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Model: legraphista/AutoCoder_S_6.7B-IMat-GGUF
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---
base_model: Bin12345/AutoCoder_S_6.7B
inference: false
library_name: gguf
license: apache-2.0
pipeline_tag: text-generation
quantized_by: legraphista
tags:
- quantized
- GGUF
- imatrix
- quantization
- imat
- imatrix
- static
---
# AutoCoder_S_6.7B-IMat-GGUF
_Llama.cpp imatrix quantization of Bin12345/AutoCoder_S_6.7B_
Original Model: [Bin12345/AutoCoder_S_6.7B](https://huggingface.co/Bin12345/AutoCoder_S_6.7B)
Original dtype: `BF16` (`bfloat16`)
Quantized by: llama.cpp [b3010](https://github.com/ggerganov/llama.cpp/releases/tag/b3010)
IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw)
- [AutoCoder_S_6.7B-IMat-GGUF](#autocoder-s-6-7b-imat-gguf)
- [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/AutoCoder_S_6.7B-IMat-GGUF/blob/main/imatrix.dat)
### Common Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [AutoCoder_S_6.7B.Q8_0.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q8_0.gguf) | Q8_0 | 7.16GB | ✅ Available | ⚪ Static | 📦 No
| [AutoCoder_S_6.7B.Q6_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q6_K.gguf) | Q6_K | 5.53GB | ✅ Available | ⚪ Static | 📦 No
| [AutoCoder_S_6.7B.Q4_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q4_K.gguf) | Q4_K | 4.08GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.Q3_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K.gguf) | Q3_K | 3.30GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.Q2_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q2_K.gguf) | Q2_K | 2.53GB | ✅ Available | 🟢 IMatrix | 📦 No
### All Quants
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
| -------- | ---------- | --------- | ------ | ------------ | -------- |
| [AutoCoder_S_6.7B.BF16.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.BF16.gguf) | BF16 | 13.48GB | ✅ Available | ⚪ Static | 📦 No
| [AutoCoder_S_6.7B.FP16.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.FP16.gguf) | F16 | 13.48GB | ✅ Available | ⚪ Static | 📦 No
| [AutoCoder_S_6.7B.Q5_K.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q5_K.gguf) | Q5_K | 4.79GB | ✅ Available | ⚪ Static | 📦 No
| [AutoCoder_S_6.7B.Q5_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q5_K_S.gguf) | Q5_K_S | 4.65GB | ✅ Available | ⚪ Static | 📦 No
| [AutoCoder_S_6.7B.Q4_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q4_K_S.gguf) | Q4_K_S | 3.86GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.Q3_K_L.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K_L.gguf) | Q3_K_L | 3.60GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.Q3_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q3_K_S.gguf) | Q3_K_S | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.Q2_K_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.Q2_K_S.gguf) | Q2_K_S | 2.32GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ4_NL.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ4_NL.gguf) | IQ4_NL | 3.83GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ4_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ4_XS.gguf) | IQ4_XS | 3.62GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ3_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_M.gguf) | IQ3_M | 3.12GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ3_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_S.gguf) | IQ3_S | 2.95GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ3_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_XS.gguf) | IQ3_XS | 2.80GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ3_XXS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ3_XXS.gguf) | IQ3_XXS | 2.59GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ2_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_M.gguf) | IQ2_M | 2.36GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ2_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_S.gguf) | IQ2_S | 2.20GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ2_XS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_XS.gguf) | IQ2_XS | 2.04GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ2_XXS.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ2_XXS.gguf) | IQ2_XXS | 1.86GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ1_M.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ1_M.gguf) | IQ1_M | 1.65GB | ✅ Available | 🟢 IMatrix | 📦 No
| [AutoCoder_S_6.7B.IQ1_S.gguf](https://huggingface.co/legraphista/AutoCoder_S_6.7B-IMat-GGUF/blob/main/AutoCoder_S_6.7B.IQ1_S.gguf) | IQ1_S | 1.53GB | ✅ 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/AutoCoder_S_6.7B-IMat-GGUF --include "AutoCoder_S_6.7B.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/AutoCoder_S_6.7B-IMat-GGUF --include "AutoCoder_S_6.7B.Q8_0/*" --local-dir ./
# see FAQ for merging GGUF's
```
---
## Inference
### Simple chat template
```
Human: Can you provide ways to eat combinations of bananas and dragonfruits?
Assistant: Sure! Here are some ways to eat bananas and dragonfruits together:
1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<｜end▁of▁sentence｜>
Human: What about solving an 2x + 3 = 7 equation?
Assistant:
```
### Chat template with system prompt
```
You are a helpful AI.
Human: Can you provide ways to eat combinations of bananas and dragonfruits?
Assistant: Sure! Here are some ways to eat bananas and dragonfruits together:
1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey.
2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.<｜end▁of▁sentence｜>
Human: What about solving an 2x + 3 = 7 equation?
