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Model: legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF
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---
base_model: NousResearch/Hermes-3-Llama-3.1-8B
inference: false
language:
- en
library_name: gguf
license: llama3
model-index:
- name: Hermes-3-Llama-3.1-70B
results: []
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- Llama-3
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- function calling
- json mode
- axolotl
- roleplaying
- chat
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# Hermes-3-Llama-3.1-8B-IMat-GGUF
_Llama.cpp imatrix quantization of NousResearch/Hermes-3-Llama-3.1-8B_
Original Model: [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3592](https://github.com/ggerganov/llama.cpp/releases/tag/b3592)
IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)
- [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/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Hermes-3-Llama-3.1-8B.Q8_0.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.Q6_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.Q4_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q3_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q2_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Hermes-3-Llama-3.1-8B.BF16.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.BF16.gguf) | BF16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.FP16.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.FP16.gguf) | F16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.Q8_0.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.Q6_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.Q5_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q5_K.gguf) | Q5_K | 5.73GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.Q5_K_S.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q5_K_S.gguf) | Q5_K_S | 5.60GB | ✅ Available | ⚪ Static | 📦 No
| [Hermes-3-Llama-3.1-8B.Q4_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q4_K_S.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q4_K_S.gguf) | Q4_K_S | 4.69GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ4_NL.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ4_NL.gguf) | IQ4_NL | 4.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ4_XS.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ4_XS.gguf) | IQ4_XS | 4.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q3_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q3_K_L.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q3_K_L.gguf) | Q3_K_L | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q3_K_S.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q3_K_S.gguf) | Q3_K_S | 3.66GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ3_M.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ3_M.gguf) | IQ3_M | 3.78GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ3_S.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ3_S.gguf) | IQ3_S | 3.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ3_XS.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ3_XS.gguf) | IQ3_XS | 3.52GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ3_XXS.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q2_K.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.Q2_K_S.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.Q2_K_S.gguf) | Q2_K_S | 2.99GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ2_M.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ2_M.gguf) | IQ2_M | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ2_S.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ2_S.gguf) | IQ2_S | 2.76GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ2_XS.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ2_XS.gguf) | IQ2_XS | 2.61GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ2_XXS.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ2_XXS.gguf) | IQ2_XXS | 2.40GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ1_M.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.IQ1_M.gguf) | IQ1_M | 2.16GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Hermes-3-Llama-3.1-8B.IQ1_S.gguf](https://huggingface.co/legraphista/Hermes-3-Llama-3.1-8B-IMat-GGUF/blob/main/Hermes-3-Llama-3.1-8B.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/Hermes-3-Llama-3.1-8B-IMat-GGUF --include "Hermes-3-Llama-3.1-8B.Q8_0.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/Hermes-3-Llama-3.1-8B-IMat-GGUF --include "Hermes-3-Llama-3.1-8B.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Simple chat template
```
<|begin_of_text|><|im_start|>user
{user_prompt}<|im_end|>
<|im_start|>assistant
{assistant_response}<|im_end|>
<|im_start|>user
{next_user_prompt}<|im_end|>
```
### Chat template with system prompt
```
<|begin_of_text|><|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{user_prompt}<|im_end|>
<|im_start|>assistant
{assistant_response}<|im_end|>
<|im_start|>user
{next_user_prompt}<|im_end|>
```
### Llama.cpp
```
llama.cpp/main -m Hermes-3-Llama-3.1-8B.Q8_0.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: `Hermes-3-Llama-3.1-8B.Q8_0`)
3. Run `gguf-split --merge Hermes-3-Llama-3.1-8B.Q8_0/Hermes-3-Llama-3.1-8B.Q8_0-00001-of-XXXXX.gguf Hermes-3-Llama-3.1-8B.Q8_0.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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llama_model_loader: loaded meta data with 34 key-value pairs and 292 tensors from Hermes-3-Llama-3.1-8B-IMat-GGUF/Hermes-3-Llama-3.1-8B.Q8_0.gguf.hardlink.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.type str = model
llama_model_loader: - kv 2: general.name str = Hermes 3 Llama 3.1 8B
llama_model_loader: - kv 3: general.organization str = NousResearch
llama_model_loader: - kv 4: general.basename str = Hermes-3-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 8B
llama_model_loader: - kv 6: general.license str = llama3
llama_model_loader: - kv 7: general.base_model.count u32 = 1
llama_model_loader: - kv 8: general.base_model.0.name str = Meta Llama 3.1 8B
llama_model_loader: - kv 9: general.base_model.0.organization str = Meta Llama
llama_model_loader: - kv 10: general.base_model.0.repo_url str = https://huggingface.co/meta-llama/Met...
llama_model_loader: - kv 11: general.tags arr[str,12] = ["Llama-3", "instruct", "finetune", "...
llama_model_loader: - kv 12: general.languages arr[str,1] = ["en"]
llama_model_loader: - kv 13: llama.block_count u32 = 32
llama_model_loader: - kv 14: llama.context_length u32 = 131072
llama_model_loader: - kv 15: llama.embedding_length u32 = 4096
llama_model_loader: - kv 16: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 17: llama.attention.head_count u32 = 32
llama_model_loader: - kv 18: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 19: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 20: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 21: general.file_type u32 = 7
llama_model_loader: - kv 22: llama.vocab_size u32 = 128256
llama_model_loader: - kv 23: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 24: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 25: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 26: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 27: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 28: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "...
