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RichardErkhov/
bertin-project_-_Gromenauer-7B-gguf

Name: bertin-project_-_Gromenauer-7B-gguf
Availability: InStock

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Gromenauer-7B - GGUF

Model creator: https://huggingface.co/bertin-project/
Original model: https://huggingface.co/bertin-project/Gromenauer-7B/

Name	Quant method	Size
Gromenauer-7B.Q2_K.gguf	Q2_K	2.53GB
Gromenauer-7B.IQ3_XS.gguf	IQ3_XS	2.81GB
Gromenauer-7B.IQ3_S.gguf	IQ3_S	2.96GB
Gromenauer-7B.Q3_K_S.gguf	Q3_K_S	2.95GB
Gromenauer-7B.IQ3_M.gguf	IQ3_M	3.06GB
Gromenauer-7B.Q3_K.gguf	Q3_K	3.28GB
Gromenauer-7B.Q3_K_M.gguf	Q3_K_M	3.28GB
Gromenauer-7B.Q3_K_L.gguf	Q3_K_L	3.56GB
Gromenauer-7B.IQ4_XS.gguf	IQ4_XS	3.67GB
Gromenauer-7B.Q4_0.gguf	Q4_0	3.83GB
Gromenauer-7B.IQ4_NL.gguf	IQ4_NL	3.87GB
Gromenauer-7B.Q4_K_S.gguf	Q4_K_S	3.86GB
Gromenauer-7B.Q4_K.gguf	Q4_K	4.07GB
Gromenauer-7B.Q4_K_M.gguf	Q4_K_M	4.07GB
Gromenauer-7B.Q4_1.gguf	Q4_1	4.24GB
Gromenauer-7B.Q5_0.gguf	Q5_0	4.65GB
Gromenauer-7B.Q5_K_S.gguf	Q5_K_S	4.65GB
Gromenauer-7B.Q5_K.gguf	Q5_K	4.78GB
Gromenauer-7B.Q5_K_M.gguf	Q5_K_M	4.78GB
Gromenauer-7B.Q5_1.gguf	Q5_1	5.07GB
Gromenauer-7B.Q6_K.gguf	Q6_K	5.53GB
Gromenauer-7B.Q8_0.gguf	Q8_0	7.17GB

Original model description:

license: apache-2.0 datasets:

fistro/gromenauer language:
es pipeline_tag: text-generation

Gromenauer-7B

gromenauer-7B logo

Overview

Gromenauer-7B is a Spanish language model designed to understand and generate high-quality Spanish text. Developed using the robust Mistral architecture, this model has been trained on an extensive literary corpus, ensuring it captures a wide range of linguistic nuances, styles, and contexts found in Spanish literature.

Model Details

Model Type: Mistral
Sequence Length: 8192
Hidden Dimension: 4096
Intermediate Dimension: 14336
Number of Layers: 32
Number of Attention Heads: 32
Number of Key-Value Heads: 8
Activation Function: SiLU
Initializer Range: 0.02
Layer Norm Epsilon: 1.0e-05
Use Flash Attention: Yes
Gradient Checkpointing: Enabled (Block Size: 5)
Sliding Window Attention: 4096
Use Bias: No

Training Details

Tokenizer: mistralai/Mistral-7B-v0.1
Batch Size: 512
Learning Rate: 1e-5
Optimizer: Adam with beta1=0.9, beta2=0.95, epsilon=1e-8
Weight Decay: 0.1
Warmup Steps: 200
Learning Rate Schedule: Cosine
Number of Training Steps: 7000

Usage

To load the model in your project, you can use the following code:

python

from transformers import AutoModel, AutoTokenizer

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("bertin-project/Gromenauer-7B")

# Load the model
model = AutoModel.from_pretrained("bertin-project/Gromenauer-7B")

# Example usage
text = "Introduce aquí tu texto en español."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

RichardErkhov/bertin-project_-_Gromenauer-7B-gguf