MergedLlama-3-8B-MS-2
MergedLlama-3-8B-MS-2 is a merge of the following models using LazyMergekit:
- NousResearch/Meta-Llama-3-8B-Instruct
- Weyaxi/Einstein-v6.1-Llama3-8B
- cognitivecomputations/dolphin-2.9-llama3-8b
- nvidia/Llama3-ChatQA-1.5-8B
- Kukedlc/SmartLlama-3-8B-MS-v0.1
- mlabonne/ChimeraLlama-3-8B-v3
🧩 Configuration
models:
- model: NousResearch/Meta-Llama-3-8B
# No parameters necessary for base model
- model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
density: 0.6
weight: 2
- model: Weyaxi/Einstein-v6.1-Llama3-8B
parameters:
density: 0.55
weight: 2
- model: cognitivecomputations/dolphin-2.9-llama3-8b
parameters:
density: 0.55
weight: 2
- model: nvidia/Llama3-ChatQA-1.5-8B
parameters:
density: 0.55
weight: 2
- model: Kukedlc/SmartLlama-3-8B-MS-v0.1
parameters:
density: 0.66
weight: 1
- model: mlabonne/ChimeraLlama-3-8B-v3
parameters:
density: 0.66
weight: 1
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: float16
💻 Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/MergedLlama-3-8B-MS-2"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
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"])