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Xenon1_-_MetaModel_moex8-gguf
RichardErkhov/Xenon1_-_MetaModel_moex8-gguf
Xenon1_-_MetaModel_moex8-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf793 GB · 100%
At a glance
- Access
- Public
- Created
- Aug 31, 2024
- Updated
- Sep 1, 2024
- SHA
- bb48a78e
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
MetaModel_moex8 - GGUF
- Model creator: https://huggingface.co/Xenon1/
- Original model: https://huggingface.co/Xenon1/MetaModel_moex8/
| Name | Quant method | Size |
|---|---|---|
| MetaModel_moex8.Q2_K.gguf | Q2_K | 24.11GB |
| MetaModel_moex8.IQ3_XS.gguf | IQ3_XS | 26.95GB |
| MetaModel_moex8.IQ3_S.gguf | IQ3_S | 28.46GB |
| MetaModel_moex8.Q3_K_S.gguf | Q3_K_S | 28.46GB |
| MetaModel_moex8.IQ3_M.gguf | IQ3_M | 29.86GB |
| MetaModel_moex8.Q3_K.gguf | Q3_K | 31.42GB |
| MetaModel_moex8.Q3_K_M.gguf | Q3_K_M | 31.42GB |
| MetaModel_moex8.Q3_K_L.gguf | Q3_K_L | 33.69GB |
| MetaModel_moex8.IQ4_XS.gguf | IQ4_XS | 19.56GB |
| MetaModel_moex8.Q4_0.gguf | Q4_0 | 36.85GB |
| MetaModel_moex8.IQ4_NL.gguf | IQ4_NL | 37.28GB |
| MetaModel_moex8.Q4_K_S.gguf | Q4_K_S | 37.28GB |
| MetaModel_moex8.Q4_K.gguf | Q4_K | 11.76GB |
| MetaModel_moex8.Q4_K_M.gguf | Q4_K_M | 6.62GB |
| MetaModel_moex8.Q4_1.gguf | Q4_1 | 32.46GB |
| MetaModel_moex8.Q5_0.gguf | Q5_0 | 44.93GB |
| MetaModel_moex8.Q5_K_S.gguf | Q5_K_S | 44.93GB |
| MetaModel_moex8.Q5_K.gguf | Q5_K | 46.33GB |
| MetaModel_moex8.Q5_K_M.gguf | Q5_K_M | 46.33GB |
| MetaModel_moex8.Q5_1.gguf | Q5_1 | 48.97GB |
| MetaModel_moex8.Q6_K.gguf | Q6_K | 53.51GB |
| MetaModel_moex8.Q8_0.gguf | Q8_0 | 69.19GB |
Original model description:
license: apache-2.0 tags:
- moe
- mergekit
- merge
- chinese
- arabic
- english
- multilingual
- german
- french
- gagan3012/MetaModel
- jeonsworld/CarbonVillain-en-10.7B-v2
- jeonsworld/CarbonVillain-en-10.7B-v4
- TomGrc/FusionNet_linear
- DopeorNope/SOLARC-M-10.7B
- VAGOsolutions/SauerkrautLM-SOLAR-Instruct
- upstage/SOLAR-10.7B-Instruct-v1.0
- fblgit/UNA-SOLAR-10.7B-Instruct-v1.0
MetaModel_moex8
This model is a Mixure of Experts (MoE) made with mergekit (mixtral branch). It uses the following base models:
- gagan3012/MetaModel
- jeonsworld/CarbonVillain-en-10.7B-v2
- jeonsworld/CarbonVillain-en-10.7B-v4
- TomGrc/FusionNet_linear
- DopeorNope/SOLARC-M-10.7B
- VAGOsolutions/SauerkrautLM-SOLAR-Instruct
- upstage/SOLAR-10.7B-Instruct-v1.0
- fblgit/UNA-SOLAR-10.7B-Instruct-v1.0
🧩 Configuration
dtype: bfloat16
experts:
- positive_prompts:
- ''
source_model: gagan3012/MetaModel
- positive_prompts:
- ''
source_model: jeonsworld/CarbonVillain-en-10.7B-v2
- positive_prompts:
- ''
source_model: jeonsworld/CarbonVillain-en-10.7B-v4
- positive_prompts:
- ''
source_model: TomGrc/FusionNet_linear
- positive_prompts:
- ''
source_model: DopeorNope/SOLARC-M-10.7B
- positive_prompts:
- ''
source_model: VAGOsolutions/SauerkrautLM-SOLAR-Instruct
- positive_prompts:
- ''
source_model: upstage/SOLAR-10.7B-Instruct-v1.0
- positive_prompts:
- ''
source_model: fblgit/UNA-SOLAR-10.7B-Instruct-v1.0
gate_mode: hidden
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "gagan3012/MetaModel_moex8"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
- Created
- Aug 31, 2024
- Updated
- Sep 1, 2024