DeepSeek-V2-Chat-GGUF
Quantizised from https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat
Using llama.cpp b3026 for quantizisation. Given the rapid release of llama.cpp builds, this will likely change over time.
Please set the metadata KV overrides below.
Usage:
Downloading the bf16:
- Find the relevant directory
- Download all files
- Run merge.py
- Merged GGUF should appear
Downloading the quantizations:
- Find the relevant directory
- Download all files
- Point to the first split (most programs should load all the splits automatically now)
Running in llama.cpp:
To start in command line chat mode (chat completion):
main -m DeepSeek-V2-Chat.{quant}.gguf -c {context length} --color -c (-i)
To use llama.cpp's OpenAI compatible server:
server \
-m DeepSeek-V2-Chat.{quant}.gguf \
-c {context_length} \
(--color [recommended: colored output in supported terminals]) \
(-i [note: interactive mode]) \
(--mlock [note: avoid using swap]) \
(--verbose) \
(--log-disable [note: disable logging to file, may be useful for prod]) \
(--metrics [note: prometheus compatible monitoring endpoint]) \
(--api-key [string]) \
(--port [int]) \
(--flash-attn [note: must be fully offloaded to supported GPU])
Making an importance matrix:
imatrix \
-m DeepSeek-V2-Chat.{quant}.gguf \
-f groups_merged.txt \
--verbosity [0, 1, 2] \
-ngl {GPU offloading; must build with CUDA} \
--ofreq {recommended: 1}
Making a quant:
quantize \
DeepSeek-V2-Chat.bf16.gguf \
DeepSeek-V2-Chat.{quant}.gguf \
{quant} \
(--imatrix [file])
Note: Use iMatrix quants only if you can fully offload to GPU, otherwise speed will be affected negatively.
Quants:
Quant | Status | Size | Description | KV Metadata | Weighted | Notes |
---|---|---|---|---|---|---|
BF16 | Available | 439 GB | Lossless :) | Old | No | Q8_0 is sufficient for most cases |
Q8_0 | Available | 233.27 GB | High quality recommended | Updated | Yes | |
Q8_0 | Available | ~110 GB | High quality recommended | Updated | Yes | |
Q5_K_M | Available | 155 GB | Medium-high quality recommended | Updated | Yes | |
Q4_K_M | Available | 132 GB | Medium quality recommended | Old | No | |
Q3_K_M | Available | 104 GB | Medium-low quality | Updated | Yes | |
IQ3_XS | Available | 89.6 GB | Better than Q3_K_M | Old | Yes | |
Q2_K | Available | 80.0 GB | Low quality not recommended | Old | No | |
IQ2_XXS | Available | 61.5 GB | Lower quality not recommended | Old | Yes | |
IQ1_M | Uploading | 27.3 GB | Extremely low quality not recommended | Old | Yes | Testing purposes; use IQ2 at least |
Planned Quants (weighted/iMatrix):
Planned Quant | Notes |
---|---|
Q5_K_S | |
Q4_K_S | |
Q3_K_S | |
IQ4_XS | |
IQ2_XS | |
IQ2_S | |
IQ2_M |
Metadata KV overrides (pass them using --override-kv
, can be specified multiple times):
deepseek2.attention.q_lora_rank=int:1536
deepseek2.attention.kv_lora_rank=int:512
deepseek2.expert_shared_count=int:2
deepseek2.expert_feed_forward_length=int:1536
deepseek2.expert_weights_scale=float:16
deepseek2.leading_dense_block_count=int:1
deepseek2.rope.scaling.yarn_log_multiplier=float:0.0707
License:
- DeepSeek license for model weights, which can be found in the
LICENSE
file in the root of this repo - MIT license for any repo code
Performance:
~1.5t/s with Ryzen 3 3700x (96gb 3200mhz) [Q2_K]
iMatrix:
Find imatrix.dat
in the root of this repo, made with a Q2_K
quant containing 62 chunks (see here for info: https://github.com/ggerganov/llama.cpp/issues/5153#issuecomment-1913185693)
Using groups_merged.txt
, find it here: https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384
Censorship:
This model is a bit censored, finetuning on toxic DPO might help.
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