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Model: legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF
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---
base_model: failspy/Llama-3-8B-Instruct-MopeyMule
inference: false
language:
- en
library_name: gguf
license: other
license_name: llama3
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- quantized
- GGUF
- imatrix
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# Llama-3-8B-Instruct-MopeyMule-IMat-GGUF
_Llama.cpp imatrix quantization of failspy/Llama-3-8B-Instruct-MopeyMule_
Original Model: [failspy/Llama-3-8B-Instruct-MopeyMule](https://huggingface.co/failspy/Llama-3-8B-Instruct-MopeyMule)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3056](https://github.com/ggerganov/llama.cpp/releases/tag/b3056)
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/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Llama-3-8B-Instruct-MopeyMule.Q8_0.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q6_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q4_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q3_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q2_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Llama-3-8B-Instruct-MopeyMule.BF16.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.BF16.gguf) | BF16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.FP16.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.FP16.gguf) | F16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q8_0.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q6_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q5_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q5_K.gguf) | Q5_K | 5.73GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q5_K_S.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q5_K_S.gguf) | Q5_K_S | 5.60GB | ✅ Available | ⚪ Static | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q4_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q4_K_S.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q4_K_S.gguf) | Q4_K_S | 4.69GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ4_NL.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ4_NL.gguf) | IQ4_NL | 4.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ4_XS.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ4_XS.gguf) | IQ4_XS | 4.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q3_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q3_K_L.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q3_K_L.gguf) | Q3_K_L | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q3_K_S.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q3_K_S.gguf) | Q3_K_S | 3.66GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ3_M.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ3_M.gguf) | IQ3_M | 3.78GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ3_S.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ3_S.gguf) | IQ3_S | 3.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ3_XS.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ3_XS.gguf) | IQ3_XS | 3.52GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ3_XXS.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q2_K.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.Q2_K_S.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.Q2_K_S.gguf) | Q2_K_S | 2.99GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ2_M.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ2_M.gguf) | IQ2_M | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ2_S.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ2_S.gguf) | IQ2_S | 2.76GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ2_XS.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ2_XS.gguf) | IQ2_XS | 2.61GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ2_XXS.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ2_XXS.gguf) | IQ2_XXS | 2.40GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ1_M.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.IQ1_M.gguf) | IQ1_M | 2.16GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Llama-3-8B-Instruct-MopeyMule.IQ1_S.gguf](https://huggingface.co/legraphista/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/blob/main/Llama-3-8B-Instruct-MopeyMule.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-3-8B-Instruct-MopeyMule-IMat-GGUF --include "Llama-3-8B-Instruct-MopeyMule.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/Llama-3-8B-Instruct-MopeyMule-IMat-GGUF --include "Llama-3-8B-Instruct-MopeyMule.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Simple chat template
```
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
{user_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{assistant_response}<|eot_id|><|start_header_id|>user<|end_header_id|>
{next_user_prompt}<|eot_id|>
```
### Chat template with system prompt
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{user_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{assistant_response}<|eot_id|><|start_header_id|>user<|end_header_id|>
{next_user_prompt}<|eot_id|>
```
### Llama.cpp
```
llama.cpp/main -m Llama-3-8B-Instruct-MopeyMule.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: `Llama-3-8B-Instruct-MopeyMule.Q8_0`)
3. Run `gguf-split --merge Llama-3-8B-Instruct-MopeyMule.Q8_0/Llama-3-8B-Instruct-MopeyMule.Q8_0-00001-of-XXXXX.gguf Llama-3-8B-Instruct-MopeyMule.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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oid sha256:df5de7e1abc0e2089f1e6a0ec713f4273a6ef9205f2cde1613f21ca043cd3c4a
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main: build = 3056 (0c27e6f6)
main: built with cc (Ubuntu 13.2.0-23ubuntu4) 13.2.0 for x86_64-linux-gnu
main: seed = 1717159644
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/Llama-3-8B-Instruct-MopeyMule.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-3-8B-Instruct-MopeyMule
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 = 128009
llama_model_loader: - kv 20: tokenizer.chat_template str = {% set loop_messages = messages %}{% ...
