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

Libraries

How to use QuantFactory/deepseek-math-7b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/deepseek-math-7b-instruct-GGUF",
	filename="deepseek-math-7b-instruct.Q2_K.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 QuantFactory/deepseek-math-7b-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/deepseek-math-7b-instruct-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 QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/deepseek-math-7b-instruct-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 QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/deepseek-math-7b-instruct-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 QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/deepseek-math-7b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/deepseek-math-7b-instruct-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": "QuantFactory/deepseek-math-7b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M

Ollama
How to use QuantFactory/deepseek-math-7b-instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/deepseek-math-7b-instruct-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 QuantFactory/deepseek-math-7b-instruct-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 QuantFactory/deepseek-math-7b-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use QuantFactory/deepseek-math-7b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/deepseek-math-7b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/deepseek-math-7b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.deepseek-math-7b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/deepseek-math-7b-instruct-GGUF

This is quanitzed version of deepseek-ai/deepseek-math-7b-instruct created using llama.cpp

Model Description

[🏠Homepage] | [🤖 Chat with DeepSeek LLM] | [Discord] | [Wechat(微信)]

Paper Link👁️

1. Introduction to DeepSeekMath

See the Introduction for more details.

2. How to Use

Here give some examples of how to use our model.

Chat Completion

❗❗❗ Please use chain-of-thought prompt to test DeepSeekMath-Instruct and DeepSeekMath-RL:

English questions: {question}\nPlease reason step by step, and put your final answer within \boxed{}.
Chinese questions: {question}\n请通过逐步推理来解答问题，并把最终答案放置于\boxed{}中。

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "deepseek-ai/deepseek-math-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id

messages = [
    {"role": "user", "content": "what is the integral of x^2 from 0 to 2?\nPlease reason step by step, and put your final answer within \\boxed{}."}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)

result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)

Avoiding the use of the provided function apply_chat_template, you can also interact with our model following the sample template. Note that messages should be replaced by your input.

User: {messages[0]['content']}

Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}

Assistant:

Note: By default (add_special_tokens=True), our tokenizer automatically adds a bos_token (<｜begin▁of▁sentence｜>) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.

3. License

This code repository is licensed under the MIT License. The use of DeepSeekMath models is subject to the Model License. DeepSeekMath supports commercial use.

See the LICENSE-MODEL for more details.

4. Contact

If you have any questions, please raise an issue or contact us at service@deepseek.com.

Downloads last month: 515

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/deepseek-math-7b-instruct-GGUF

Base model

deepseek-ai/deepseek-math-7b-instruct

Quantized

(19)

this model

Paper for QuantFactory/deepseek-math-7b-instruct-GGUF

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Paper • 2402.03300 • Published Feb 5, 2024 • 145