metadata

base_model:
  - meta-llama/Meta-Llama-3-8B-Instruct
library_name: transformers
tags:
  - mergekit
  - merge
license: other
language:
  - en

Meta-Llama-3-13B-Instruct

Meta-Llama-3-13B-Instruct is a meta-llama/Meta-Llama-3-8B-Instruct self-merge made with MergeKit.

Configuration

The following YAML configuration was used to produce this model:

slices:
- sources:
  - layer_range: [0, 16]
    model: meta-llama/Meta-Llama-3-8B-Instruct
- sources:
  - layer_range: [4, 24]
    model: meta-llama/Meta-Llama-3-8B-Instruct
- sources:
  - layer_range: [8, 31]
    model: meta-llama/Meta-Llama-3-8B-Instruct
merge_method: passthrough
dtype: float16

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "andrijdavid/Meta-Llama-3-13B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

terminators = [
    tokenizer.eos_token_id,
    tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    eos_token_id=terminators,
    do_sample=True,
    temperature=0.6,
    top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))