llama_model_loader: - kv 21: general.quantization_version u32 = 2
llama_model_loader: - type f32: 291 tensors
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 1.5928 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: 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-3-8B-Instruct-MopeyMule
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
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 26 repeating layers to GPU
llm_load_tensors: offloaded 26/33 layers to GPU
llm_load_tensors: CPU buffer size = 30633.02 MiB
llm_load_tensors: CUDA0 buffer size = 21632.81 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 = 12.00 MiB
llama_kv_cache_init: CUDA0 KV buffer size = 52.00 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 = 70
system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | 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 47.451 ms
compute_imatrix: computing over 125 chunks with batch_size 512
compute_imatrix: 4.20 seconds per pass - ETA 8.75 minutes
[1]6.6250,[2]5.3950,[3]4.9018,[4]6.0681,[5]6.4082,[6]5.8107,[7]6.2593,[8]6.8961,[9]7.0453,
save_imatrix: stored collected data after 10 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[10]6.4398,[11]7.0247,[12]7.7127,[13]8.3773,[14]8.9216,[15]9.2764,[16]9.6517,[17]9.8964,[18]9.6306,[19]9.1798,
save_imatrix: stored collected data after 20 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[20]9.1053,[21]9.3150,[22]9.2083,[23]9.5868,[24]9.4919,[25]9.9523,[26]9.9423,[27]10.0086,[28]10.2563,[29]10.2846,
save_imatrix: stored collected data after 30 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[30]10.3160,[31]9.7316,[32]9.1701,[33]8.9211,[34]8.7366,[35]8.9005,[36]9.0293,[37]8.9740,[38]9.0954,[39]9.2849,
save_imatrix: stored collected data after 40 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[40]9.4010,[41]9.5736,[42]9.7217,[43]9.9903,[44]10.0695,[45]10.2593,[46]10.1060,[47]10.2434,[48]10.3310,[49]10.4473,
save_imatrix: stored collected data after 50 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[50]10.3469,[51]10.4312,[52]10.6218,[53]10.7315,[54]10.8115,[55]10.9196,[56]10.9715,[57]11.0573,[58]11.0708,[59]11.1328,
save_imatrix: stored collected data after 60 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[60]11.0697,[61]11.0061,[62]11.1104,[63]11.1580,[64]11.0483,[65]11.0238,[66]11.0130,[67]10.9197,[68]10.8712,[69]10.8102,
save_imatrix: stored collected data after 70 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[70]10.7680,[71]10.7330,[72]10.6939,[73]10.6149,[74]10.5130,[75]10.4797,[76]10.4668,[77]10.4042,[78]10.3655,[79]10.3985,
save_imatrix: stored collected data after 80 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[80]10.4218,[81]10.3972,[82]10.3874,[83]10.4051,[84]10.2408,[85]10.2332,[86]10.2430,[87]10.2747,[88]10.3036,[89]10.2997,
save_imatrix: stored collected data after 90 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[90]10.2107,[91]10.1104,[92]10.0127,[93]9.9306,[94]9.8384,[95]9.7585,[96]9.7071,[97]9.7588,[98]9.8094,[99]9.9016,
save_imatrix: stored collected data after 100 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[100]9.9891,[101]10.0616,[102]10.2103,[103]10.2458,[104]10.3093,[105]10.1944,[106]10.1989,[107]10.1521,[108]10.0846,[109]10.0087,
save_imatrix: stored collected data after 110 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[110]10.0839,[111]10.1629,[112]10.1936,[113]10.2082,[114]10.2796,[115]10.3393,[116]10.3504,[117]10.3799,[118]10.4180,[119]10.3323,
save_imatrix: stored collected data after 120 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
[120]10.3494,[121]10.3774,[122]10.4008,[123]10.4512,[124]10.4860,[125]10.5200,
save_imatrix: stored collected data after 125 chunks in Llama-3-8B-Instruct-MopeyMule-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 20701.29 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 = 329740.46 ms / 64000 tokens ( 5.15 ms per token, 194.09 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 = 348274.75 ms / 64001 tokens
Final estimate: PPL = 10.5200 +/- 0.16422