mradermacher/ChunMengDie-1.0-0.4b-GGUF overview

---

base_model: XingChina/ChunMengDie-1.0-0.4b

datasets:

• BelleGroup/train_0.5M_CN
• liumindmind/NekoQA-10K
• cyberlangke/Nana-catgirl-dataset-110k
• XingChina/ChunMengDie-1.0-User-Data

language:

• zh
• en

library_name: transformers

license: bsd-3-clause

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

• chinese
• instruction-tuning
• conversational
• experimental
• safetensors
• gguf
• quantized
• 8bit
• 4bit
• llama.cpp

---

About

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static quants of https://huggingface.co/XingChina/ChunMengDie-1.0-0.4b

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For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's

READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for

more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |

|:-----|:-----|--------:|:------|

| GGUF | Q2_K | 0.2 | |

| GGUF | Q3_K_S | 0.2 | |

| GGUF | Q3_K_M | 0.3 | lower quality |

| GGUF | IQ4_XS | 0.3 | |

| GGUF | Q4_K_S | 0.3 | fast, recommended |

| GGUF | Q3_K_L | 0.3 | |

| GGUF | Q4_K_M | 0.3 | fast, recommended |

| GGUF | Q5_K_S | 0.3 | |

| GGUF | Q5_K_M | 0.3 | |

| GGUF | Q6_K | 0.3 | very good quality |

| GGUF | Q8_0 | 0.4 | fast, best quality |

| GGUF | f16 | 0.6 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.png

And here are Artefact2's thoughts on the matter:

https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to

questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting

me use its servers and providing upgrades to my workstation to enable

this work in my free time.

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