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Model: legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF
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---
base_model: princeton-nlp/Llama-3-Instruct-8B-SimPO
inference: false
library_name: gguf
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- quantized
- GGUF
- imatrix
- quantization
- imat
- imatrix
- static
---
# Llama-3-Instruct-8B-SimPO-IMat-GGUF
_Llama.cpp imatrix quantization of princeton-nlp/Llama-3-Instruct-8B-SimPO_
Original Model: [princeton-nlp/Llama-3-Instruct-8B-SimPO](https://huggingface.co/princeton-nlp/Llama-3-Instruct-8B-SimPO)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3023](https://github.com/ggerganov/llama.cpp/releases/tag/b3023)
IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw)
- [Llama-3-Instruct-8B-SimPO-IMat-GGUF](#llama-3-instruct-8b-simpo-imat-gguf)
- [Files](#files)
- [IMatrix](#imatrix)
- [Common Quants](#common-quants)
- [All Quants](#all-quants)
- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
- [Inference](#inference)
- [Simple chat template](#simple-chat-template)
- [Chat template with system prompt](#chat-template-with-system-prompt)
- [Llama.cpp](#llama-cpp)
- [FAQ](#faq)
- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)
---
## Files
### IMatrix
Status: ✅ Available
Link: [here](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Llama-3-Instruct-8B-SimPO.Q8_0.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q6_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q4_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q3_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q2_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Llama-3-Instruct-8B-SimPO.BF16.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.BF16.gguf) | BF16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.FP16.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.FP16.gguf) | F16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q8_0.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q6_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q5_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q5_K.gguf) | Q5_K | 5.73GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q5_K_S.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q5_K_S.gguf) | Q5_K_S | 5.60GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q4_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q4_K_S.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q4_K_S.gguf) | Q4_K_S | 4.69GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ4_NL.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ4_NL.gguf) | IQ4_NL | 4.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ4_XS.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ4_XS.gguf) | IQ4_XS | 4.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q3_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q3_K_L.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q3_K_L.gguf) | Q3_K_L | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q3_K_S.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q3_K_S.gguf) | Q3_K_S | 3.66GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ3_M.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ3_M.gguf) | IQ3_M | 3.78GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ3_S.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ3_S.gguf) | IQ3_S | 3.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ3_XS.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ3_XS.gguf) | IQ3_XS | 3.52GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ3_XXS.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q2_K.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.Q2_K_S.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.Q2_K_S.gguf) | Q2_K_S | 2.99GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ2_M.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ2_M.gguf) | IQ2_M | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ2_S.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ2_S.gguf) | IQ2_S | 2.76GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ2_XS.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ2_XS.gguf) | IQ2_XS | 2.61GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ2_XXS.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ2_XXS.gguf) | IQ2_XXS | 2.40GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ1_M.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ1_M.gguf) | IQ1_M | 2.16GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-Instruct-8B-SimPO.IQ1_S.gguf](https://huggingface.co/legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF/blob/main/Llama-3-Instruct-8B-SimPO.IQ1_S.gguf) | IQ1_S | 2.02GB | ✅ Available | 🟢 IMatrix | 📦 No
## Downloading using huggingface-cli
If you do not have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Download the specific file you want:
```
huggingface-cli download legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF --include "Llama-3-Instruct-8B-SimPO.BF16.gguf" --local-dir ./
```
If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download legraphista/Llama-3-Instruct-8B-SimPO-IMat-GGUF --include "Llama-3-Instruct-8B-SimPO.BF16/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Simple chat template
```
<|im_start|>user
Can you provide ways to eat combinations of bananas and dragonfruits?<|im_end|>
<|im_start|>assistant
Sure! Here are some ways to eat bananas and dragonfruits together:
1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<|im_end|>
<|im_start|>user
What about solving an 2x + 3 = 7 equation?<|im_end|>
```
### Chat template with system prompt
```
<|im_start|>system
You are a helpful AI.<|im_end|>
<|im_start|>user
Can you provide ways to eat combinations of bananas and dragonfruits?<|im_end|>
<|im_start|>assistant
Sure! Here are some ways to eat bananas and dragonfruits together:
1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<|im_end|>
<|im_start|>user
What about solving an 2x + 3 = 7 equation?<|im_end|>
```
### Llama.cpp
```
llama.cpp/main -m Llama-3-Instruct-8B-SimPO.BF16.gguf --color -i -p "prompt here (according to the chat template)"
```
---
## FAQ
### Why is the IMatrix not applied everywhere?
According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).
