Edit model card

test_tiny_mixtral

test_tiny_mixtral is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

🧩 Configuration

base_model: openaccess-ai-collective/tiny-mistral
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: openaccess-ai-collective/tiny-mistral
    positive_prompts:
      - "math"
    # You can add negative_prompts if needed
  - source_model: openaccess-ai-collective/tiny-mistral

    positive_prompts:
      - "science"
  - source_model: openaccess-ai-collective/tiny-mistral
    positive_prompts:
      - "writing"
    # You can add negative_prompts if needed
  - source_model: openaccess-ai-collective/tiny-mistral
    positive_prompts:
      - "general"

πŸ’» Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "JSpergel/test_tiny_mixtral"

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"])
Downloads last month
6
Safetensors
Model size
743M params
Tensor type
BF16
Β·
Inference API
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for JSpergel/test_tiny_mixtral

Finetuned
this model