metadata

license: mit
library_name: transformers
pipeline_tag: text-generation
tags:
  - code
  - deepseek
  - gguf
  - bf16
metrics:
  - accuracy
language:
  - en
  - zh

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.