初始化项目，由ModelHub XC社区提供模型
Model: legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF Source: Original Platform
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ModelHub XC
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---
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base_model: deepseek-ai/DeepSeek-V2-Lite-Chat
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inference: false
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library_name: gguf
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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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---
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# DeepSeek-V2-Lite-Chat-IMat-GGUF
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_Llama.cpp imatrix quantization of deepseek-ai/DeepSeek-V2-Lite-Chat_
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Original Model: [deepseek-ai/DeepSeek-V2-Lite-Chat](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat)
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Original dtype: `BF16` (`bfloat16`)
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Quantized by: llama.cpp fork [PR 7519](https://github.com/ggerganov/llama.cpp/pull/7519)
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IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw)
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- [DeepSeek-V2-Lite-Chat-IMat-GGUF](#deepseek-v2-lite-chat-imat-gguf)
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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-V2-Lite-Chat-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-V2-Lite-Chat.Q8_0.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q8_0.gguf) | Q8_0 | 16.70GB | ✅ Available | ⚪ No | 📦 No
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| [DeepSeek-V2-Lite-Chat.Q6_K.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q6_K.gguf) | Q6_K | 14.07GB | ✅ Available | ⚪ No | 📦 No
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| [DeepSeek-V2-Lite-Chat.Q4_K.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q4_K.gguf) | Q4_K | 10.36GB | ✅ Available | 🟢 Yes | 📦 No
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| [DeepSeek-V2-Lite-Chat.Q3_K.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q3_K.gguf) | Q3_K | 8.13GB | ✅ Available | 🟢 Yes | 📦 No
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| [DeepSeek-V2-Lite-Chat.Q2_K.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q2_K.gguf) | Q2_K | 6.43GB | ✅ Available | 🟢 Yes | 📦 No
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### All Quants
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| Filename | Quant type | File Size | Status | Uses IMatrix | Is Split |
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| -------- | ---------- | --------- | ------ | ------------ | -------- |
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| [DeepSeek-V2-Lite-Chat.FP16.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.FP16.gguf) | F16 | 31.42GB | ✅ Available | ⚪ No | 📦 No
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| [DeepSeek-V2-Lite-Chat.BF16.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.BF16.gguf) | BF16 | 31.42GB | ✅ Available | ⚪ No | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.Q5_K.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q5_K.gguf) | Q5_K | 11.85GB | ✅ Available | ⚪ No | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.Q5_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q5_K_S.gguf) | Q5_K_S | 11.14GB | ✅ Available | ⚪ No | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.Q4_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q4_K_S.gguf) | Q4_K_S | 9.53GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.Q3_K_L.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q3_K_L.gguf) | Q3_K_L | 8.46GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.Q3_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q3_K_S.gguf) | Q3_K_S | 7.49GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.Q2_K_S.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.Q2_K_S.gguf) | Q2_K_S | 6.46GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ4_NL.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ4_NL.gguf) | IQ4_NL | 8.91GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ4_XS.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ4_XS.gguf) | IQ4_XS | 8.57GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ3_M.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ3_M.gguf) | IQ3_M | 7.55GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ3_S.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ3_S.gguf) | IQ3_S | 7.49GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ3_XS.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ3_XS.gguf) | IQ3_XS | 7.12GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ3_XXS.