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RichardErkhov/
Isotonic_-_TinyQwex-4x620M-MoE-gguf

Name: Isotonic_-_TinyQwex-4x620M-MoE-gguf
Availability: InStock

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TinyQwex-4x620M-MoE - GGUF

Model creator: https://huggingface.co/Isotonic/
Original model: https://huggingface.co/Isotonic/TinyQwex-4x620M-MoE/

Name	Quant method	Size
TinyQwex-4x620M-MoE.Q2_K.gguf	Q2_K	0.49GB
TinyQwex-4x620M-MoE.IQ3_XS.gguf	IQ3_XS	0.54GB
TinyQwex-4x620M-MoE.IQ3_S.gguf	IQ3_S	0.56GB
TinyQwex-4x620M-MoE.Q3_K_S.gguf	Q3_K_S	0.56GB
TinyQwex-4x620M-MoE.IQ3_M.gguf	IQ3_M	0.57GB
TinyQwex-4x620M-MoE.Q3_K.gguf	Q3_K	0.6GB
TinyQwex-4x620M-MoE.Q3_K_M.gguf	Q3_K_M	0.6GB
TinyQwex-4x620M-MoE.Q3_K_L.gguf	Q3_K_L	0.64GB
TinyQwex-4x620M-MoE.IQ4_XS.gguf	IQ4_XS	0.67GB
TinyQwex-4x620M-MoE.Q4_0.gguf	Q4_0	0.69GB
TinyQwex-4x620M-MoE.IQ4_NL.gguf	IQ4_NL	0.7GB
TinyQwex-4x620M-MoE.Q4_K_S.gguf	Q4_K_S	0.7GB
TinyQwex-4x620M-MoE.Q4_K.gguf	Q4_K	0.73GB
TinyQwex-4x620M-MoE.Q4_K_M.gguf	Q4_K_M	0.73GB
TinyQwex-4x620M-MoE.Q4_1.gguf	Q4_1	0.76GB
TinyQwex-4x620M-MoE.Q5_0.gguf	Q5_0	0.82GB
TinyQwex-4x620M-MoE.Q5_K_S.gguf	Q5_K_S	0.82GB
TinyQwex-4x620M-MoE.Q5_K.gguf	Q5_K	0.84GB
TinyQwex-4x620M-MoE.Q5_K_M.gguf	Q5_K_M	0.84GB
TinyQwex-4x620M-MoE.Q5_1.gguf	Q5_1	0.88GB
TinyQwex-4x620M-MoE.Q6_K.gguf	Q6_K	0.96GB
TinyQwex-4x620M-MoE.Q8_0.gguf	Q8_0	1.24GB

Original model description:

license: apache-2.0 tags:

moe
merge
mergekit
lazymergekit
Qwen/Qwen1.5-0.5B

TinyQwex-4x620M-MoE

TinyQwex-4x620M-MoE is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

🌟 Buying me coffee is a direct way to show support for this project.

💻 Usage

python

!pip install -qU transformers bitsandbytes accelerate eniops

from transformers import AutoTokenizer
import transformers
import torch

model = "Isotonic/TinyQwex-4x620M-MoE"

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-0.5B")
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.bfloat16, "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"])

🧩 Configuration

yamlbase_model:

experts:
  - source_model: Qwen/Qwen1.5-0.5B
    positive_prompts:
    - "reasoning"

  - source_model: Qwen/Qwen1.5-0.5B
    positive_prompts:
    - "program"

  - source_model: Qwen/Qwen1.5-0.5B
    positive_prompts:
    - "storytelling"

  - source_model: Qwen/Qwen1.5-0.5B
    positive_prompts:
    - "Instruction following assistant"

RichardErkhov/Isotonic_-_TinyQwex-4x620M-MoE-gguf

Original model description:

TinyQwex-4x620M-MoE

💻 Usage

🧩 Configuration

RichardErkhov/
Isotonic_-_TinyQwex-4x620M-MoE-gguf