Exllamav2 quant (exl2 / 8.0 bpw) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
Quant | Model Size | lm_head |
---|---|---|
Meta-Llama-3-12B-Instruct
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
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Meta-Llama-3-8B-Instruct
- NousResearch/Meta-Llama-3-8B-Instruct
π Evaluation
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 |
𧩠Configuration
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
π» Usage
!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"])
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Model tree for Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2
Base model
NousResearch/Meta-Llama-3-8B-Instruct
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