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AurelPx_-_Dare-k-7B-ties-gguf
RichardErkhov/AurelPx_-_Dare-k-7B-ties-gguf
AurelPx_-_Dare-k-7B-ties-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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.gguf95.2 GB · 100%
At a glance
- Access
- Public
- Created
- Sep 14, 2024
- Updated
- Sep 14, 2024
- SHA
- 68c2819c
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
Dare-k-7B-ties - GGUF
- Model creator: https://huggingface.co/AurelPx/
- Original model: https://huggingface.co/AurelPx/Dare-k-7B-ties/
| Name | Quant method | Size |
|---|---|---|
| Dare-k-7B-ties.Q2_K.gguf | Q2_K | 2.53GB |
| Dare-k-7B-ties.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Dare-k-7B-ties.IQ3_S.gguf | IQ3_S | 2.96GB |
| Dare-k-7B-ties.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Dare-k-7B-ties.IQ3_M.gguf | IQ3_M | 3.06GB |
| Dare-k-7B-ties.Q3_K.gguf | Q3_K | 3.28GB |
| Dare-k-7B-ties.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Dare-k-7B-ties.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Dare-k-7B-ties.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Dare-k-7B-ties.Q4_0.gguf | Q4_0 | 3.83GB |
| Dare-k-7B-ties.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Dare-k-7B-ties.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Dare-k-7B-ties.Q4_K.gguf | Q4_K | 4.07GB |
| Dare-k-7B-ties.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Dare-k-7B-ties.Q4_1.gguf | Q4_1 | 4.24GB |
| Dare-k-7B-ties.Q5_0.gguf | Q5_0 | 4.65GB |
| Dare-k-7B-ties.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Dare-k-7B-ties.Q5_K.gguf | Q5_K | 4.78GB |
| Dare-k-7B-ties.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Dare-k-7B-ties.Q5_1.gguf | Q5_1 | 5.07GB |
| Dare-k-7B-ties.Q6_K.gguf | Q6_K | 5.53GB |
| Dare-k-7B-ties.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
tags:
- merge
- mergekit
- lazymergekit
- samir-fama/SamirGPT-v1
- EmbeddedLLM/Mistral-7B-Merge-14-v0.2 base_model:
- samir-fama/SamirGPT-v1
- EmbeddedLLM/Mistral-7B-Merge-14-v0.2 license: apache-2.0
Dare-k-7B-ties
Dare-k-7B-ties is a merge of the following models using LazyMergekit:
🧩 Configuration
models:
- model: mistralai/Mistral-7B-v0.1
# No parameters necessary for base model
- model: samir-fama/SamirGPT-v1
parameters:
density: 0.53
weight: 0.5
- model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
parameters:
density: 0.53
weight: 0.5
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 = "AurelPx/Dare-k-7B-ties"
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"])
- Created
- Sep 14, 2024
- Updated
- Sep 14, 2024