ModelVerificadoExterno

Mistral 7B

This repository contains the complete configuration, tokenizer, and optimized model weights (model.safetensors) for the Mistral-7B architecture. It is ready for deployment, inference, and downstream fine-tuning tasks.

Fontehuggingface.co/mengleap-stnap/Mistral-7B/tree/fa1de00d50192f64c6feedab3ea016e59f45366a

TipoText modelModelo basemistralai/Mistral-7B-v0.1VersãomodelPublicador@sentinelapexCnota

Fonte do modelo

Tipo: Text model
Tamanho do arquivo: 7,01 GB
Modelo base: mistralai/Mistral-7B-v0.1
Versão: vmodel
Atualizado: 5 de out. de 2026
Publicador: @sentinelapex
Downloads: 53
Parâmetros: 7B
Fonte: Hugging Face

Descrição da fonte

Mistral-7B

This repository contains the complete configuration, tokenizer, and optimized model weights (model.safetensors) for the Mistral-7B architecture. It is ready for deployment, inference, and downstream fine-tuning tasks.

1. About Mistral-7B

Mistral-7B is a highly efficient 7-billion parameter language model engineered for high performance and low latency.

Key Features:

Sliding Window Attention (SWA): Handles longer sequences with a lower memory footprint ($8k$ context length).
Grouped-query Attention (GQA): Enables faster inference times and reduces cache size during generation.
Byte-fallback BPE Tokenizer: Ensures that unknown characters never break the text processing pipeline.

Repository Structure:

model.safetensors: The primary tensor weights ($\approx 7.34$ GB optimized format).
config.json & generation_config.json: Architecture settings and text generation parameters (temperature, top_p).
tokenizer.json & tokenizer_config.json: The vocabulary mapping and tokenization configurations.
chat_template.jinja: Built-in template for structuring conversational inputs.

2. Setup & Installation

Follow these steps to configure your environment and run the model locally.

Hardware Requirements:

GPU: Minimum 12GB VRAM (e.g., RTX 3060 12GB, RTX 4060 Ti 16GB, or T4/A10G on Cloud).
RAM: 16GB system memory minimum.

Step 1: Install Dependencies

Ensure you have Python installed, then run the following command to install the required libraries:

pip install transformers torch accelerate safetensors

Step 2: Python Implementation Script Create a python file (e.g., run_inference.py) and use the official Hugging Face transformers pipeline to run the model:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "mengleap-stnap/Mistral-7B"

print("Loading tokenizer and model...")
# Load Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load Model with FP16 precision for VRAM optimization
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Define your prompt
prompt = "Explain the concept of Artificial Intelligence in three simple sentences."

# Format input tokens
inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")

print("Generating response...")
# Generate tokens
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.7,
        do_sample=True,
        top_p=0.9,
        pad_token_id=tokenizer.eos_token_id
    )

# Decode and print output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("\n--- AI Response ---")
print(response)

‌3. Recommended Generation Parameters

For optimal results, use the following parameters during generation setup:

Parameter             Value                          Description
-------------------------------------------------------------------------------------------------
temperature            0.7               Balances creativity and factual consistency.
-------------------------------------------------------------------------------------------------
top_p	               0.9	             Filters out low-probability words for smoother sentences.
-------------------------------------------------------------------------------------------------
do_sample	           True	             Enables probabilistic sampling instead of greedy decoding.

Mistral-7B Throughput

Pic3

MMLU - KNOWLEDGE

Pic1

ACCURACY

Pic2

License

This model is distributed under the Apache-2.0 License. You are free to use, modify, and distribute it for both commercial and non-commercial applications.

Fontes

1 fonte

Hugging FacePrimárioVersão fa1de00d50192f64c6feedab3ea016e59f45366a

huggingface.co/mengleap-stnap/Mistral-7B/tree/fa1de00d50192f64c6feedab3ea016e59f45366a

Verificado 30 de ago.

