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Model: legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF Source: Original Platform
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---
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base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Base
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inference: false
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library_name: gguf
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license: other
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license_link: LICENSE
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license_name: deepseek-license
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pipeline_tag: text-generation
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quantized_by: legraphista
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tags:
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- quantized
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- GGUF
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- imatrix
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- quantization
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- imat
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- imatrix
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- static
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- 16bit
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- 8bit
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- 6bit
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- 5bit
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- 4bit
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- 3bit
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- 2bit
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- 1bit
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---
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# DeepSeek-Coder-V2-Lite-Base-IMat-GGUF
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_Llama.cpp imatrix quantization of deepseek-ai/DeepSeek-Coder-V2-Lite-Base_
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Original Model: [deepseek-ai/DeepSeek-Coder-V2-Lite-Base](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base)
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Original dtype: `BF16` (`bfloat16`)
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Quantized by: llama.cpp [b3166](https://github.com/ggerganov/llama.cpp/releases/tag/b3166)
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IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt)
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- [Files](#files)
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- [IMatrix](#imatrix)
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- [Common Quants](#common-quants)
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- [All Quants](#all-quants)
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- [Downloading using huggingface-cli](#downloading-using-huggingface-cli)
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- [Inference](#inference)
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- [Simple chat template](#simple-chat-template)
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- [Chat template with system prompt](#chat-template-with-system-prompt)
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- [Llama.cpp](#llama-cpp)
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- [FAQ](#faq)
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- [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere)
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- [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf)
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---
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## Files
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### IMatrix
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Status: ✅ Available
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Link: [here](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/imatrix.dat)
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### Common Quants
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| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
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| -------- | ---------- | --------- | ------ | ------------ | -------- |
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| [DeepSeek-Coder-V2-Lite-Base.Q8_0.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q8_0.gguf) | Q8_0 | 16.70GB | ✅ Available | ⚪ Static | 📦 No
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| [DeepSeek-Coder-V2-Lite-Base.Q6_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q6_K.gguf) | Q6_K | 14.07GB | ✅ Available | ⚪ Static | 📦 No
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| [DeepSeek-Coder-V2-Lite-Base.Q4_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q4_K.gguf) | Q4_K | 10.36GB | ✅ Available | 🟢 IMatrix | 📦 No
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| [DeepSeek-Coder-V2-Lite-Base.Q3_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q3_K.gguf) | Q3_K | 8.13GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q2_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q2_K.gguf) | Q2_K | 6.43GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
|
||||
|
||||
### All Quants
|
||||
| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
|
||||
| -------- | ---------- | --------- | ------ | ------------ | -------- |
|
||||
| [DeepSeek-Coder-V2-Lite-Base.BF16.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.BF16.gguf) | BF16 | 31.42GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.FP16.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.FP16.gguf) | F16 | 31.42GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q8_0.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q8_0.gguf) | Q8_0 | 16.70GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q6_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q6_K.gguf) | Q6_K | 14.07GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q5_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q5_K.gguf) | Q5_K | 11.85GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q5_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q5_K_S.gguf) | Q5_K_S | 11.14GB | ✅ Available | ⚪ Static | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q4_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q4_K.gguf) | Q4_K | 10.36GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q4_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q4_K_S.gguf) | Q4_K_S | 9.53GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ4_NL.