Instructions to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/deepseek-llm-7b-chat-GGUF",
	filename="deepseek-llm-7b-chat_Q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SandLogicTechnologies/deepseek-llm-7b-chat-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SandLogicTechnologies/deepseek-llm-7b-chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M

Ollama
How to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M
```

Unsloth Studio new

How to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/deepseek-llm-7b-chat-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/deepseek-llm-7b-chat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/deepseek-llm-7b-chat-GGUF to start chatting

Docker Model Runner
How to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/deepseek-llm-7b-chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/deepseek-llm-7b-chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.deepseek-llm-7b-chat-GGUF-Q4_K_M

List all available models

lemonade list

Quantized DeepSeek-LLM-7B-Chat (GGUF)

DeepSeek-LLM-7B-Chat is a 7-billion-parameter, instruction-tuned large language model designed for conversational AI, reasoning, and coding tasks. To enable efficient local deployment, the model is provided in GGUF quantized formats, where Q4_K_M and Q5_K_M quantization reduce numerical precision from full precision to 4-bit and 5-bit representations. This significantly lowers memory usage and improves inference speed on CPUs and consumer-grade GPUs, while largely preserving the model’s response quality, reasoning ability, and coding performance.

Model Overview

Model Name: DeepSeek-LLM-7B-Chat
Base Model: deepseek-ai/deepseek-llm-7b-chat
Architecture: Decoder-only Transformer
Quantized Versions:
- Q4_K_M (4-bit quantization)
- Q5_K_M (5-bit quantization)
Parameters: 7 Billion
Context Length: 4K tokens
Modalities: Text only
Developer: DeepSeek AI
License: deepseek

Quantization Details

Q4_K_M

Approx. ~70% size reduction
Lower memory footprint (~3.93 GB)
Optimized for low-resource environments
Faster inference on CPU
Minor degradation in complex reasoning tasks

Q5_K_M

Approx. ~65% size reduction
Better fidelity to the original FP16 model (~4.59 GB)
Improved coherence and reasoning
Recommended when VRAM allows

Training Details (Original Model)

DeepSeek-LLM-7B-Chat is a 7-billion-parameter, decoder-only transformer developed by DeepSeek AI and trained in multiple stages to support high-quality conversational, reasoning, and coding tasks.

Pretraining

Trained on a large-scale, high-quality corpus consisting of web text, programming code, mathematics, and technical content.
Uses autoregressive language modeling as the primary training objective.
Focuses on strong English-language understanding with significant exposure to code and STEM data.
Learns general language representations, reasoning patterns, and code structure.

Instruction Fine-Tuning

Fine-tuned on diverse instruction–response datasets to improve task-following behavior.
Covers a wide range of use cases, including:
- General question answering
- Coding and debugging
- Logical and mathematical reasoning
- Step-by-step explanations
Improves response clarity, usefulness, and alignment with user intent.

Key Features

-Instruction-tuned chat model : Trained to follow user instructions accurately and respond in a conversational, helpful manner.

-Multi-turn dialogue : Maintains context across multiple conversation turns for coherent and consistent interactions.

-Coding assistance : Helps write, explain, and debug code across common programming languages.

-Logical reasoning : Performs step-by-step reasoning to solve structured and analytical problems.

-Math and STEM explanations : Explains mathematical concepts and technical topics in a clear, structured way.

-Optimized for conversational alignment : Fine-tuned to produce safe, relevant, and context-aware chat responses.

-Efficient inference via GGUF format : Uses the GGUF format to enable fast, low-memory inference on CPUs and consumer GPUs.

Usage

llama.cpp

./llama-cli \
  -m SandLogicTechnologies/deepseek-llm-7b-chat_Q4_K_M.gguf \
  -p "Explain transformers in simple terms."

Recommended Use Cases

Local AI assistants Run a fully offline conversational assistant on your own machine without relying on cloud services.
Coding help and debugging Generate, explain, and debug code snippets across multiple programming languages in real time.
Research and reasoning tasks Assist with logical analysis, mathematical reasoning, and structured problem-solving for technical work.
Edge devices and CPU inference Efficiently deploy the model on CPUs or low-VRAM hardware such as laptops and edge devices.
Privacy-preserving offline chatbots Keep all conversations and data local, ensuring full privacy with no external data transmission.

Acknowledgments

These quantized models are based on the original work by Deepseek-AI development team.

Special thanks to:

The Deepseek-ai team for developing and releasing the deepseek-llm-7b-chat model.
Georgi Gerganov and the entire llama.cpp open-source community for enabling efficient model quantization and inference via the GGUF format.

Contact

For any inquiries or support, please contact us at [email protected] or visit our Website. ```

Downloads last month: 40

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

Model tree for SandLogicTechnologies/deepseek-llm-7b-chat-GGUF

Base model

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

Quantized

(16)

this model