NeuralLLaMa-3-8b-DT-v0.1
NeuralLLaMa-3-8b-DT-v0.1 is a merge of the following models using LazyMergekit:
🧩 Configuration
models:
- model: NousResearch/Meta-Llama-3-8B
# No parameters necessary for base model
- model: mlabonne/ChimeraLlama-3-8B-v2
parameters:
density: 0.33
weight: 0.2
- model: nbeerbower/llama-3-stella-8B
parameters:
density: 0.44
weight: 0.4
- model: uygarkurt/llama-3-merged-linear
parameters:
density: 0.55
weight: 0.4
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: float16
🗨️ Chats
💻 Usage
!pip install -qU transformers accelerate bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, BitsAndBytesConfig
import torch
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
MODEL_NAME = 'Kukedlc/NeuralLLaMa-3-8b-DT-v0.1'
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:0', quantization_config=bnb_config)
prompt_system = "You are an advanced language model that speaks Spanish fluently, clearly, and precisely.\
You are called Roberto the Robot and you are an aspiring post-modern artist."
prompt = "Create a piece of art that represents how you see yourself, Roberto, as an advanced LLm, with ASCII art, mixing diagrams, engineering and let yourself go."
chat = [
{"role": "system", "content": f"{prompt_system}"},
{"role": "user", "content": f"{prompt}"},
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(chat, return_tensors="pt").to('cuda')
streamer = TextStreamer(tokenizer)
stop_token = "<|eot_id|>"
stop = tokenizer.encode(stop_token)[0]
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=1024, do_sample=True, temperature=0.7, repetition_penalty=1.2, top_p=0.9, eos_token_id=stop)
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 21.12 |
| IFEval (0-Shot) | 43.71 |
| BBH (3-Shot) | 28.01 |
| MATH Lvl 5 (4-Shot) | 7.25 |
| GPQA (0-shot) | 7.05 |
| MuSR (0-shot) | 9.69 |
| MMLU-PRO (5-shot) | 31.02 |