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ4_NL.gguf) | IQ4_NL | 8.91GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ4_XS.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ4_XS.gguf) | IQ4_XS | 8.57GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q3_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q3_K.gguf) | Q3_K | 8.13GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q3_K_L.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q3_K_L.gguf) | Q3_K_L | 8.46GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q3_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q3_K_S.gguf) | Q3_K_S | 7.49GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ3_M.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ3_M.gguf) | IQ3_M | 7.55GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ3_S.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ3_S.gguf) | IQ3_S | 7.49GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ3_XS.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ3_XS.gguf) | IQ3_XS | 7.12GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ3_XXS.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ3_XXS.gguf) | IQ3_XXS | 6.96GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q2_K.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q2_K.gguf) | Q2_K | 6.43GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.Q2_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.Q2_K_S.gguf) | Q2_K_S | 6.46GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ2_M.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ2_M.gguf) | IQ2_M | 6.33GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ2_S.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ2_S.gguf) | IQ2_S | 6.01GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ2_XS.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ2_XS.gguf) | IQ2_XS | 5.97GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ2_XXS.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ2_XXS.gguf) | IQ2_XXS | 5.64GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ1_M.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ1_M.gguf) | IQ1_M | 5.24GB | ✅ Available | 🟢 IMatrix | 📦 No
|
||||
| [DeepSeek-Coder-V2-Lite-Base.IQ1_S.gguf](https://huggingface.co/legraphista/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/blob/main/DeepSeek-Coder-V2-Lite-Base.IQ1_S.gguf) | IQ1_S | 4.99GB | ✅ 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/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF --include "DeepSeek-Coder-V2-Lite-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/DeepSeek-Coder-V2-Lite-Base-IMat-GGUF --include "DeepSeek-Coder-V2-Lite-Base.Q8_0/*" --local-dir ./
|
||||
# see FAQ for merging GGUF's
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Inference
|
||||
|
||||
### Simple chat template
|
||||
```
|
||||
<｜begin▁of▁sentence｜>User: {user_prompt}
|
||||
|
||||
Assistant: {assistant_response}<｜end▁of▁sentence｜>User: {next_user_prompt}
|
||||
|
||||
|
||||
```
|
||||
|
||||
### Chat template with system prompt
|
||||
```
|
||||
<｜begin▁of▁sentence｜>{system_prompt}
|
||||
|
||||
User: {user_prompt}
|
||||
|
||||
Assistant: {assistant_response}<｜end▁of▁sentence｜>User: {next_user_prompt}
|
||||
|
||||
|
||||
```
|
||||
|
||||
### Llama.cpp
|
||||
```
|
||||
llama.cpp/main -m DeepSeek-Coder-V2-Lite-Base.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: `DeepSeek-Coder-V2-Lite-Base.Q8_0`)
|
||||
3. Run `gguf-split --merge DeepSeek-Coder-V2-Lite-Base.Q8_0/DeepSeek-Coder-V2-Lite-Base.Q8_0-00001-of-XXXXX.gguf DeepSeek-Coder-V2-Lite-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)!
|
||||
3
imatrix.dat
Normal file
3
imatrix.dat
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:576899b482d04966cdf62c050de61e695b73d7349e0818597ef0d768094efc9d
|
||||
size 38356438
|
||||
2482
imatrix.dataset
Normal file
2482
imatrix.dataset
Normal file
File diff suppressed because one or more lines are too long
161
imatrix.log
Normal file
161
imatrix.log
Normal file
@@ -0,0 +1,161 @@
|
||||
llama_model_loader: loaded meta data with 38 key-value pairs and 377 tensors from DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/DeepSeek-Coder-V2-Lite-Base.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 = deepseek2
|
||||
llama_model_loader: - kv 1: general.name str = DeepSeek-Coder-V2-Lite-Base
|
||||
llama_model_loader: - kv 2: deepseek2.block_count u32 = 27
|
||||
llama_model_loader: - kv 3: deepseek2.context_length u32 = 163840
|
||||
llama_model_loader: - kv 4: deepseek2.embedding_length u32 = 2048
|
||||
llama_model_loader: - kv 5: deepseek2.feed_forward_length u32 = 10944
|
||||
llama_model_loader: - kv 6: deepseek2.attention.head_count u32 = 16
|
||||
llama_model_loader: - kv 7: deepseek2.attention.head_count_kv u32 = 16
|
||||
llama_model_loader: - kv 8: deepseek2.rope.freq_base f32 = 10000.000000
|
||||
llama_model_loader: - kv 9: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
|
||||
llama_model_loader: - kv 10: deepseek2.expert_used_count u32 = 6
|
||||
llama_model_loader: - kv 11: general.file_type u32 = 0
|
||||
llama_model_loader: - kv 12: deepseek2.leading_dense_block_count u32 = 1
|
||||
llama_model_loader: - kv 13: deepseek2.vocab_size u32 = 102400
|
||||
llama_model_loader: - kv 14: deepseek2.attention.kv_lora_rank u32 = 512
|
||||
llama_model_loader: - kv 15: deepseek2.attention.key_length u32 = 192
|
||||
llama_model_loader: - kv 16: deepseek2.attention.value_length u32 = 128
|
||||
llama_model_loader: - kv 17: deepseek2.expert_feed_forward_length u32 = 1408
|
||||
llama_model_loader: - kv 18: deepseek2.expert_count u32 = 64
|
||||
llama_model_loader: - kv 19: deepseek2.expert_shared_count u32 = 2
|
||||
llama_model_loader: - kv 20: deepseek2.expert_weights_scale f32 = 1.000000
|
||||
llama_model_loader: - kv 21: deepseek2.rope.dimension_count u32 = 64
|
||||
llama_model_loader: - kv 22: deepseek2.rope.scaling.type str = yarn
|
||||
llama_model_loader: - kv 23: deepseek2.rope.scaling.factor f32 = 40.000000
|
||||
llama_model_loader: - kv 24: deepseek2.rope.scaling.original_context_length u32 = 4096
|
||||
llama_model_loader: - kv 25: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.070700
|
||||
llama_model_loader: - kv 26: tokenizer.ggml.model str = gpt2
|
||||
llama_model_loader: - kv 27: tokenizer.ggml.pre str = deepseek-llm
|
||||
llama_model_loader: - kv 28: tokenizer.ggml.tokens arr[str,102400] = ["!", "\"", "#", "$", "%", "&", "'", ...
