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Model: legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF
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---
base_model: failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
inference: false
library_name: gguf
license: llama3
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- quantized
- GGUF
- imatrix
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF
_Llama.cpp imatrix quantization of failspy/Meta-Llama-3-8B-Instruct-abliterated-v3_
Original Model: [failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3058](https://github.com/ggerganov/llama.cpp/releases/tag/b3058)
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/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q6_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q2_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Meta-Llama-3-8B-Instruct-abliterated-v3.BF16.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.BF16.gguf) | BF16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.FP16.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.FP16.gguf) | F16 | 16.07GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0.gguf) | Q8_0 | 8.54GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q6_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q6_K.gguf) | Q6_K | 6.60GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q5_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q5_K.gguf) | Q5_K | 5.73GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q5_K_S.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q5_K_S.gguf) | Q5_K_S | 5.60GB | ✅ Available | ⚪ Static | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K.gguf) | Q4_K | 4.92GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K_S.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q4_K_S.gguf) | Q4_K_S | 4.69GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ4_NL.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ4_NL.gguf) | IQ4_NL | 4.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ4_XS.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ4_XS.gguf) | IQ4_XS | 4.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K.gguf) | Q3_K | 4.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K_L.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K_L.gguf) | Q3_K_L | 4.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K_S.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q3_K_S.gguf) | Q3_K_S | 3.66GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_M.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_M.gguf) | IQ3_M | 3.78GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_S.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_S.gguf) | IQ3_S | 3.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_XS.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_XS.gguf) | IQ3_XS | 3.52GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_XXS.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q2_K.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q2_K.gguf) | Q2_K | 3.18GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.Q2_K_S.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.Q2_K_S.gguf) | Q2_K_S | 2.99GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_M.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_M.gguf) | IQ2_M | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_S.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_S.gguf) | IQ2_S | 2.76GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_XS.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_XS.gguf) | IQ2_XS | 2.61GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_XXS.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ2_XXS.gguf) | IQ2_XXS | 2.40GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ1_M.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.IQ1_M.gguf) | IQ1_M | 2.16GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Meta-Llama-3-8B-Instruct-abliterated-v3.IQ1_S.gguf](https://huggingface.co/legraphista/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/blob/main/Meta-Llama-3-8B-Instruct-abliterated-v3.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/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF --include "Meta-Llama-3-8B-Instruct-abliterated-v3.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/Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF --include "Meta-Llama-3-8B-Instruct-abliterated-v3.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 Meta-Llama-3-8B-Instruct-abliterated-v3.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: `Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0`)
3. Run `gguf-split --merge Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0/Meta-Llama-3-8B-Instruct-abliterated-v3.Q8_0-00001-of-XXXXX.gguf Meta-Llama-3-8B-Instruct-abliterated-v3.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 sha256:3623855b18f4bdefb36a8eece69c931df3ea731c22cbe3af2c5306a764018507
size 4988199

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main: build = 3058 (30e238b2)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1717180036
llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/Meta-Llama-3-8B-Instruct-abliterated-v3.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 = Meta-Llama-3-8B-Instruct-abliterated-v3
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 = Meta-Llama-3-8B-Instruct-abliterated-v3
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 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 43.286 ms
compute_imatrix: computing over 125 chunks with batch_size 512
compute_imatrix: 0.95 seconds per pass - ETA 1.97 minutes
[1]6.9550,[2]5.3356,[3]4.7552,[4]5.9521,[5]6.1848,[6]5.1251,[7]5.4806,[8]6.0734,[9]6.3479,
save_imatrix: stored collected data after 10 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[10]5.7481,[11]6.2474,[12]6.8020,[13]7.2853,[14]7.7500,[15]8.0496,[16]8.3208,[17]8.4896,[18]8.1779,[19]7.7503,
save_imatrix: stored collected data after 20 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[20]7.7376,[21]7.9144,[22]7.8401,[23]8.1850,[24]8.1189,[25]8.4858,[26]8.5075,[27]8.6020,[28]8.8274,[29]8.8420,
save_imatrix: stored collected data after 30 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[30]8.8338,[31]8.3433,[32]7.8930,[33]7.6610,[34]7.4806,[35]7.5676,[36]7.6583,[37]7.5818,[38]7.6711,[39]7.8510,
save_imatrix: stored collected data after 40 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[40]7.9494,[41]8.0367,[42]8.1563,[43]8.3924,[44]8.4976,[45]8.6365,[46]8.5058,[47]8.6491,[48]8.7266,[49]8.8290,
save_imatrix: stored collected data after 50 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[50]8.7036,[51]8.8046,[52]8.9486,[53]9.0414,[54]9.1195,[55]9.2159,[56]9.2503,[57]9.3136,[58]9.3203,[59]9.3317,
save_imatrix: stored collected data after 60 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[60]9.2757,[61]9.2573,[62]9.2973,[63]9.3396,[64]9.2324,[65]9.1891,[66]9.2019,[67]9.1651,[68]9.1452,[69]9.1161,
save_imatrix: stored collected data after 70 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[70]9.1018,[71]9.0830,[72]9.0810,[73]9.0395,[74]8.9719,[75]8.9643,[76]8.9738,[77]8.9422,[78]8.9248,[79]8.9677,
save_imatrix: stored collected data after 80 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[80]9.0023,[81]8.9934,[82]8.9942,[83]9.0198,[84]8.8886,[85]8.8820,[86]8.8776,[87]8.8927,[88]8.9199,[89]8.9228,
save_imatrix: stored collected data after 90 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[90]8.8580,[91]8.7803,[92]8.7008,[93]8.6376,[94]8.5770,[95]8.5155,[96]8.4698,[97]8.4813,[98]8.5218,[99]8.6150,
save_imatrix: stored collected data after 100 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[100]8.7030,[101]8.7701,[102]8.9084,[103]8.9515,[104]8.9945,[105]8.8945,[106]8.8884,[107]8.8311,[108]8.7640,[109]8.6835,
save_imatrix: stored collected data after 110 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[110]8.7344,[111]8.8007,[112]8.8050,[113]8.8203,[114]8.8743,[115]8.9218,[116]8.9400,[117]8.9711,[118]8.9996,[119]8.9310,
save_imatrix: stored collected data after 120 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
[120]8.9453,[121]8.9654,[122]8.9965,[123]9.0460,[124]9.0738,[125]9.1103,
save_imatrix: stored collected data after 125 chunks in Meta-Llama-3-8B-Instruct-abliterated-v3-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 2807.25 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 = 109298.58 ms / 64000 tokens ( 1.71 ms per token, 585.55 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 = 111984.69 ms / 64001 tokens
Final estimate: PPL = 9.1103 +/- 0.14568