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---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/ChimeraLlama-3-8B-v2
- nbeerbower/llama-3-stella-8B
- uygarkurt/llama-3-merged-linear
base_model:
- mlabonne/ChimeraLlama-3-8B-v2
- nbeerbower/llama-3-stella-8B
- uygarkurt/llama-3-merged-linear
license: other
---
# NeuralLLaMa-3-8b-DT-v0.1
NeuralLLaMa-3-8b-DT-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/ChimeraLlama-3-8B-v2](https://huggingface.co/mlabonne/ChimeraLlama-3-8B-v2)
* [nbeerbower/llama-3-stella-8B](https://huggingface.co/nbeerbower/llama-3-stella-8B)
* [uygarkurt/llama-3-merged-linear](https://huggingface.co/uygarkurt/llama-3-merged-linear)
## 🧩 Configuration
```yaml
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
```python
!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)
``` |