README.md

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

# DeepSeek-V2-Chat-GGUF

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6604e5b21eb292d6df393365/j_LWkNdegeMjQXuAOFZ1N.jpeg)

Quantizised from [https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat](https://huggingface.co/deepseek-ai/DeepSeek-V2-Chat)

Using llama.cpp [b3026](https://github.com/ggerganov/llama.cpp/releases/tag/b3026) for quantizisation. Given the rapid release of llama.cpp builds, this will likely change over time.

**If you are using an older quant, 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      |       |
| Q5_K_M   | Uploading   | 155 GB    | Medium-low quality                         | 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            |         |
| Q6_K              |         |
| 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
```

Quants with "Updated" metadata contain these parameters, so as long as you're running a supported build of llama.cpp no `--override-kv` parameters are required.

A precompiled Windows AVX2 version is avaliable at `llama.cpp-039896407afd40e54321d47c5063c46a52da3e01.zip` in the root of this repo.

# 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](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](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)

# Censorship:

This model is a bit censored, finetuning on toxic DPO might help.