Artefatos de modelo

1 artefato

model.safetensors

safetensors · 7,01 GB · SHA-256 1abe11ea9775…57f3 · Hugging Face

Download file

Trechos de fonte

3 trechos

Readme17 de jul.huggingface.co/mengleap-stnap/Mistral-7B

--- license: apache-2.0 language: - en base_model: mistralai/Mistral-7B-v0.1 tags: - mistral - text-generation - conversational --- # Mistral-7B This repository contains the complete configuration, tokenizer, and optimized model weights (`model.safetensors`) for the **Mistral-7B** architecture. It is ready for deployment, inference, and downstream fine-tuning tasks. --- ## 1. About Mistral-7B **Mistral-7B** is a highly efficient 7-billion parameter language model engineered for high performance and low latency. ### Key Features: * **Sliding Window Attention (SWA):** Handles longer sequences with a lower memory footprint ($8k$ context length). * **Grouped-query Attention (GQA):** Enables faster inference times and reduces cache size during generation. * **Byte-fallback BPE Tokenizer:** Ensures that unknown characters never break the text processing pipeline. ### Repository Structure: * `model.safetensors`: The primary tensor weights ($\approx 7.34$ GB optimized format). * `config.json` & `generation_config.json`: Architecture settings and text generation parameters (temperature, top_p). * `tokenizer.json` & `tokenizer_config.json`: The vocabulary mapping and tokenization configurations. * `chat_template.jinja`: Built-in template for structuring conversational inputs. --- ## 2. Setup & Installation Follow these steps to configure your environment and run the model locally. ### Hardware Requirements: * **GPU:** Minimum 12GB VRAM (e.g., RTX 3060 12GB, RTX 4060 Ti 16GB, or T4/A10G on Cloud). * **RAM:** 16GB system memory minimum. ### Step 1: Install Dependencies Ensure you have Python installed, then run the following command to install the required libraries: ```bash pip install transformers torch accelerate safetensors ``` Step 2: Python Implementation Script Create a python file (e.g., run_inference.py) and use the official Hugging Face transformers pipeline to run the model: ```bash from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "mengleap-stnap/Mistral-7B" print("Loading tokenizer and model...") # Load Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id) # Load Model with FP16 precision for VRAM optimization model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" ) # Define your prompt prompt = "Explain the concept of Artificial Intelligence in t...

Readme11 de jul.huggingface.co/mengleap-stnap/Mistral-7B

--- license: apache-2.0 language: - en base_model: mistralai/Mistral-7B-v0.1 tags: - mistral - text-generation - conversational --- # Mistral-7B This repository contains the complete configuration, tokenizer, and optimized model weights (`model.safetensors`) for the **Mistral-7B** architecture. It is ready for deployment, inference, and downstream fine-tuning tasks. --- ## 1. About Mistral-7B **Mistral-7B** is a highly efficient 7-billion parameter language model engineered for high performance and low latency. ### Key Features: * **Sliding Window Attention (SWA):** Handles longer sequences with a lower memory footprint ($8k$ context length). * **Grouped-query Attention (GQA):** Enables faster inference times and reduces cache size during generation. * **Byte-fallback BPE Tokenizer:** Ensures that unknown characters never break the text processing pipeline. ### Repository Structure: * `model.safetensors`: The primary tensor weights ($\approx 7.34$ GB optimized format). * `config.json` & `generation_config.json`: Architecture settings and text generation parameters (temperature, top_p). * `tokenizer.json` & `tokenizer_config.json`: The vocabulary mapping and tokenization configurations. * `chat_template.jinja`: Built-in template for structuring conversational inputs. --- ## 2. Setup & Installation Follow these steps to configure your environment and run the model locally. ### Hardware Requirements: * **GPU:** Minimum 12GB VRAM (e.g., RTX 3060 12GB, RTX 4060 Ti 16GB, or T4/A10G on Cloud). * **RAM:** 16GB system memory minimum. ### Step 1: Install Dependencies Ensure you have Python installed, then run the following command to install the required libraries: ```bash pip install transformers torch accelerate safetensors ``` Step 2: Python Implementation Script Create a python file (e.g., run_inference.py) and use the official Hugging Face transformers pipeline to run the model: ```bash from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "sentinelapex/Mistral-7B" print("Loading tokenizer and model...") # Load Tokenizer tokenizer = AutoTokenizer.from_pretrained(model_id) # Load Model with FP16 precision for VRAM optimization model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" ) # Define your prompt prompt = "Explain the concept of Artificial Intelligence in thr...

Note17 de jul.huggingface.co/mengleap-stnap/Mistral-7B

Source context: 134 downloads · 0 likes · Pipeline text-generation · Repo mengleap-stnap/Mistral-7B