deepseek-ai/deepseek-llm-7b-chat

📋 Overview

deepseek-ai/deepseek-llm-7b-chat is an advanced open-source language model developed by deepseek. With 7B parameters, this model offers excellent performance for a wide range of natural language processing tasks. It's designed to be accessible for both research and production use, with optimized inference capabilities.

🚀 Deployment Guide

Method 1: Using Ollama (Recommended)

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run the model
ollama run deepseek/ai/deepseek/llm/7b/chat

Method 2: Using HuggingFace Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("deepseek/ai/deepseek/llm/7b/chat")
tokenizer = AutoTokenizer.from_pretrained("deepseek/ai/deepseek/llm/7b/chat")

prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0]))

Method 3: Using vLLM (Production)

# Install vLLM
pip install vllm

# Start server
python -m vllm.entrypoints.openai.api_server \
    --model deepseek/ai/deepseek/llm/7b/chat \
    --host 0.0.0.0 \
    --port 8000

Method 4: Using Docker

docker run -d \
  -p 8080:8080 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  ghcr.io/huggingface/text-generation-inference:latest \
  --model-id deepseek/ai/deepseek/llm/7b/chat

💻 Code Examples

Python Example

import requests

response = requests.post("http://localhost:8000/v1/completions", json={
    "model": "deepseek/ai/deepseek/llm/7b/chat",
    "prompt": "Write a function to calculate fibonacci numbers",
    "max_tokens": 200
})

print(response.json()["choices"][0]["text"])

cURL Example

curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek/ai/deepseek/llm/7b/chat",
    "prompt": "Explain quantum computing",
    "max_tokens": 200
  }'

🎯 Use Cases

🖥️ Hardware Requirements

📊 Model Stats

🔗 Resources