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RichardErkhov/gate369_-_Blurdus-7b-v0.1-gguf
gate369_-_Blurdus-7b-v0.1-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.
Downloads · 30 days
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From the Hugging Face model README
Quantization made by Richard Erkhov.
Blurdus-7b-v0.1 - GGUF
| Name | Quant method | Size |
|---|---|---|
| Blurdus-7b-v0.1.Q2_K.gguf | Q2_K | 2.53GB |
| Blurdus-7b-v0.1.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| Blurdus-7b-v0.1.IQ3_S.gguf | IQ3_S | 2.96GB |
| Blurdus-7b-v0.1.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| Blurdus-7b-v0.1.IQ3_M.gguf | IQ3_M | 3.06GB |
| Blurdus-7b-v0.1.Q3_K.gguf | Q3_K | 3.28GB |
| Blurdus-7b-v0.1.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| Blurdus-7b-v0.1.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| Blurdus-7b-v0.1.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| Blurdus-7b-v0.1.Q4_0.gguf | Q4_0 | 3.83GB |
| Blurdus-7b-v0.1.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| Blurdus-7b-v0.1.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| Blurdus-7b-v0.1.Q4_K.gguf | Q4_K | 4.07GB |
| Blurdus-7b-v0.1.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| Blurdus-7b-v0.1.Q4_1.gguf | Q4_1 | 4.24GB |
| Blurdus-7b-v0.1.Q5_0.gguf | Q5_0 | 4.65GB |
| Blurdus-7b-v0.1.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| Blurdus-7b-v0.1.Q5_K.gguf | Q5_K | 4.78GB |
| Blurdus-7b-v0.1.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| Blurdus-7b-v0.1.Q5_1.gguf | Q5_1 | 5.07GB |
| Blurdus-7b-v0.1.Q6_K.gguf | Q6_K | 5.53GB |
| Blurdus-7b-v0.1.Q8_0.gguf | Q8_0 | 7.17GB |
license: apache-2.0 tags:
Blurdus-7b-v0.1 is a merge of the following models using LazyMergekit:
models:
- model: 222gate/Blurred-Beagle-7b-slerp
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: 222gate/BrurryDog-7b-v0.1
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: liminerity/Blur-7b-v1.21
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: udkai/Turdus
parameters:
normalize: true
int8_mask: true
dtype: float16
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "222gate/Blurdus-7b-v0.1"
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"])
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 74.35 |
| AI2 Reasoning Challenge (25-Shot) | 72.27 |
| HellaSwag (10-Shot) | 88.50 |
| MMLU (5-Shot) | 64.82 |
| TruthfulQA (0-shot) | 69.72 |
| Winogrande (5-shot) | 82.95 |
| GSM8k (5-shot) | 67.85 |