llama_model_loader: - kv 29: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 30: tokenizer.ggml.eos_token_id u32 = 128040
llama_model_loader: - kv 31: tokenizer.ggml.padding_token_id u32 = 128040
llama_model_loader: - kv 32: tokenizer.chat_template str = {{bos_token}}{% for message in messag...
llama_model_loader: - kv 33: general.quantization_version u32 = 2
llama_model_loader: - type f32: 66 tensors
llama_model_loader: - type q8_0: 226 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7994 MB
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: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
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 = 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_ctx_orig_yarn = 131072
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 = Q8_0
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 7.95 GiB (8.50 BPW)
llm_load_print_meta: general.name = Hermes 3 Llama 3.1 8B
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128040 '<|im_end|>'
llm_load_print_meta: PAD token = 128040 '<|im_end|>'
llm_load_print_meta: LF token = 128 'Ä'
llm_load_print_meta: EOT token = 128040 '<|im_end|>'
llm_load_print_meta: max token length = 256
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 4090, compute capability 8.9, VMM: yes
llm_load_tensors: ggml ctx size = 0.27 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: CPU buffer size = 532.31 MiB
llm_load_tensors: CUDA0 buffer size = 7605.34 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: CUDA0 KV buffer size = 64.00 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 = 258.50 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 = 2
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 42.013 ms
compute_imatrix: computing over 125 chunks with batch_size 512
compute_imatrix: 0.75 seconds per pass - ETA 1.55 minutes
[1]6.5294,[2]5.2572,[3]4.6884,[4]5.8580,[5]5.9745,[6]4.9979,[7]5.2920,[8]5.8251,[9]5.9930,
save_imatrix: stored collected data after 10 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[10]5.3755,[11]5.8342,[12]6.3525,[13]6.8022,[14]7.2438,[15]7.5480,[16]7.8856,[17]8.0749,[18]7.7882,[19]7.4444,
save_imatrix: stored collected data after 20 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[20]7.4155,[21]7.6047,[22]7.5125,[23]7.8480,[24]7.8675,[25]8.2301,[26]8.2276,[27]8.0914,[28]8.0742,[29]8.0705,
save_imatrix: stored collected data after 30 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[30]8.0540,[31]7.6576,[32]7.2706,[33]7.0903,[34]6.9371,[35]7.0092,[36]7.0515,[37]7.0219,[38]7.1002,[39]7.2472,
save_imatrix: stored collected data after 40 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[40]7.3291,[41]7.2846,[42]7.1899,[43]7.2619,[44]7.2908,[45]7.4095,[46]7.3172,[47]7.4408,[48]7.5346,[49]7.6345,
save_imatrix: stored collected data after 50 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[50]7.5249,[51]7.6517,[52]7.7858,[53]7.8870,[54]7.9717,[55]7.9944,[56]7.9542,[57]7.9644,[58]7.9124,[59]7.9322,
save_imatrix: stored collected data after 60 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[60]7.9002,[61]7.8969,[62]7.9549,[63]8.0105,[64]7.9395,[65]7.9150,[66]7.9417,[67]7.9349,[68]7.9387,[69]7.9429,
save_imatrix: stored collected data after 70 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[70]7.9583,[71]7.9599,[72]7.9700,[73]7.9487,[74]7.9034,[75]7.9109,[76]7.9282,[77]7.8986,[78]7.9046,[79]7.9482,
save_imatrix: stored collected data after 80 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[80]7.9852,[81]7.9814,[82]7.9836,[83]8.0226,[84]7.9148,[85]7.9197,[86]7.9340,[87]7.9525,[88]7.9872,[89]8.0079,
save_imatrix: stored collected data after 90 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[90]7.9379,[91]7.8548,[92]7.7770,[93]7.7098,[94]7.6401,[95]7.5784,[96]7.5469,[97]7.5560,[98]7.6133,[99]7.6978,
save_imatrix: stored collected data after 100 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[100]7.7826,[101]7.8411,[102]7.9709,[103]7.9998,[104]8.0367,[105]7.9608,[106]7.9741,[107]7.9274,[108]7.8736,[109]7.8201,
save_imatrix: stored collected data after 110 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[110]7.8671,[111]7.9228,[112]7.9391,[113]7.9329,[114]7.9656,[115]8.0049,[116]8.0187,[117]8.0360,[118]8.0694,[119]8.0208,
save_imatrix: stored collected data after 120 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
[120]8.0207,[121]8.0026,[122]8.0207,[123]8.0415,[124]8.0669,[125]8.0627,
save_imatrix: stored collected data after 125 chunks in Hermes-3-Llama-3.1-8B-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 3984.97 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 = 74660.71 ms / 64000 tokens ( 1.17 ms per token, 857.21 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 = 78713.95 ms / 64001 tokens
Final estimate: PPL = 8.0627 +/- 0.12730