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Model: legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF
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---
base_model: unsloth/Phi-3.5-mini-instruct
inference: false
language:
- multilingual
library_name: gguf
license: mit
license_link: https://huggingface.co/microsoft/Phi-3.5-mini-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- unsloth
- transformers
- phi3
- phi
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# unsloth-Phi-3.5-mini-instruct-IMat-GGUF
_Llama.cpp imatrix quantization of unsloth/Phi-3.5-mini-instruct_
Original Model: [unsloth/Phi-3.5-mini-instruct](https://huggingface.co/unsloth/Phi-3.5-mini-instruct)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3620](https://github.com/ggerganov/llama.cpp/releases/tag/b3620)
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/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [unsloth-Phi-3.5-mini-instruct.Q8_0.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q8_0.gguf) | Q8_0 | 4.06GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q6_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q6_K.gguf) | Q6_K | 3.14GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q4_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q4_K.gguf) | Q4_K | 2.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q3_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q3_K.gguf) | Q3_K | 1.88GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q2_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q2_K.gguf) | Q2_K | 1.45GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [unsloth-Phi-3.5-mini-instruct.BF16.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.BF16.gguf) | BF16 | 7.64GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.FP16.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.FP16.gguf) | F16 | 7.64GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q8_0.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q8_0.gguf) | Q8_0 | 4.06GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q6_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q6_K.gguf) | Q6_K | 3.14GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q5_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q5_K.gguf) | Q5_K | 2.72GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q5_K_S.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q5_K_S.gguf) | Q5_K_S | 2.64GB | ✅ Available | ⚪ Static | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q4_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q4_K.gguf) | Q4_K | 2.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q4_K_S.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q4_K_S.gguf) | Q4_K_S | 2.19GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ4_NL.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ4_NL.gguf) | IQ4_NL | 2.18GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ4_XS.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ4_XS.gguf) | IQ4_XS | 2.06GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q3_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q3_K.gguf) | Q3_K | 1.88GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q3_K_L.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q3_K_L.gguf) | Q3_K_L | 2.05GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q3_K_S.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q3_K_S.gguf) | Q3_K_S | 1.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ3_M.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ3_M.gguf) | IQ3_M | 1.78GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ3_S.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ3_S.gguf) | IQ3_S | 1.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ3_XS.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ3_XS.gguf) | IQ3_XS | 1.60GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ3_XXS.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ3_XXS.gguf) | IQ3_XXS | 1.48GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q2_K.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q2_K.gguf) | Q2_K | 1.45GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.Q2_K_S.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.Q2_K_S.gguf) | Q2_K_S | 1.33GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ2_M.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ2_M.gguf) | IQ2_M | 1.35GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ2_S.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ2_S.gguf) | IQ2_S | 1.26GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ2_XS.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ2_XS.gguf) | IQ2_XS | 1.16GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ2_XXS.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ2_XXS.gguf) | IQ2_XXS | 1.06GB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ1_M.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ1_M.gguf) | IQ1_M | 950.14MB | ✅ Available | 🟢 IMatrix | 📦 No
| [unsloth-Phi-3.5-mini-instruct.IQ1_S.gguf](https://huggingface.co/legraphista/unsloth-Phi-3.5-mini-instruct-IMat-GGUF/blob/main/unsloth-Phi-3.5-mini-instruct.IQ1_S.gguf) | IQ1_S | 881.72MB | ✅ 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/unsloth-Phi-3.5-mini-instruct-IMat-GGUF --include "unsloth-Phi-3.5-mini-instruct.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/unsloth-Phi-3.5-mini-instruct-IMat-GGUF --include "unsloth-Phi-3.5-mini-instruct.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Simple chat template
```
<|user|>
{user_prompt}<|end|>
<|assistant|>
{assistant_response}<|end|>
<|user|>
{next_user_prompt}<|end|>
<|endoftext|>
```
### Chat template with system prompt
```
<|system|>
{system_prompt}<|end|>
<|user|>
{user_prompt}<|end|>
<|assistant|>
{assistant_response}<|end|>
<|user|>
{next_user_prompt}<|end|>
<|endoftext|>
```
### Llama.cpp
```
llama.cpp/main -m unsloth-Phi-3.5-mini-instruct.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: `unsloth-Phi-3.5-mini-instruct.Q8_0`)
3. Run `gguf-split --merge unsloth-Phi-3.5-mini-instruct.Q8_0/unsloth-Phi-3.5-mini-instruct.Q8_0-00001-of-XXXXX.gguf unsloth-Phi-3.5-mini-instruct.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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version https://git-lfs.github.com/spec/v1
oid sha256:6800af1e5dcbe9eafbd0c117b996bf3f0ea2f2303599eed9ec9bbe952c1d827f
size 3415325

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llama_model_loader: loaded meta data with 35 key-value pairs and 291 tensors from unsloth-Phi-3.5-mini-instruct-IMat-GGUF/unsloth-Phi-3.5-mini-instruct.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 = Unsloth Phi 3.5 Mini Instruct
llama_model_loader: - kv 3: general.finetune str = instruct
llama_model_loader: - kv 4: general.basename str = unsloth-Phi-3.5
llama_model_loader: - kv 5: general.size_label str = mini
llama_model_loader: - kv 6: general.license str = mit
llama_model_loader: - kv 7: general.license.link str = https://huggingface.co/microsoft/Phi-...
