How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="marcuscedricridia/Mixmix-LlaMAX3.2-1B-Merge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("marcuscedricridia/Mixmix-LlaMAX3.2-1B-Merge")
model = AutoModelForMultimodalLM.from_pretrained("marcuscedricridia/Mixmix-LlaMAX3.2-1B-Merge")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
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merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using unsloth/Llama-3.2-1B-Instruct as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: model_stock  
base_model: unsloth/Llama-3.2-1B-Instruct
models:  
  - model: NickyNicky/Llama-1B-base-GRPO-miniThinky_v1
  - model: enesarda22/Med-Llama-3.2-1B-DeepSeek67B-Distilled
  - model: keeeeenw/Llama-3.2-1B-Instruct-Open-R1-Distill
  - model: unsloth/Llama-3.2-1B-Instruct+Leon1309/Llama-3.2-1B-SFT-LoRA-All
  - model: unsloth/Llama-3.2-1B-Instruct+bunnycore/LLama-3.2-1B-General-lora_model
  - model: marcuscedricridia/Mixmix-LlaMAX3.2-1B
dtype: bfloat16  
tokenizer_source: base  
int8_mask: true  
normalize: true  
name: Mixmix-LlaMAX3.2-1B-Merge
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