Meta-Llama-3-120B-Instruct

Meta-Llama-3-120B-Instruct is a self-merge with meta-llama/Meta-Llama-3-70B-Instruct.

It was inspired by large merges like:

🔍 Applications

I recommend using this model for creative writing. It uses the Llama 3 chat template with a default context window of 8K (can be extended with rope theta).

Check the examples in the evaluation section to get an idea of its performance.

⚡ Quantized models

Thanks to Eric Hartford, elinas, and the mlx-community for providing these models.

🏆 Evaluation

The model looks excellent for creating writing tasks, outperforming GPT-4. Thanks again to Eric Hartford for noticing this.

X thread by Eric Hartford (creative writing): https://twitter.com/erhartford/status/1787050962114207886
X thread by Daniel Kaiser (creative writing): https://twitter.com/spectate_or/status/1787257261309518101
X thread by Simon (reasoning): https://twitter.com/NewDigitalEdu/status/1787403266894020893
r/LocalLLaMa: https://www.reddit.com/r/LocalLLaMA/comments/1cl525q/goliath_lovers_where_is_the_feedback_about/

🧩 Configuration

slices:
- sources:
  - layer_range: [0, 20]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [10, 30]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [20, 40]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [30, 50]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [40, 60]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [50, 70]
    model: meta-llama/Meta-Llama-3-70B-Instruct
- sources:
  - layer_range: [60, 80]
    model: meta-llama/Meta-Llama-3-70B-Instruct
merge_method: passthrough
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Llama-3-120B"
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"])