|
||||
llama_model_loader: - kv 29: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
|
||||
llama_model_loader: - kv 30: tokenizer.ggml.merges arr[str,99757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
|
||||
llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 100000
|
||||
llama_model_loader: - kv 32: tokenizer.ggml.eos_token_id u32 = 100001
|
||||
llama_model_loader: - kv 33: tokenizer.ggml.padding_token_id u32 = 100001
|
||||
llama_model_loader: - kv 34: tokenizer.ggml.add_bos_token bool = true
|
||||
llama_model_loader: - kv 35: tokenizer.ggml.add_eos_token bool = false
|
||||
llama_model_loader: - kv 36: tokenizer.chat_template str = {% if not add_generation_prompt is de...
|
||||
llama_model_loader: - kv 37: general.quantization_version u32 = 2
|
||||
llama_model_loader: - type f32: 377 tensors
|
||||
llm_load_vocab: special tokens cache size = 2400
|
||||
llm_load_vocab: token to piece cache size = 0.6661 MB
|
||||
llm_load_print_meta: format = GGUF V3 (latest)
|
||||
llm_load_print_meta: arch = deepseek2
|
||||
llm_load_print_meta: vocab type = BPE
|
||||
llm_load_print_meta: n_vocab = 102400
|
||||
llm_load_print_meta: n_merges = 99757
|
||||
llm_load_print_meta: n_ctx_train = 163840
|
||||
llm_load_print_meta: n_embd = 2048
|
||||
llm_load_print_meta: n_head = 16
|
||||
llm_load_print_meta: n_head_kv = 16
|
||||
llm_load_print_meta: n_layer = 27
|
||||
llm_load_print_meta: n_rot = 64
|
||||
llm_load_print_meta: n_embd_head_k = 192
|
||||
llm_load_print_meta: n_embd_head_v = 128
|
||||
llm_load_print_meta: n_gqa = 1
|
||||
llm_load_print_meta: n_embd_k_gqa = 3072
|
||||
llm_load_print_meta: n_embd_v_gqa = 2048
|
||||
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 = 10944
|
||||
llm_load_print_meta: n_expert = 64
|
||||
llm_load_print_meta: n_expert_used = 6
|
||||
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 = yarn
|
||||
llm_load_print_meta: freq_base_train = 10000.0
|
||||
llm_load_print_meta: freq_scale_train = 0.025
|
||||
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 = 16B
|
||||
llm_load_print_meta: model ftype = all F32
|
||||
llm_load_print_meta: model params = 15.71 B
|
||||
llm_load_print_meta: model size = 58.51 GiB (32.00 BPW)
|
||||
llm_load_print_meta: general.name = DeepSeek-Coder-V2-Lite-Base
|
||||
llm_load_print_meta: BOS token = 100000 '<｜begin▁of▁sentence｜>'
|
||||
llm_load_print_meta: EOS token = 100001 '<｜end▁of▁sentence｜>'
|
||||
llm_load_print_meta: PAD token = 100001 '<｜end▁of▁sentence｜>'
|
||||
llm_load_print_meta: LF token = 126 'Ä'
|
||||
llm_load_print_meta: n_layer_dense_lead = 1
|
||||
llm_load_print_meta: n_lora_q = 0
|
||||
llm_load_print_meta: n_lora_kv = 512
|
||||
llm_load_print_meta: n_ff_exp = 1408
|
||||
llm_load_print_meta: n_expert_shared = 2
|
||||
llm_load_print_meta: expert_weights_scale = 1.0
|
||||
llm_load_print_meta: rope_yarn_log_mul = 0.0707
|
||||
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 = 0.35 MiB
|
||||
llm_load_tensors: offloading 9 repeating layers to GPU
|
||||
llm_load_tensors: offloaded 9/28 layers to GPU
|
||||
llm_load_tensors: CPU buffer size = 39836.32 MiB
|
||||
llm_load_tensors: CUDA0 buffer size = 20079.16 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 = 0.025
|
||||
llama_kv_cache_init: CUDA_Host KV buffer size = 90.00 MiB
|
||||
llama_kv_cache_init: CUDA0 KV buffer size = 45.00 MiB
|
||||
llama_new_context_with_model: KV self size = 135.00 MiB, K (f16): 81.00 MiB, V (f16): 54.00 MiB
|
||||
llama_new_context_with_model: CUDA_Host output buffer size = 0.39 MiB
|
||||
llama_new_context_with_model: CUDA0 compute buffer size = 1012.00 MiB
|
||||
llama_new_context_with_model: CUDA_Host compute buffer size = 11.01 MiB
|
||||
llama_new_context_with_model: graph nodes = 1924
|
||||
