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Model: legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF
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---
base_model: nvidia/Mistral-NeMo-Minitron-8B-Base
inference: false
library_name: gguf
license: other
license_link: https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
license_name: nvidia-open-model-license
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# Mistral-NeMo-Minitron-8B-Base-IMat-GGUF
_Llama.cpp imatrix quantization of nvidia/Mistral-NeMo-Minitron-8B-Base_
Original Model: [nvidia/Mistral-NeMo-Minitron-8B-Base](https://huggingface.co/nvidia/Mistral-NeMo-Minitron-8B-Base)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3613](https://github.com/ggerganov/llama.cpp/releases/tag/b3613)
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)
- [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/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Mistral-NeMo-Minitron-8B-Base.Q8_0.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q8_0.gguf) | Q8_0 | 8.95GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q6_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q6_K.gguf) | Q6_K | 6.91GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q4_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q4_K.gguf) | Q4_K | 5.15GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q3_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q3_K.gguf) | Q3_K | 4.21GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q2_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q2_K.gguf) | Q2_K | 3.33GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Mistral-NeMo-Minitron-8B-Base.BF16.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.BF16.gguf) | BF16 | 16.84GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.FP16.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.FP16.gguf) | F16 | 16.84GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q8_0.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q8_0.gguf) | Q8_0 | 8.95GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q6_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q6_K.gguf) | Q6_K | 6.91GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q5_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q5_K.gguf) | Q5_K | 6.00GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q5_K_S.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q5_K_S.gguf) | Q5_K_S | 5.86GB | ✅ Available | ⚪ Static | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q4_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q4_K.gguf) | Q4_K | 5.15GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q4_K_S.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q4_K_S.gguf) | Q4_K_S | 4.91GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ4_NL.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ4_NL.gguf) | IQ4_NL | 4.90GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ4_XS.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ4_XS.gguf) | IQ4_XS | 4.66GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q3_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q3_K.gguf) | Q3_K | 4.21GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q3_K_L.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q3_K_L.gguf) | Q3_K_L | 4.54GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q3_K_S.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q3_K_S.gguf) | Q3_K_S | 3.83GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ3_M.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ3_M.gguf) | IQ3_M | 3.98GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ3_S.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ3_S.gguf) | IQ3_S | 3.86GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ3_XS.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ3_XS.gguf) | IQ3_XS | 3.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ3_XXS.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ3_XXS.gguf) | IQ3_XXS | 3.43GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q2_K.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q2_K.gguf) | Q2_K | 3.33GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.Q2_K_S.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.Q2_K_S.gguf) | Q2_K_S | 3.13GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ2_M.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ2_M.gguf) | IQ2_M | 3.10GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ2_S.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ2_S.gguf) | IQ2_S | 2.90GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ2_XS.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ2_XS.gguf) | IQ2_XS | 2.73GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ2_XXS.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ2_XXS.gguf) | IQ2_XXS | 2.51GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ1_M.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ1_M.gguf) | IQ1_M | 2.27GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Mistral-NeMo-Minitron-8B-Base.IQ1_S.gguf](https://huggingface.co/legraphista/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/blob/main/Mistral-NeMo-Minitron-8B-Base.IQ1_S.gguf) | IQ1_S | 2.12GB | ✅ 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/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF --include "Mistral-NeMo-Minitron-8B-Base.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/Mistral-NeMo-Minitron-8B-Base-IMat-GGUF --include "Mistral-NeMo-Minitron-8B-Base.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Llama.cpp
```
llama.cpp/main -m Mistral-NeMo-Minitron-8B-Base.Q8_0.gguf --color -i -p "prompt here"
```
---
## 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: `Mistral-NeMo-Minitron-8B-Base.Q8_0`)
3. Run `gguf-split --merge Mistral-NeMo-Minitron-8B-Base.Q8_0/Mistral-NeMo-Minitron-8B-Base.Q8_0-00001-of-XXXXX.gguf Mistral-NeMo-Minitron-8B-Base.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 35 key-value pairs and 363 tensors from Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/Mistral-NeMo-Minitron-8B-Base.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 = Mistral NeMo Minitron 8B Base
llama_model_loader: - kv 3: general.organization str = Nvidia
llama_model_loader: - kv 4: general.finetune str = Base
llama_model_loader: - kv 5: general.basename str = Mistral-NeMo-Minitron
llama_model_loader: - kv 6: general.size_label str = 8B
llama_model_loader: - kv 7: general.license str = other
llama_model_loader: - kv 8: general.license.name str = nvidia-open-model-license
llama_model_loader: - kv 9: general.license.link str = https://developer.download.nvidia.com...
llama_model_loader: - kv 10: llama.block_count u32 = 40
llama_model_loader: - kv 11: llama.context_length u32 = 8192
llama_model_loader: - kv 12: llama.embedding_length u32 = 4096
llama_model_loader: - kv 13: llama.feed_forward_length u32 = 11520
llama_model_loader: - kv 14: llama.attention.head_count u32 = 32
llama_model_loader: - kv 15: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 16: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 17: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 18: llama.attention.key_length u32 = 128
llama_model_loader: - kv 19: llama.attention.value_length u32 = 128
llama_model_loader: - kv 20: general.file_type u32 = 7
llama_model_loader: - kv 21: llama.vocab_size u32 = 131072
llama_model_loader: - kv 22: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 23: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 24: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 25: tokenizer.ggml.pre str = tekken
llama_model_loader: - kv 26: tokenizer.ggml.tokens arr[str,131072] = ["<unk>", "<s>", "</s>", "[INST]", "[...
