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mlabonne/Meta-Llama-3-12B-Instruct
Meta-Llama-3-12B-Instruct is a text generation model from mlabonne. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as other.
Meta-Llama-3-12B-Instruct is a merge of the following models using LazyMergekit: NousResearch/Meta-Llama-3-8B-Instruct NousResearch/Meta-Llama-3-8B-Instruct
Downloads · 30 days
34
2% of all-time downloads
All-time downloads
2.2K
Public
Parameters
11.5B
23 GB on disk
Likes
4
Public
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.safetensors23 GB · 100%
From the Hugging Face model README
Meta-Llama-3-12B-Instruct is a merge of the following models using LazyMergekit:
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|---|---|---|---|---|
| Meta-Llama-3-12B-Instruct | 41.7 | 67.71 | 52.75 | 40.58 | 50.69 |
| Meta-Llama-3-12B | 29.46 | 68.01 | 41.02 | 35.57 | 43.52 |
slices:
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [0,9]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [5,14]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [10,19]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [15,24]
- sources:
- model: NousResearch/Meta-Llama-3-8B-Instruct
layer_range: [20,32]
merge_method: passthrough
dtype: bfloat16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mlabonne/Meta-Llama-3-12B-Instruct"
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"])