.967905
34: Layer 28, <cos_sim> = 0.96873
35: Layer 32, <cos_sim> = 0.969066
36: Layer 30, <cos_sim> = 0.969155
37: Layer 29, <cos_sim> = 0.969895
38: Layer 31, <cos_sim> = 0.969988
======================== sorted attention importances
0: Layer 0, <cos_sim> = 0.253426
1: Layer 1, <cos_sim> = 0.38511
2: Layer 2, <cos_sim> = 0.568119
3: Layer 3, <cos_sim> = 0.70009
4: Layer 4, <cos_sim> = 0.753275
5: Layer 5, <cos_sim> = 0.783473
6: Layer 7, <cos_sim> = 0.822807
7: Layer 6, <cos_sim> = 0.833536
8: Layer 8, <cos_sim> = 0.85773
9: Layer 9, <cos_sim> = 0.869933
10: Layer 10, <cos_sim> = 0.870238
11: Layer 11, <cos_sim> = 0.876139
12: Layer 12, <cos_sim> = 0.880516
13: Layer 15, <cos_sim> = 0.883828
14: Layer 14, <cos_sim> = 0.890839
15: Layer 13, <cos_sim> = 0.891501
16: Layer 17, <cos_sim> = 0.892781
17: Layer 16, <cos_sim> = 0.897206
18: Layer 20, <cos_sim> = 0.90434
19: Layer 19, <cos_sim> = 0.905305
20: Layer 21, <cos_sim> = 0.905376
21: Layer 18, <cos_sim> = 0.910555
22: Layer 23, <cos_sim> = 0.921951
23: Layer 26, <cos_sim> = 0.926056
24: Layer 25, <cos_sim> = 0.927626
25: Layer 24, <cos_sim> = 0.928499
26: Layer 28, <cos_sim> = 0.936632
27: Layer 22, <cos_sim> = 0.936688
28: Layer 27, <cos_sim> = 0.939766
29: Layer 29, <cos_sim> = 0.946173
30: Layer 31, <cos_sim> = 0.950643
31: Layer 39, <cos_sim> = 0.951655
32: Layer 30, <cos_sim> = 0.952739
33: Layer 32, <cos_sim> = 0.955543
34: Layer 36, <cos_sim> = 0.955873
35: Layer 34, <cos_sim> = 0.957643
36: Layer 33, <cos_sim> = 0.958336
37: Layer 38, <cos_sim> = 0.960393
38: Layer 37, <cos_sim> = 0.960471
39: Layer 35, <cos_sim> = 0.962264
======================== sorted ffn importances
0: Layer 0, <cos_sim> = 0.562579
1: Layer 1, <cos_sim> = 0.580676
2: Layer 2, <cos_sim> = 0.616983
3: Layer 3, <cos_sim> = 0.706686
4: Layer 4, <cos_sim> = 0.731208
5: Layer 6, <cos_sim> = 0.756786
6: Layer 5, <cos_sim> = 0.757354
7: Layer 7, <cos_sim> = 0.796257
8: Layer 8, <cos_sim> = 0.815461
9: Layer 10, <cos_sim> = 0.824589
10: Layer 9, <cos_sim> = 0.826519
11: Layer 11, <cos_sim> = 0.846745
12: Layer 13, <cos_sim> = 0.859737
13: Layer 14, <cos_sim> = 0.86228
14: Layer 12, <cos_sim> = 0.866246
15: Layer 16, <cos_sim> = 0.866582
16: Layer 15, <cos_sim> = 0.868753
17: Layer 18, <cos_sim> = 0.870342
18: Layer 19, <cos_sim> = 0.870973
19: Layer 17, <cos_sim> = 0.874143
20: Layer 20, <cos_sim> = 0.886187
21: Layer 22, <cos_sim> = 0.892857
22: Layer 21, <cos_sim> = 0.902702
23: Layer 23, <cos_sim> = 0.902868
24: Layer 24, <cos_sim> = 0.904163
25: Layer 25, <cos_sim> = 0.904319
26: Layer 27, <cos_sim> = 0.914438
27: Layer 26, <cos_sim> = 0.917688
28: Layer 28, <cos_sim> = 0.926051
29: Layer 38, <cos_sim> = 0.927326
30: Layer 29, <cos_sim> = 0.92942
31: Layer 30, <cos_sim> = 0.932488
32: Layer 35, <cos_sim> = 0.934298
33: Layer 31, <cos_sim> = 0.934668
34: Layer 37, <cos_sim> = 0.935018
35: Layer 33, <cos_sim> = 0.936569
36: Layer 32, <cos_sim> = 0.938647
37: Layer 36, <cos_sim> = 0.938813
38: Layer 34, <cos_sim> = 0.94036
```
</details>
Really appreciate your implementing this for further experimentation! Gotta run for now but will dig in more later this week! Thanks!
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