Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
Paper • 2311.03099 • Published • 36
How to use amylynn/Prima-Llama-8B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="amylynn/Prima-Llama-8B") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("amylynn/Prima-Llama-8B")
model = AutoModelForCausalLM.from_pretrained("amylynn/Prima-Llama-8B", device_map="auto")How to use amylynn/Prima-Llama-8B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amylynn/Prima-Llama-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "amylynn/Prima-Llama-8B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/amylynn/Prima-Llama-8B
How to use amylynn/Prima-Llama-8B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "amylynn/Prima-Llama-8B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "amylynn/Prima-Llama-8B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "amylynn/Prima-Llama-8B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "amylynn/Prima-Llama-8B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use amylynn/Prima-Llama-8B with Docker Model Runner:
docker model run hf.co/amylynn/Prima-Llama-8B
This is a merge of pre-trained language models created using mergekit.
This model was merged using the DARE TIES merge method using meta-llama/Meta-Llama-3.1-8B as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
merge_method: dare_ties
base_model: meta-llama/Meta-Llama-3.1-8B
models:
# 1. Your main adult RP creative base (heavier weight to preserve erotic style & prose)
- model: DavidAU/L3.1-RP-Hero-Dirty_Harry-8B
parameters:
weight: 0.82
density: 0.75 # Keep most of the creative/erotic deltas
# 2. DeepSeek-R1 reasoning adapter (strong analytical boost)
- model: theo77186/Llama-3.2-8B-Instruct+amylynn/llama-3.1-8b-lora-deepseek-r1-distill-llama-8b
parameters:
weight: 0.58
density: 0.65
# 3. DeepHermes-3 adapter (judgment, thinking mode, coherence)
- model: SicariusSicariiStuff/Llama-3.3-8B-Instruct-128K_Abliterated+amylynn/llama-3.1-8b-lora-deephermes-3-llama-3-8b-preview
parameters:
weight: 0.55
density: 0.60
parameters:
normalize: true
int8_mask: true
dtype: bfloat16