Meta-Llama-3-8b-Configurable-Lexi-Uninstruct-function-calling-json-mode-Task-Arithmetic-v0.0A

Meta-Llama-3-8b-Configurable-Lexi-Uninstruct-function-calling-json-mode-Task-Arithmetic-v0.0A

Meta-Llama-3-8b-Configurable-Lexi-Uninstruct-function-calling-json-mode-Task-Arithmetic-v0.0A is a merge of the following models using LazyMergekit:

• hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode
• Orenguteng/Lexi-Llama-3-8B-Uncensored
• NousResearch/Meta-Llama-3-8B
• vicgalle/Configurable-Llama-3-8B-v0.3
• NousResearch/Meta-Llama-3-8B-Instruct

🧩 Configuration

slices:
  - sources:
      - model: hiieu/Meta-Llama-3-8B-Instruct-function-calling-json-mode
        parameters:
          weight: 1
        layer_range: [0, 32]
      - model: Orenguteng/Lexi-Llama-3-8B-Uncensored
        parameters:
          weight: 0.9
        layer_range: [0, 32]
      - model: NousResearch/Meta-Llama-3-8B
        parameters:
          weight: 0.6
        layer_range: [0, 32]
      - model: vicgalle/Configurable-Llama-3-8B-v0.3
        parameters:
          weight: 0.8
        layer_range: [0, 32]
      - model: NousResearch/Meta-Llama-3-8B-Instruct
        parameters:
          weight: 0.7
        layer_range: [0, 32]
merge_method: task_arithmetic
base_model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Nhoodie/Meta-Llama-3-8b-Configurable-Lexi-Uninstruct-function-calling-json-mode-Task-Arithmetic-v0.0A"
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"])