llama_model_loader: - kv 8: general.tags arr[str,4] = ["unsloth", "transformers", "phi3", "...
llama_model_loader: - kv 9: general.languages arr[str,1] = ["multilingual"]
llama_model_loader: - kv 10: llama.block_count u32 = 32
llama_model_loader: - kv 11: llama.context_length u32 = 131072
llama_model_loader: - kv 12: llama.embedding_length u32 = 3072
llama_model_loader: - kv 13: llama.feed_forward_length u32 = 8192
llama_model_loader: - kv 14: llama.attention.head_count u32 = 32
llama_model_loader: - kv 15: llama.attention.head_count_kv u32 = 32
llama_model_loader: - kv 16: llama.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 17: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 18: general.file_type u32 = 7
llama_model_loader: - kv 19: llama.vocab_size u32 = 32064
llama_model_loader: - kv 20: llama.rope.dimension_count u32 = 96
llama_model_loader: - kv 21: tokenizer.ggml.add_space_prefix bool = false
llama_model_loader: - kv 22: tokenizer.ggml.model str = llama
llama_model_loader: - kv 23: tokenizer.ggml.pre str = default
llama_model_loader: - kv 24: tokenizer.ggml.tokens arr[str,32064] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 25: tokenizer.ggml.scores arr[f32,32064] = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv 26: tokenizer.ggml.token_type arr[i32,32064] = [3, 3, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 27: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 32000
llama_model_loader: - kv 29: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 30: tokenizer.ggml.padding_token_id u32 = 32009
llama_model_loader: - kv 31: tokenizer.ggml.add_bos_token bool = false
llama_model_loader: - kv 32: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 33: tokenizer.chat_template str = {% for message in messages %}{% if me...
