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vanillaOVO_-_merge_7B_state_1-gguf
RichardErkhov/vanillaOVO_-_merge_7B_state_1-gguf
vanillaOVO_-_merge_7B_state_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.
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At a glance
- Access
- Public
- Created
- Sep 21, 2024
- Updated
- Sep 21, 2024
- SHA
- c32cb371
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
merge_7B_state_1 - GGUF
- Model creator: https://huggingface.co/vanillaOVO/
- Original model: https://huggingface.co/vanillaOVO/merge_7B_state_1/
| Name | Quant method | Size |
|---|---|---|
| merge_7B_state_1.Q2_K.gguf | Q2_K | 2.53GB |
| merge_7B_state_1.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| merge_7B_state_1.IQ3_S.gguf | IQ3_S | 2.96GB |
| merge_7B_state_1.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| merge_7B_state_1.IQ3_M.gguf | IQ3_M | 3.06GB |
| merge_7B_state_1.Q3_K.gguf | Q3_K | 3.28GB |
| merge_7B_state_1.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| merge_7B_state_1.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| merge_7B_state_1.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| merge_7B_state_1.Q4_0.gguf | Q4_0 | 3.83GB |
| merge_7B_state_1.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| merge_7B_state_1.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| merge_7B_state_1.Q4_K.gguf | Q4_K | 4.07GB |
| merge_7B_state_1.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| merge_7B_state_1.Q4_1.gguf | Q4_1 | 4.24GB |
| merge_7B_state_1.Q5_0.gguf | Q5_0 | 4.65GB |
| merge_7B_state_1.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| merge_7B_state_1.Q5_K.gguf | Q5_K | 4.78GB |
| merge_7B_state_1.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| merge_7B_state_1.Q5_1.gguf | Q5_1 | 5.07GB |
| merge_7B_state_1.Q6_K.gguf | Q6_K | 5.53GB |
| merge_7B_state_1.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
base_model: [] tags:
- mergekit
- merge license: apache-2.0
This is a merge of pre-trained language models created based on DARE using mergekit.
More descriptions of the model will be added soon.
Loading the Model
Use the following Python code to load the model:
import torch
from transformers import MistralForCausalLM, AutoTokenizer
model = MistralForCausalLM.from_pretrained("vanillaOVO/merge_7B_state_1", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("vanillaOVO/merge_7B_state_1")
Generating Text
To generate text, use the following Python code:
text = "Large language models are "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
- Created
- Sep 21, 2024
- Updated
- Sep 21, 2024