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
NeuralMaxime-7B-slerp
NeuralMaxime-7B-slerp is a merge of the following models using LazyMergekit:
🧩 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"])