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ3_XXS.gguf) | IQ3_XXS | 6.96GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ2_M.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ2_M.gguf) | IQ2_M | 6.33GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ2_S.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ2_S.gguf) | IQ2_S | 6.01GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ2_XS.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ2_XS.gguf) | IQ2_XS | 5.97GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ2_XXS.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ2_XXS.gguf) | IQ2_XXS | 5.64GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ1_M.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ1_M.gguf) | IQ1_M | 5.24GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
| [DeepSeek-V2-Lite-Chat.IQ1_S.gguf](https://huggingface.co/legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF/blob/main/DeepSeek-V2-Lite-Chat.IQ1_S.gguf) | IQ1_S | 4.99GB | ✅ Available | 🟢 Yes | 📦 No
|
||||
|
||||
|
||||
## Downloading using huggingface-cli
|
||||
First, make sure you have hugginface-cli installed:
|
||||
```
|
||||
pip install -U "huggingface_hub[cli]"
|
||||
```
|
||||
Then, you can target the specific file you want:
|
||||
```
|
||||
huggingface-cli download legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF --include "DeepSeek-V2-Lite-Chat.Q8_0.gguf" --local-dir ./
|
||||
```
|
||||
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
|
||||
```
|
||||
huggingface-cli download legraphista/DeepSeek-V2-Lite-Chat-IMat-GGUF --include "DeepSeek-V2-Lite-Chat.Q8_0/*" --local-dir DeepSeek-V2-Lite-Chat.Q8_0
|
||||
# see FAQ for merging GGUF's
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Inference
|
||||
|
||||
### Simple chat template
|
||||
```
|
||||
<｜begin▁of▁sentence｜>User: {user_message_1}
|
||||
|
||||
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
|
||||
|
||||
Assistant:
|
||||
```
|
||||
|
||||
### Chat template with system prompt
|
||||
```
|
||||
<｜begin▁of▁sentence｜>{system_message}
|
||||
|
||||
User: {user_message_1}
|
||||
|
||||
Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
|
||||
|
||||
Assistant:
|
||||
```
|
||||
|
||||
### Llama.cpp
|
||||
```
|
||||
llama.cpp/main -m DeepSeek-V2-Lite-Chat.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-V2-Lite-Chat.Q8_0`)
|
||||
3. Run `gguf-split --merge DeepSeek-V2-Lite-Chat.Q8_0/DeepSeek-V2-Lite-Chat.Q8_0-00001-of-XXXXX.gguf DeepSeek-V2-Lite-Chat.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:dec567a6f54be9458da709983e4d06a32f12221de121c6b9c1e56c8eea727bb1
|
||||
size 38356432
|
||||
3142
imatrix.dataset
Normal file
3142
imatrix.dataset
Normal file
File diff suppressed because one or more lines are too long
165
imatrix.log
Normal file
165
imatrix.log
Normal file
@@ -0,0 +1,165 @@
|
||||
warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored
|
||||
warning: see main README.md for information on enabling GPU BLAS support
|
||||
main: build = 3004 (bb9c3618)
|
||||
main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
|
||||
main: seed = 1716723554
|
||||
llama_model_loader: loaded meta data with 38 key-value pairs and 377 tensors from DeepSeek-V2-Lite-Chat-IMat-GGUF/DeepSeek-V2-Lite-Chat.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-V2-Lite-Chat
|
||||
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 definition check successful ( 2400/102400 ).
|
||||
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_yarn_orig_ctx = 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-V2-Lite-Chat
|
||||
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_tensors: ggml ctx size = 0.18 MiB
|
||||
llm_load_tensors: CPU buffer size = 59915.48 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: CPU KV buffer size = 135.00 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: CPU output buffer size = 0.39 MiB
|
||||
llama_new_context_with_model: CPU compute buffer size = 367.76 MiB
|
||||
llama_new_context_with_model: graph nodes = 1924
|
||||
llama_new_context_with_model: graph splits = 1
|
||||
|
||||
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 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
|
||||
compute_imatrix: tokenizing the input ..
|
||||
compute_imatrix: tokenization took 290.224 ms
|
||||
compute_imatrix: computing over 214 chunks with batch_size 512
|
||||
compute_imatrix: 3.74 seconds per pass - ETA 13.33 minutes
|
||||
[1]8.2768,[2]5.4737,[3]5.4391,[4]6.2128,[5]5.9929,[6]5.7320,[7]6.1111,[8]6.2020,[9]6.9063,
|
||||
save_imatrix: stored collected data after 10 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[10]7.1247,[11]7.2969,[12]7.6469,[13]7.1879,[14]7.5155,[15]7.6139,[16]7.9793,[17]8.1919,[18]8.4695,[19]8.5381,
|
||||