Assistant:
```
### Llama.cpp
```
llama.cpp/main -m AutoCoder_S_6.7B.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: `AutoCoder_S_6.7B.Q8_0`)
3. Run `gguf-split --merge AutoCoder_S_6.7B.Q8_0/AutoCoder_S_6.7B.Q8_0-00001-of-XXXXX.gguf AutoCoder_S_6.7B.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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main: build = 3010 (95f84d5c)
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
main: seed = 1716905720
llama_model_loader: loaded meta data with 27 key-value pairs and 291 tensors from AutoCoder_S_6.7B-IMat-GGUF/AutoCoder_S_6.7B.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 = AutoCoder_S_6.7B
llama_model_loader: - kv 2: llama.block_count u32 = 32
llama_model_loader: - kv 3: llama.context_length u32 = 16384
llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 11008
llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 32
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 100000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 10: general.file_type u32 = 0
llama_model_loader: - kv 11: llama.vocab_size u32 = 32256
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: llama.rope.scaling.type str = linear
llama_model_loader: - kv 14: llama.rope.scaling.factor f32 = 4.000000
llama_model_loader: - kv 15: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 16: tokenizer.ggml.pre str = deepseek-coder
llama_model_loader: - kv 17: tokenizer.ggml.tokens arr[str,32256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,32256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: tokenizer.ggml.merges arr[str,31757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
llama_model_loader: - kv 20: tokenizer.ggml.bos_token_id u32 = 32013
llama_model_loader: - kv 21: tokenizer.ggml.eos_token_id u32 = 32021
llama_model_loader: - kv 22: tokenizer.ggml.padding_token_id u32 = 32014
llama_model_loader: - kv 23: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 24: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 25: tokenizer.chat_template str = {% if messages[0]['role'] == 'system'...
llama_model_loader: - kv 26: general.quantization_version u32 = 2
llama_model_loader: - type f32: 291 tensors
llm_load_vocab: mismatch in special tokens definition ( 243/32256 vs 256/32256 ).
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 = 32256
llm_load_print_meta: n_merges = 31757
llm_load_print_meta: n_ctx_train = 16384
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
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 = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
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 = 11008
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 = 100000.0
llm_load_print_meta: freq_scale_train = 0.25
llm_load_print_meta: n_yarn_orig_ctx = 16384
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 = 7B
llm_load_print_meta: model ftype = all F32
llm_load_print_meta: model params = 6.74 B
llm_load_print_meta: model size = 25.11 GiB (32.00 BPW)
llm_load_print_meta: general.name = AutoCoder_S_6.7B
llm_load_print_meta: BOS token = 32013 '<｜begin▁of▁sentence｜>'
llm_load_print_meta: EOS token = 32021 '<|EOT|>'
llm_load_print_meta: PAD token = 32014 '<｜end▁of▁sentence｜>'
llm_load_print_meta: LF token = 126 'Ä'
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 29 repeating layers to GPU
llm_load_tensors: offloaded 29/33 layers to GPU
llm_load_tensors: CPU buffer size = 25713.02 MiB
llm_load_tensors: CUDA0 buffer size = 22388.91 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 = 100000.0
llama_new_context_with_model: freq_scale = 0.25
llama_kv_cache_init: CUDA_Host KV buffer size = 24.00 MiB
llama_kv_cache_init: CUDA0 KV buffer size = 232.00 MiB
llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.12 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 575.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 17.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 37
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 394.173 ms
compute_imatrix: computing over 236 chunks with batch_size 512
compute_imatrix: 1.41 seconds per pass - ETA 5.53 minutes
[1]6.9711,[2]5.6324,[3]5.7695,[4]6.9482,[5]7.1003,[6]6.8935,[7]6.0051,[8]6.8299,[9]6.5963,
save_imatrix: stored collected data after 10 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[10]7.4973,[11]7.8150,[12]7.6111,[13]8.2735,[14]7.5730,[15]8.4049,[16]8.5410,[17]8.9150,[18]9.0491,[19]9.3996,
save_imatrix: stored collected data after 20 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[20]9.1504,[21]9.4010,[22]9.2047,[23]8.7323,[24]8.8343,[25]8.2072,[26]7.7374,[27]7.4032,[28]7.2794,[29]7.3243,
save_imatrix: stored collected data after 30 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[30]7.4102,[31]7.5618,[32]7.7531,[33]8.0081,[34]7.8609,[35]7.4665,[36]7.1310,[37]7.0749,[38]7.0595,[39]7.0472,