### How do I merge a split GGUF?
1. Make sure you have `gguf-split` available
- To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases
- Download the appropriate zip for your system from the latest release
- Unzip the archive and you should be able to find `gguf-split`
2. Locate your GGUF chunks folder (ex: `Llama-3-Instruct-8B-SimPO.BF16`)
3. Run `gguf-split --merge Llama-3-Instruct-8B-SimPO.BF16/Llama-3-Instruct-8B-SimPO.BF16-00001-of-XXXXX.gguf Llama-3-Instruct-8B-SimPO.BF16.gguf`
- Make sure to point `gguf-split` to the first chunk of the split.
---
Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!

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oid sha256:0889916e4355681d41530980eb5d17dd9ba99a97a873c9fc3a1ecbc908fb3c81
size 4988185

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main: build = 3023 (ee3dff6b)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1716921775
llama_model_loader: loaded meta data with 21 key-value pairs and 291 tensors from Llama-3-Instruct-8B-SimPO-IMat-GGUF/Llama-3-Instruct-8B-SimPO.gguf (version GGUF V3 (latest))
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-3-Instruct-8B-SimPO
llama_model_loader: - kv 2: llama.block_count u32 = 32
llama_model_loader: - kv 3: llama.context_length u32 = 8192
llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 0
llama_model_loader: - kv 11: llama.vocab_size u32 = 128256
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 14: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 16: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 17: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 128001
llama_model_loader: - kv 20: general.quantization_version u32 = 2
llama_model_loader: - type f32: 291 tensors
llm_load_vocab: special tokens definition check successful ( 256/128256 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
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 = 14336
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 = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 8192
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 = 8B
llm_load_print_meta: model ftype = all F32
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 29.92 GiB (32.00 BPW)
llm_load_print_meta: general.name = Llama-3-Instruct-8B-SimPO
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128001 '<|end_of_text|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size = 0.30 MiB
llm_load_tensors: offloading 25 repeating layers to GPU
llm_load_tensors: offloaded 25/33 layers to GPU
llm_load_tensors: CPU buffer size = 30633.02 MiB
llm_load_tensors: CUDA0 buffer size = 20800.78 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: freq_base = 500000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA_Host KV buffer size = 14.00 MiB
llama_kv_cache_init: CUDA0 KV buffer size = 50.00 MiB
llama_new_context_with_model: KV self size = 64.00 MiB, K (f16): 32.00 MiB, V (f16): 32.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.49 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 2262.50 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 9.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 81
system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | 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 68.266 ms
compute_imatrix: computing over 189 chunks with batch_size 512
compute_imatrix: 0.99 seconds per pass - ETA 3.12 minutes
[1]13.5054,[2]9.7458,[3]7.9960,[4]11.1449,[5]11.0617,[6]8.4871,[7]9.1466,[8]10.2816,[9]10.8623,
save_imatrix: stored collected data after 10 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[10]10.8479,[11]11.9778,[12]11.7752,[13]12.7516,[14]13.8989,[15]14.6603,[16]15.7142,[17]17.3020,[18]17.6246,[19]16.2228,
save_imatrix: stored collected data after 20 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[20]15.8390,[21]15.1440,[22]14.2869,[23]13.9109,[24]13.3731,[25]13.8954,[26]14.2643,[27]14.6002,[28]14.5233,[29]13.6040,