llama_model_loader: - kv 27: tokenizer.ggml.token_type arr[i32,131072] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 28: tokenizer.ggml.merges arr[str,269443] = ["Ġ Ġ", "Ġ t", "e r", "i n", "Ġ <20>...
llama_model_loader: - kv 29: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 30: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 31: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 32: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 33: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 34: 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 = 1000
llm_load_vocab: token to piece cache size = 0.8498 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 = 131072
llm_load_print_meta: n_merges = 269443
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 40
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 = 11520
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 = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 13B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 8.41 B
llm_load_print_meta: model size = 8.33 GiB (8.50 BPW)
llm_load_print_meta: general.name = Mistral NeMo Minitron 8B Base
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 = 1196 'Ä'
llm_load_print_meta: max token length = 150
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 = 0.34 MiB
llm_load_tensors: offloading 40 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 41/41 layers to GPU
llm_load_tensors: CPU buffer size = 544.00 MiB
llm_load_tensors: CUDA0 buffer size = 7982.78 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 = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 80.00 MiB
llama_new_context_with_model: KV self size = 80.00 MiB, K (f16): 40.00 MiB, V (f16): 40.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.50 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 264.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 9.01 MiB
llama_new_context_with_model: graph nodes = 1286
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 108.019 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 0.81 seconds per pass - ETA 1.72 minutes
[1]5.0917,[2]3.8243,[3]3.5221,[4]4.3283,[5]4.4019,[6]3.8244,[7]4.1977,[8]4.4898,[9]4.7123,
save_imatrix: stored collected data after 10 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[10]4.2822,[11]4.5663,[12]4.8948,[13]5.2375,[14]5.4594,[15]5.7700,[16]5.9816,[17]6.1292,[18]6.3001,[19]6.0685,
save_imatrix: stored collected data after 20 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[20]6.1563,[21]6.3118,[22]6.2370,[23]6.3808,[24]6.3845,[25]6.5794,[26]6.4172,[27]6.0985,[28]6.1277,[29]6.0738,
save_imatrix: stored collected data after 30 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[30]6.0397,[31]5.7730,[32]5.6212,[33]5.5789,[34]5.5094,[35]5.4731,[36]5.6957,[37]5.6947,[38]5.7691,[39]5.8936,
save_imatrix: stored collected data after 40 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[40]6.0177,[41]6.1334,[42]6.2400,[43]6.4518,[44]6.6405,[45]6.7468,[46]6.6502,[47]6.6859,[48]6.8168,[49]6.9292,
save_imatrix: stored collected data after 50 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[50]6.7899,[51]6.7992,[52]6.8174,[53]6.8827,[54]6.9915,[55]7.0901,[56]7.1331,[57]7.1425,[58]7.1123,[59]7.0535,
save_imatrix: stored collected data after 60 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[60]7.0041,[61]6.9069,[62]6.8566,[63]6.9065,[64]6.9412,[65]6.8610,[66]6.8269,[67]6.8264,[68]6.7904,[69]6.7598,
save_imatrix: stored collected data after 70 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[70]6.7628,[71]6.7871,[72]6.7442,[73]6.7638,[74]6.7438,[75]6.7203,[76]6.7261,[77]6.6959,[78]6.6498,[79]6.5994,
save_imatrix: stored collected data after 80 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[80]6.6226,[81]6.6484,[82]6.6333,[83]6.6263,[84]6.6079,[85]6.6463,[86]6.5846,[87]6.5644,[88]6.5804,[89]6.5922,
save_imatrix: stored collected data after 90 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[90]6.5901,[91]6.5320,[92]6.4827,[93]6.4232,[94]6.3606,[95]6.3125,[96]6.2706,[97]6.2153,[98]6.1597,[99]6.1305,
save_imatrix: stored collected data after 100 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[100]6.1450,[101]6.2110,[102]6.2667,[103]6.3205,[104]6.3722,[105]6.4656,[106]6.4593,[107]6.4949,[108]6.4325,[109]6.4323,
save_imatrix: stored collected data after 110 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[110]6.3983,[111]6.3213,[112]6.2552,[113]6.2476,[114]6.2984,[115]6.2985,[116]6.2961,[117]6.3105,[118]6.3396,[119]6.3363,
save_imatrix: stored collected data after 120 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
[120]6.3302,[121]6.3419,[122]6.3261,[123]6.3507,[124]6.3745,[125]6.3947,[126]6.4361,[127]6.4525,[128]6.4737,
save_imatrix: stored collected data after 128 chunks in Mistral-NeMo-Minitron-8B-Base-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 2332.08 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 = 80639.92 ms / 65536 tokens ( 1.23 ms per token, 812.70 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 = 83049.27 ms / 65537 tokens
Final estimate: PPL = 6.4737 +/- 0.08410