save_imatrix: stored collected data after 40 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[40]7.0351,[41]7.1613,[42]7.3376,[43]7.4777,[44]7.7039,[45]7.6982,[46]7.8623,[47]8.0818,[48]8.2927,[49]8.5157,
save_imatrix: stored collected data after 50 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[50]8.6643,[51]8.5805,[52]8.4288,[53]8.2782,[54]8.1212,[55]8.2830,[56]8.3925,[57]8.4649,[58]8.6312,[59]8.6761,
save_imatrix: stored collected data after 60 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[60]8.8491,[61]9.0009,[62]9.1895,[63]9.3434,[64]9.4736,[65]9.5979,[66]9.6884,[67]9.8557,[68]9.9761,[69]10.0247,
save_imatrix: stored collected data after 70 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[70]10.0730,[71]9.9550,[72]9.9019,[73]9.8934,[74]9.8477,[75]9.8404,[76]9.8001,[77]9.7517,[78]9.6413,[79]9.5902,
save_imatrix: stored collected data after 80 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[80]9.5973,[81]9.5626,[82]9.6427,[83]9.7263,[84]9.8151,[85]9.6581,[86]9.6787,[87]9.6081,[88]9.6448,[89]9.7148,
save_imatrix: stored collected data after 90 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[90]9.7689,[91]9.8819,[92]9.9310,[93]10.0121,[94]10.0795,[95]10.0701,[96]10.0096,[97]10.0003,[98]10.0176,[99]10.0750,
save_imatrix: stored collected data after 100 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[100]10.1176,[101]10.1105,[102]10.1082,[103]10.0825,[104]10.0619,[105]10.0554,[106]10.0109,[107]9.9972,[108]9.9992,[109]9.9618,
save_imatrix: stored collected data after 110 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[110]9.9424,[111]9.9028,[112]9.9015,[113]9.8885,[114]9.8615,[115]9.8325,[116]9.8164,[117]9.8120,[118]9.7936,[119]9.7131,
save_imatrix: stored collected data after 120 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[120]9.7549,[121]9.7954,[122]9.8028,[123]9.7688,[124]9.7939,[125]9.8058,[126]9.7924,[127]9.7033,[128]9.7055,[129]9.7133,
save_imatrix: stored collected data after 130 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[130]9.6597,[131]9.6724,[132]9.5981,[133]9.5206,[134]9.4413,[135]9.3635,[136]9.2893,[137]9.2113,[138]9.1416,[139]9.0679,
save_imatrix: stored collected data after 140 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[140]9.0140,[141]8.9418,[142]8.8786,[143]8.8081,[144]8.7210,[145]8.6630,[146]8.6062,[147]8.5428,[148]8.4765,[149]8.4173,
save_imatrix: stored collected data after 150 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[150]8.3599,[151]8.2933,[152]8.2349,[153]8.1789,[154]8.1178,[155]8.0694,[156]8.0121,[157]7.9777,[158]7.9050,[159]7.8430,
save_imatrix: stored collected data after 160 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[160]7.8331,[161]7.8808,[162]7.9044,[163]7.9540,[164]8.0023,[165]7.9800,[166]8.0035,[167]7.9998,[168]7.9850,[169]7.9934,
save_imatrix: stored collected data after 170 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[170]7.9962,[171]8.0055,[172]7.9904,[173]8.0206,[174]8.0128,[175]8.0323,[176]8.0310,[177]8.0447,[178]8.0503,[179]8.0647,
save_imatrix: stored collected data after 180 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[180]8.0664,[181]8.0854,[182]8.1037,[183]8.1084,[184]8.1318,[185]8.1649,[186]8.2033,[187]8.2198,[188]8.2490,[189]8.2654,
save_imatrix: stored collected data after 190 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[190]8.2910,[191]8.3175,[192]8.3504,[193]8.3763,[194]8.3824,[195]8.4359,[196]8.4523,[197]8.4449,[198]8.5025,[199]8.5595,
save_imatrix: stored collected data after 200 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[200]8.6122,[201]8.6825,[202]8.7286,[203]8.7456,[204]8.7602,[205]8.7219,[206]8.7232,[207]8.7519,[208]8.7948,[209]8.8008,
save_imatrix: stored collected data after 210 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[210]8.8094,[211]8.8229,[212]8.8434,[213]8.8669,[214]8.8710,[215]8.8792,[216]8.8937,[217]8.9263,[218]8.9901,[219]8.9619,
save_imatrix: stored collected data after 220 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[220]8.9730,[221]8.9575,[222]8.9700,[223]8.9656,[224]8.9616,[225]8.9832,[226]8.9590,[227]8.9759,[228]8.9843,[229]9.0451,
save_imatrix: stored collected data after 230 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
[230]9.1160,[231]9.1884,[232]9.2569,[233]9.3015,[234]9.2740,[235]9.2473,[236]9.2192,
save_imatrix: stored collected data after 236 chunks in AutoCoder_S_6.7B-IMat-GGUF/imatrix.dat
llama_print_timings: load time = 4638.76 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 = 291757.04 ms / 120832 tokens ( 2.41 ms per token, 414.15 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 = 300310.61 ms / 120833 tokens
Final estimate: PPL = 9.2192 +/- 0.10306