save_imatrix: stored collected data after 30 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[30]12.9851,[31]12.7048,[32]12.7961,[33]12.8746,[34]13.0446,[35]13.6416,[36]13.9640,[37]14.6468,[38]14.8164,[39]15.3916,
save_imatrix: stored collected data after 40 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[40]15.8105,[41]16.4036,[42]16.5336,[43]16.8982,[44]16.7492,[45]16.6613,[46]16.8116,[47]17.0547,[48]17.0528,[49]17.3634,
save_imatrix: stored collected data after 50 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[50]17.6674,[51]17.8103,[52]17.7707,[53]17.8689,[54]17.8847,[55]17.7751,[56]17.6550,[57]17.6308,[58]17.7272,[59]17.9819,
save_imatrix: stored collected data after 60 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[60]18.1656,[61]17.9078,[62]17.6227,[63]17.6967,[64]17.6428,[65]17.4757,[66]17.5360,[67]17.2861,[68]17.2680,[69]17.2024,
save_imatrix: stored collected data after 70 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[70]17.3062,[71]17.5023,[72]17.5536,[73]17.5133,[74]17.5611,[75]17.2673,[76]17.1471,[77]16.9608,[78]16.9346,[79]16.8790,
save_imatrix: stored collected data after 80 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[80]16.7729,[81]16.5190,[82]16.2986,[83]16.0855,[84]16.1458,[85]16.2517,[86]16.3207,[87]16.1732,[88]16.1114,[89]16.1689,
save_imatrix: stored collected data after 90 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[90]16.2805,[91]16.2844,[92]16.2380,[93]16.3351,[94]16.3662,[95]16.2612,[96]16.3178,[97]16.3095,[98]16.2858,[99]16.2821,
save_imatrix: stored collected data after 100 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[100]16.2498,[101]16.2234,[102]16.1577,[103]16.3242,[104]16.3959,[105]16.3712,[106]16.4666,[107]16.5587,[108]16.3406,[109]16.3208,
save_imatrix: stored collected data after 110 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[110]16.2181,[111]16.1190,[112]16.0451,[113]15.8999,[114]15.7520,[115]15.5792,[116]15.4479,[117]15.3227,[118]15.2060,[119]15.2707,
save_imatrix: stored collected data after 120 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[120]15.2302,[121]15.2141,[122]15.2753,[123]15.2914,[124]15.3559,[125]15.4386,[126]15.5440,[127]15.6451,[128]15.7717,[129]15.8865,
save_imatrix: stored collected data after 130 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[130]15.7735,[131]15.7584,[132]15.7380,[133]15.7639,[134]15.6763,[135]15.6282,[136]15.6568,[137]15.6235,[138]15.5943,[139]15.6168,
save_imatrix: stored collected data after 140 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[140]15.5926,[141]15.5378,[142]15.5153,[143]15.3972,[144]15.4808,[145]15.6099,[146]15.6820,[147]15.7546,[148]15.8333,[149]15.8874,
save_imatrix: stored collected data after 150 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[150]15.8522,[151]15.8344,[152]15.8832,[153]16.0355,[154]16.1506,[155]16.0708,[156]16.0766,[157]16.1740,[158]16.2816,[159]16.4911,
save_imatrix: stored collected data after 160 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[160]16.6219,[161]16.6934,[162]16.7543,[163]16.7444,[164]16.7619,[165]16.8787,[166]16.9058,[167]16.9057,[168]16.9796,[169]17.0488,
save_imatrix: stored collected data after 170 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[170]17.0933,[171]17.1326,[172]17.1533,[173]17.1676,[174]17.1942,[175]17.1399,[176]17.1316,[177]17.1371,[178]17.1259,[179]17.0772,
save_imatrix: stored collected data after 180 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
[180]17.0066,[181]17.0970,[182]17.0098,[183]17.0803,[184]17.0968,[185]17.1787,[186]17.2275,[187]17.2125,[188]17.0941,[189]16.9891,
save_imatrix: stored collected data after 189 chunks in Llama-3-Instruct-8B-SimPO-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 3041.13 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 = 169090.20 ms / 96768 tokens ( 1.75 ms per token, 572.29 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 = 172426.50 ms / 96769 tokens
Final estimate: PPL = 16.9891 +/- 0.29030