save_imatrix: stored collected data after 20 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[20]8.9097,[21]8.5797,[22]8.6558,[23]8.0642,[24]7.6486,[25]7.3866,[26]7.1277,[27]7.4222,[28]7.3693,[29]7.5654,
|
||||
save_imatrix: stored collected data after 30 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[30]7.4697,[31]7.5927,[32]7.2141,[33]7.0326,[34]6.9585,[35]6.9555,[36]6.9146,[37]6.8700,[38]6.9146,[39]7.0737,
|
||||
save_imatrix: stored collected data after 40 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[40]7.1851,[41]7.3125,[42]7.3394,[43]7.5495,[44]7.7104,[45]7.8862,[46]7.9809,[47]8.0805,[48]8.0834,[49]8.1130,
|
||||
save_imatrix: stored collected data after 50 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[50]7.9198,[51]8.0131,[52]8.0931,[53]8.1593,[54]8.2404,[55]8.2924,[56]8.3474,[57]8.4669,[58]8.4857,[59]8.4941,
|
||||
save_imatrix: stored collected data after 60 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[60]8.5300,[61]8.5434,[62]8.6402,[63]8.6901,[64]8.7586,[65]8.8041,[66]8.7296,[67]8.6748,[68]8.6409,[69]8.6341,
|
||||
save_imatrix: stored collected data after 70 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[70]8.6099,[71]8.5414,[72]8.4441,[73]8.4169,[74]8.3939,[75]8.3706,[76]8.4325,[77]8.4689,[78]8.4645,[79]8.3915,
|
||||
save_imatrix: stored collected data after 80 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[80]8.3912,[81]8.2811,[82]8.2327,[83]8.1841,[84]8.1584,[85]8.1328,[86]8.1233,[87]8.1040,[88]8.0841,[89]8.0420,
|
||||
save_imatrix: stored collected data after 90 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[90]8.0628,[91]8.0975,[92]8.1182,[93]8.1254,[94]8.0728,[95]8.0515,[96]8.0575,[97]8.0787,[98]8.0673,[99]8.0877,
|
||||
save_imatrix: stored collected data after 100 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[100]8.1236,[101]8.0989,[102]8.1019,[103]8.1148,[104]8.1103,[105]8.0952,[106]8.0690,[107]8.0914,[108]8.0672,[109]8.0498,
|
||||
save_imatrix: stored collected data after 110 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[110]8.0253,[111]8.0585,[112]8.0983,[113]8.0915,[114]8.0882,[115]8.0723,[116]8.1090,[117]8.0438,[118]8.0238,[119]7.9827,
|
||||
save_imatrix: stored collected data after 120 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[120]7.9136,[121]7.9178,[122]7.8371,[123]7.7582,[124]7.6814,[125]7.6017,[126]7.5301,[127]7.4569,[128]7.3836,[129]7.3148,
|
||||
save_imatrix: stored collected data after 130 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[130]7.3209,[131]7.3164,[132]7.2912,[133]7.2601,[134]7.2430,[135]7.2101,[136]7.1949,[137]7.1702,[138]7.1602,[139]7.1316,
|
||||
save_imatrix: stored collected data after 140 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[140]7.1220,[141]7.0939,[142]7.0929,[143]7.0717,[144]7.0836,[145]7.1291,[146]7.2135,[147]7.2831,[148]7.3252,[149]7.3546,
|
||||
save_imatrix: stored collected data after 150 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[150]7.3515,[151]7.3579,[152]7.3738,[153]7.3494,[154]7.3624,[155]7.3616,[156]7.3830,[157]7.3775,[158]7.3696,[159]7.3718,
|
||||
save_imatrix: stored collected data after 160 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[160]7.3719,[161]7.3653,[162]7.3560,[163]7.3624,[164]7.3382,[165]7.3665,[166]7.3864,[167]7.4096,[168]7.4063,[169]7.4368,
|
||||
save_imatrix: stored collected data after 170 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[170]7.4473,[171]7.4306,[172]7.4294,[173]7.4347,[174]7.4371,[175]7.4414,[176]7.4471,[177]7.4279,[178]7.4707,[179]7.5203,
|
||||
save_imatrix: stored collected data after 180 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[180]7.5633,[181]7.6337,[182]7.6936,[183]7.7097,[184]7.7229,[185]7.6975,[186]7.7126,[187]7.7328,[188]7.7450,[189]7.7400,
|
||||
save_imatrix: stored collected data after 190 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[190]7.7441,[191]7.7533,[192]7.7629,[193]7.7630,[194]7.7572,[195]7.7748,[196]7.7876,[197]7.8267,[198]7.8181,[199]7.8260,
|
||||
save_imatrix: stored collected data after 200 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[200]7.8176,[201]7.8249,[202]7.8238,[203]7.8531,[204]7.8300,[205]7.8388,[206]7.8234,[207]7.8745,[208]7.9260,[209]7.9768,
|
||||
save_imatrix: stored collected data after 210 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
[210]8.0105,[211]8.0434,[212]8.0049,[213]7.9776,[214]7.9422,
|
||||
save_imatrix: stored collected data after 214 chunks in DeepSeek-V2-Lite-Chat-IMat-GGUF/imatrix.dat
|
||||
|
||||
llama_print_timings: load time = 5606.62 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 = 792874.73 ms / 109568 tokens ( 7.24 ms per token, 138.19 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 = 796095.09 ms / 109569 tokens
|
||||
|
||||
Final estimate: PPL = 7.9422 +/- 0.09818
|
||||
Reference in New Issue
Block a user