README.md

metadata

license: apache-2.0
language:
  - en

NeuralMaxime-7B-slerp-GGUF

Description

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

Files Provided

Name	Quant	Bits	File Size	Remark
neuralmaxime-7b-slerp.IQ3_XXS.gguf	IQ3_XXS	3	3.02 GB	3.06 bpw quantization
neuralmaxime-7b-slerp.IQ3_S.gguf	IQ3_S	3	3.18 GB	3.44 bpw quantization
neuralmaxime-7b-slerp.IQ3_M.gguf	IQ3_M	3	3.28 GB	3.66 bpw quantization mix
neuralmaxime-7b-slerp.Q4_0.gguf	Q4_0	4	4.11 GB	3.56G, +0.2166 ppl
neuralmaxime-7b-slerp.IQ4_NL.gguf	IQ4_NL	4	4.16 GB	4.25 bpw non-linear quantization
neuralmaxime-7b-slerp.Q4_K_M.gguf	Q4_K_M	4	4.37 GB	3.80G, +0.0532 ppl
neuralmaxime-7b-slerp.Q5_K_M.gguf	Q5_K_M	5	5.13 GB	4.45G, +0.0122 ppl
neuralmaxime-7b-slerp.Q6_K.gguf	Q6_K	6	5.94 GB	5.15G, +0.0008 ppl
neuralmaxime-7b-slerp.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
Kukedlc/NeuralMaxime-7B-slerp	mistral	MistralForCausalLM	10000.0	4096	32768

Benchmarks

Original Model Card

---

tags: - merge - mergekit - lazymergekit - mlabonne/AlphaMonarch-7B - mlabonne/NeuralMonarch-7B base_model: - mlabonne/AlphaMonarch-7B - mlabonne/NeuralMonarch-7B license: apache-2.0

NeuralMaxime-7B-slerp

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

• mlabonne/AlphaMonarch-7B
• mlabonne/NeuralMonarch-7B

🧩 Configuration

slices:
  - sources:
      - model: mlabonne/AlphaMonarch-7B
        layer_range: [0, 32]
      - model: mlabonne/NeuralMonarch-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/AlphaMonarch-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Kukedlc/NeuralMaxime-7B-slerp"
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"])