llama_new_context_with_model: graph splits = 288
|
||||
|
||||
system_info: n_threads = 25 / 32 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | 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 190.191 ms
|
||||
compute_imatrix: computing over 139 chunks with batch_size 512
|
||||
compute_imatrix: 34.56 seconds per pass - ETA 1 hours 20.05 minutes
|
||||
[1]6.2603,[2]4.2559,[3]4.2127,[4]4.7761,[5]4.5767,[6]4.3695,[7]4.6843,[8]4.7775,[9]5.3341,
|
||||
save_imatrix: stored collected data after 10 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[10]5.5364,[11]5.0894,[12]5.3454,[13]5.7265,[14]6.0547,[15]6.1866,[16]6.5001,[17]6.7199,[18]6.8140,[19]7.0880,
|
||||
save_imatrix: stored collected data after 20 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[20]6.7173,[21]6.6857,[22]6.6704,[23]6.7736,[24]6.5974,[25]6.7703,[26]6.6414,[27]6.7620,[28]6.6177,[29]6.8103,
|
||||
save_imatrix: stored collected data after 30 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[30]6.9909,[31]7.0526,[32]6.9375,[33]6.7018,[34]6.3698,[35]6.0946,[36]5.9819,[37]5.8802,[38]5.8355,[39]5.7438,
|
||||
save_imatrix: stored collected data after 40 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[40]5.6738,[41]5.5508,[42]5.4551,[43]5.5012,[44]5.5681,[45]5.6692,[46]5.6761,[47]5.8850,[48]6.0827,[49]6.2164,
|
||||
save_imatrix: stored collected data after 50 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[50]6.3397,[51]6.4074,[52]6.3073,[53]6.4070,[54]6.4883,[55]6.5826,[56]6.4898,[57]6.4658,[58]6.4771,[59]6.5720,
|
||||
save_imatrix: stored collected data after 60 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[60]6.6571,[61]6.7510,[62]6.7832,[63]6.7929,[64]6.8095,[65]6.8090,[66]6.7834,[67]6.7655,[68]6.7364,[69]6.7711,
|
||||
save_imatrix: stored collected data after 70 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[70]6.7805,[71]6.7797,[72]6.8014,[73]6.7961,[74]6.7804,[75]6.7637,[76]6.7513,[77]6.7458,[78]6.7567,[79]6.7167,
|
||||
save_imatrix: stored collected data after 80 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[80]6.7398,[81]6.7282,[82]6.7182,[83]6.6923,[84]6.6760,[85]6.6566,[86]6.6329,[87]6.6121,[88]6.6344,[89]6.6562,
|
||||
save_imatrix: stored collected data after 90 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[90]6.6532,[91]6.6857,[92]6.7165,[93]6.6615,[94]6.6489,[95]6.6257,[96]6.6471,[97]6.6566,[98]6.6490,[99]6.5802,
|
||||
save_imatrix: stored collected data after 100 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[100]6.5077,[101]6.4362,[102]6.3653,[103]6.2953,[104]6.2303,[105]6.1670,[106]6.1073,[107]6.0471,[108]6.0034,[109]6.0147,
|
||||
save_imatrix: stored collected data after 110 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[110]6.0502,[111]6.1111,[112]6.1705,[113]6.2131,[114]6.3040,[115]6.3445,[116]6.3729,[117]6.3757,[118]6.4049,[119]6.3903,
|
||||
save_imatrix: stored collected data after 120 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[120]6.3742,[121]6.3021,[122]6.2349,[123]6.2705,[124]6.3141,[125]6.3077,[126]6.3098,[127]6.3189,[128]6.3448,[129]6.3466,
|
||||
save_imatrix: stored collected data after 130 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
[130]6.3555,[131]6.3755,[132]6.3570,[133]6.3367,[134]6.3710,[135]6.4155,[136]6.4502,[137]6.4921,[138]6.5475,[139]6.5773,
|
||||
save_imatrix: stored collected data after 139 chunks in DeepSeek-Coder-V2-Lite-Base-IMat-GGUF/imatrix.dat
|
||||
|
||||
llama_print_timings: load time = 52295.72 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 = 549194.65 ms / 71168 tokens ( 7.72 ms per token, 129.59 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 = 571412.12 ms / 71169 tokens
|
||||
|
||||
Final estimate: PPL = 6.5773 +/- 0.08523
|
||||
Reference in New Issue
Block a user