llama_model_loader: - kv 34: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q8_0: 226 tensors
llm_load_vocab: special tokens cache size = 14
llm_load_vocab: token to piece cache size = 0.1685 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32064
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 3072
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_rot = 96
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 96
llm_load_print_meta: n_embd_head_v = 96
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 3072
llm_load_print_meta: n_embd_v_gqa = 3072
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 = 8192
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 = 10000.0
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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 3.82 B
llm_load_print_meta: model size = 3.78 GiB (8.50 BPW)
llm_load_print_meta: general.name = Unsloth Phi 3.5 Mini Instruct
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 32000 '<|endoftext|>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: PAD token = 32009 '<|placeholder6|>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_print_meta: EOT token = 32007 '<|end|>'
llm_load_print_meta: max token length = 48
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 = 99.81 MiB
llm_load_tensors: CUDA0 buffer size = 3772.57 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 = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 192.00 MiB
llama_new_context_with_model: KV self size = 192.00 MiB, K (f16): 96.00 MiB, V (f16): 96.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.12 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 68.62 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 7.01 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 143.583 ms
compute_imatrix: computing over 151 chunks with batch_size 512
compute_imatrix: 0.42 seconds per pass - ETA 1.05 minutes
[1]5.6455,[2]4.2496,[3]4.2246,[4]4.7837,[5]5.2227,[6]5.3658,[7]4.8447,[8]5.3686,[9]5.5685,
save_imatrix: stored collected data after 10 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[10]5.9472,[11]5.9353,[12]5.4576,[13]5.3625,[14]5.6370,[15]6.0624,[16]6.1669,[17]6.4863,[18]6.6621,[19]6.8056,
save_imatrix: stored collected data after 20 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[20]6.9293,[21]7.1482,[22]6.8526,[23]6.5285,[24]6.5929,[25]6.6662,[26]6.5827,[27]6.4753,[28]6.5536,[29]6.7352,
save_imatrix: stored collected data after 30 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[30]6.8579,[31]6.8264,[32]6.9704,[33]7.0894,[34]7.2737,[35]7.2872,[36]7.2740,[37]6.9656,[38]6.7646,[39]6.6571,
save_imatrix: stored collected data after 40 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[40]6.5504,[41]6.4638,[42]6.3819,[43]6.2510,[44]6.1741,[45]6.0896,[46]6.0434,[47]6.0518,[48]6.1210,[49]6.2249,
save_imatrix: stored collected data after 50 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[50]6.2443,[51]6.4326,[52]6.6097,[53]6.8114,[54]7.0049,[55]7.1141,[56]7.0518,[57]6.9795,[58]6.9947,[59]7.0529,
save_imatrix: stored collected data after 60 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[60]7.1530,[61]7.0435,[62]7.0612,[63]7.1083,[64]7.1889,[65]7.2617,[66]7.2995,[67]7.3482,[68]7.4029,[69]7.3956,
save_imatrix: stored collected data after 70 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[70]7.4210,[71]7.4304,[72]7.4420,[73]7.3864,[74]7.3039,[75]7.2846,[76]7.3311,[77]7.3356,[78]7.2983,[79]7.2867,
save_imatrix: stored collected data after 80 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[80]7.2890,[81]7.2540,[82]7.2330,[83]7.2053,[84]7.2096,[85]7.2169,[86]7.2051,[87]7.2006,[88]7.1903,[89]7.1772,
save_imatrix: stored collected data after 90 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[90]7.1623,[91]7.1720,[92]7.1281,[93]7.1245,[94]7.0971,[95]7.0535,[96]7.0674,[97]7.0479,[98]7.0504,[99]7.0249,
save_imatrix: stored collected data after 100 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[100]7.0132,[101]7.0250,[102]6.9877,[103]6.9490,[104]6.9416,[105]6.9636,[106]6.9712,[107]6.9971,[108]7.0300,[109]6.9885,
save_imatrix: stored collected data after 110 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[110]6.9476,[111]6.9077,[112]6.8681,[113]6.8215,[114]6.7730,[115]6.7377,[116]6.6973,[117]6.6657,[118]6.6775,[119]6.6842,
save_imatrix: stored collected data after 120 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[120]6.7383,[121]6.7896,[122]6.8503,[123]6.9010,[124]6.9832,[125]7.0567,[126]7.0667,[127]7.0719,[128]7.0068,[129]7.0001,
save_imatrix: stored collected data after 130 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[130]6.9700,[131]6.9500,[132]6.9093,[133]6.8680,[134]6.8811,[135]6.9009,[136]6.8981,[137]6.8963,[138]6.9030,[139]6.9162,
save_imatrix: stored collected data after 140 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[140]6.9331,[141]6.9349,[142]6.9355,[143]6.9334,[144]6.9116,[145]6.9291,[146]6.9632,[147]7.0090,[148]7.0513,[149]7.0970,
save_imatrix: stored collected data after 150 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
[150]7.1404,[151]7.1870,
save_imatrix: stored collected data after 151 chunks in unsloth-Phi-3.5-mini-instruct-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 1420.67 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 = 55358.52 ms / 77312 tokens ( 0.72 ms per token, 1396.57 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 = 56810.47 ms / 77313 tokens
Final estimate: PPL = 7.1870 +/- 0.09220

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