Description

This repo contains GGUF format model files for NeuralDarewin-7B.

Files Provided

Name	Quant	Bits	File Size	Remark
neuraldarewin-7b.IQ3_XXS.gguf	IQ3_XXS	3	3.02 GB	3.06 bpw quantization
neuraldarewin-7b.IQ3_S.gguf	IQ3_S	3	3.18 GB	3.44 bpw quantization
neuraldarewin-7b.IQ3_M.gguf	IQ3_M	3	3.28 GB	3.66 bpw quantization mix
neuraldarewin-7b.Q4_0.gguf	Q4_0	4	4.11 GB	3.56G, +0.2166 ppl
neuraldarewin-7b.IQ4_NL.gguf	IQ4_NL	4	4.16 GB	4.25 bpw non-linear quantization
neuraldarewin-7b.Q4_K_M.gguf	Q4_K_M	4	4.37 GB	3.80G, +0.0532 ppl
neuraldarewin-7b.Q5_K_M.gguf	Q5_K_M	5	5.13 GB	4.45G, +0.0122 ppl
neuraldarewin-7b.Q6_K.gguf	Q6_K	6	5.94 GB	5.15G, +0.0008 ppl
neuraldarewin-7b.Q8_0.gguf	Q8_0	8	7.70 GB	6.70G, +0.0004 ppl

Parameters

path	type	architecture	rope_theta	sliding_win	max_pos_embed
mlabonne/Darewin-7B	mistral	MistralForCausalLM	10000.0	4096	32768

Benchmarks

Original Model Card

Darewin-7B is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model
  - model: Intel/neural-chat-7b-v3-3
    parameters:
      density: 0.6
      weight: 0.2
  - model: openaccess-ai-collective/DPOpenHermes-7B-v2
    parameters:
      density: 0.6
      weight: 0.1
  - model: fblgit/una-cybertron-7b-v2-bf16
    parameters:
      density: 0.6
      weight: 0.2
  - model: openchat/openchat-3.5-0106
    parameters:
      density: 0.6
      weight: 0.15
  - model: OpenPipe/mistral-ft-optimized-1227
    parameters:
      density: 0.6
      weight: 0.25
  - model: mlabonne/NeuralHermes-2.5-Mistral-7B
    parameters:
      density: 0.6
      weight: 0.1
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/NeuralDarewin-7B"
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"])