# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AxeronAI/axeron-mf-32")
model = AutoModelForCausalLM.from_pretrained("AxeronAI/axeron-mf-32", device_map="auto")Quick Links
axeron-32
This is a forge of pre-trained language models created using forgelm.
Forge Details
Forge Method
This model was forged using the SLERP forge method.
Models Forged
The following models were included in the forge:
Configuration
The following YAML configuration was used to produce this model:
base_model: meta-llama/Llama-3.1-8B
dtype: bfloat16
forge_method: slerp
modules:
default:
slices:
- sources:
- layer_range: [0, 32]
model: meta-llama/Llama-3.1-8B
- layer_range: [0, 32]
model: meta-llama/Llama-3.1-8B-Instruct
parameters:
t: 0.5
tokenizer:
source: base
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Base model
meta-llama/Llama-3.1-8B
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AxeronAI/axeron-mf-32")