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---
base_model: Qwen/Qwen2-Math-7B-Instruct
inference: false
language:
- en
library_name: gguf
license: apache-2.0
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- chat
- quantized
- GGUF
- quantization
- imat
- imatrix
- static
- 16bit
- 8bit
- 6bit
- 5bit
- 4bit
- 3bit
- 2bit
- 1bit
---
# Qwen2-Math-7B-Instruct-IMat-GGUF
_Llama.cpp imatrix quantization of Qwen/Qwen2-Math-7B-Instruct_
Original Model: [Qwen/Qwen2-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2-Math-7B-Instruct)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3547](https://github.com/ggerganov/llama.cpp/releases/tag/b3547)
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/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Qwen2-Math-7B-Instruct.Q8_0.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q8_0.gguf) | Q8_0 | 8.10GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.Q6_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q6_K.gguf) | Q6_K | 6.25GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.Q4_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q4_K.gguf) | Q4_K | 4.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q3_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q3_K.gguf) | Q3_K | 3.81GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q2_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q2_K.gguf) | Q2_K | 3.02GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [Qwen2-Math-7B-Instruct.BF16.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.BF16.gguf) | BF16 | 15.24GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.FP16.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.FP16.gguf) | F16 | 15.24GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.Q8_0.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q8_0.gguf) | Q8_0 | 8.10GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.Q6_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q6_K.gguf) | Q6_K | 6.25GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.Q5_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q5_K.gguf) | Q5_K | 5.44GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.Q5_K_S.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.32GB | ✅ Available | ⚪ Static | 📦 No
| [Qwen2-Math-7B-Instruct.Q4_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q4_K.gguf) | Q4_K | 4.68GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q4_K_S.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.46GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ4_NL.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ4_NL.gguf) | IQ4_NL | 4.44GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ4_XS.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.22GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q3_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q3_K.gguf) | Q3_K | 3.81GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q3_K_L.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q3_K_L.gguf) | Q3_K_L | 4.09GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q3_K_S.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.49GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ3_M.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ3_M.gguf) | IQ3_M | 3.57GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ3_S.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ3_S.gguf) | IQ3_S | 3.50GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ3_XS.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ3_XS.gguf) | IQ3_XS | 3.35GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ3_XXS.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ3_XXS.gguf) | IQ3_XXS | 3.11GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q2_K.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q2_K.gguf) | Q2_K | 3.02GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.Q2_K_S.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.Q2_K_S.gguf) | Q2_K_S | 2.83GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ2_M.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ2_M.gguf) | IQ2_M | 2.78GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ2_S.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ2_S.gguf) | IQ2_S | 2.60GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ2_XS.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ2_XS.gguf) | IQ2_XS | 2.47GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ2_XXS.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ2_XXS.gguf) | IQ2_XXS | 2.27GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ1_M.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ1_M.gguf) | IQ1_M | 2.04GB | ✅ Available | 🟢 IMatrix | 📦 No
| [Qwen2-Math-7B-Instruct.IQ1_S.gguf](https://huggingface.co/legraphista/Qwen2-Math-7B-Instruct-IMat-GGUF/blob/main/Qwen2-Math-7B-Instruct.IQ1_S.gguf) | IQ1_S | 1.90GB | ✅ 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/Qwen2-Math-7B-Instruct-IMat-GGUF --include "Qwen2-Math-7B-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/Qwen2-Math-7B-Instruct-IMat-GGUF --include "Qwen2-Math-7B-Instruct.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Simple chat template
```
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|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
```
<|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 Qwen2-Math-7B-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: `Qwen2-Math-7B-Instruct.Q8_0`)
3. Run `gguf-split --merge Qwen2-Math-7B-Instruct.Q8_0/Qwen2-Math-7B-Instruct.Q8_0-00001-of-XXXXX.gguf Qwen2-Math-7B-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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oid sha256:31e7d069cdaa50209932d88b0e2e15058bc5ac36e32933bf39d4b73fe6b193c8
size 4536690

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llama_model_loader: loaded meta data with 28 key-value pairs and 339 tensors from Qwen2-Math-7B-Instruct-IMat-GGUF/Qwen2-Math-7B-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 = qwen2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Qwen2 Math 7B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Qwen2-Math
llama_model_loader: - kv 5: general.size_label str = 7B
llama_model_loader: - kv 6: general.license str = apache-2.0
llama_model_loader: - kv 7: general.tags arr[str,2] = ["chat", "text-generation"]
llama_model_loader: - kv 8: general.languages arr[str,1] = ["en"]
llama_model_loader: - kv 9: qwen2.block_count u32 = 28
llama_model_loader: - kv 10: qwen2.context_length u32 = 4096
llama_model_loader: - kv 11: qwen2.embedding_length u32 = 3584
llama_model_loader: - kv 12: qwen2.feed_forward_length u32 = 18944
llama_model_loader: - kv 13: qwen2.attention.head_count u32 = 28
llama_model_loader: - kv 14: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: - kv 15: qwen2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 16: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 17: general.file_type u32 = 7
llama_model_loader: - kv 18: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 19: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 20: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 21: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 22: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 23: tokenizer.ggml.eos_token_id u32 = 151645
llama_model_loader: - kv 24: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 26: tokenizer.chat_template str = {% for message in messages %}{% if lo...
llama_model_loader: - kv 27: general.quantization_version u32 = 2
llama_model_loader: - type f32: 141 tensors
llama_model_loader: - type q8_0: 198 tensors
llm_load_vocab: special tokens cache size = 3
llm_load_vocab: token to piece cache size = 0.9308 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 152064
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_embd = 3584
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_head = 28
llm_load_print_meta: n_head_kv = 4
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 = 7
llm_load_print_meta: n_embd_k_gqa = 512
llm_load_print_meta: n_embd_v_gqa = 512
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
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 = 18944
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 = 2
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 = 4096
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 = ?B
llm_load_print_meta: model ftype = Q8_0
llm_load_print_meta: model params = 7.62 B
llm_load_print_meta: model size = 7.54 GiB (8.50 BPW)
llm_load_print_meta: general.name = Qwen2 Math 7B Instruct
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151645 '<|im_end|>'
llm_load_print_meta: PAD token = 151643 '<|endoftext|>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: EOT token = 151645 '<|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 = 0.30 MiB
llm_load_tensors: offloading 28 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 29/29 layers to GPU
llm_load_tensors: CPU buffer size = 552.23 MiB
llm_load_tensors: CUDA0 buffer size = 7165.44 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 = 28.00 MiB
llama_new_context_with_model: KV self size = 28.00 MiB, K (f16): 14.00 MiB, V (f16): 14.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.58 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 304.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 8.01 MiB
llama_new_context_with_model: graph nodes = 986
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 134.361 ms
compute_imatrix: computing over 128 chunks with batch_size 512
compute_imatrix: 0.70 seconds per pass - ETA 1.48 minutes
[1]18.0590,[2]11.2011,[3]9.5899,[4]11.0452,[5]10.6825,[6]10.1264,[7]10.3411,[8]10.2988,[9]11.3523,
save_imatrix: stored collected data after 10 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[10]10.7695,[11]10.1874,[12]11.2178,[13]12.6955,[14]13.1547,[15]14.7406,[16]15.3128,[17]15.8186,[18]17.0696,[19]16.6457,
save_imatrix: stored collected data after 20 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[20]16.5903,[21]17.5665,[22]17.8621,[23]17.8537,[24]18.4056,[25]18.9724,[26]19.0466,[27]20.0233,[28]20.7322,[29]21.5257,
save_imatrix: stored collected data after 30 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[30]21.4964,[31]21.5009,[32]20.7806,[33]20.2614,[34]19.6217,[35]19.2250,[36]19.5374,[37]20.5638,[38]21.1323,[39]21.3799,
save_imatrix: stored collected data after 40 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[40]21.8888,[41]22.0149,[42]23.1586,[43]23.9396,[44]24.8159,[45]25.5009,[46]25.9405,[47]25.5047,[48]25.5574,[49]25.6802,
save_imatrix: stored collected data after 50 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[50]25.6914,[51]25.3650,[52]25.5112,[53]26.1704,[54]26.4285,[55]27.0047,[56]27.2073,[57]27.2955,[58]27.4467,[59]27.3533,
save_imatrix: stored collected data after 60 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[60]27.4828,[61]27.2780,[62]27.0881,[63]27.2735,[64]27.5393,[65]27.3328,[66]27.1882,[67]27.0582,[68]26.5715,[69]26.3075,
save_imatrix: stored collected data after 70 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[70]26.0733,[71]25.7333,[72]25.5042,[73]25.3687,[74]24.9236,[75]24.4856,[76]24.0797,[77]23.8348,[78]23.6856,[79]23.5075,
save_imatrix: stored collected data after 80 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[80]23.2261,[81]23.1967,[82]23.0807,[83]22.8498,[84]22.8795,[85]22.8150,[86]22.7432,[87]22.5736,[88]22.4748,[89]22.5302,
save_imatrix: stored collected data after 90 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[90]22.6007,[91]22.5752,[92]22.2275,[93]22.0666,[94]21.7474,[95]21.4864,[96]21.3032,[97]21.0089,[98]20.7840,[99]20.7967,
save_imatrix: stored collected data after 100 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[100]20.7888,[101]20.7904,[102]21.0381,[103]21.3159,[104]21.5573,[105]21.9637,[106]22.3012,[107]22.3843,[108]22.2405,[109]22.2636,
save_imatrix: stored collected data after 110 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[110]22.2668,[111]22.0337,[112]21.7606,[113]21.6516,[114]21.7166,[115]21.7715,[116]21.8111,[117]21.8969,[118]21.9941,[119]21.9955,
save_imatrix: stored collected data after 120 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
[120]21.9721,[121]21.9586,[122]21.7884,[123]21.8810,[124]22.0677,[125]22.2065,[126]22.4323,[127]22.6586,[128]22.8041,
save_imatrix: stored collected data after 128 chunks in Qwen2-Math-7B-Instruct-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 2190.72 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 = 68655.99 ms / 65536 tokens ( 1.05 ms per token, 954.56 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 = 71144.16 ms / 65537 tokens
Final estimate: PPL = 22.8041 +/- 0.48532