Instructions to use QuantFactory/internlm2-math-plus-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/internlm2-math-plus-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/internlm2-math-plus-7b-GGUF",
	filename="internlm2-math-plus-7b.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/internlm2-math-plus-7b-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/internlm2-math-plus-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/internlm2-math-plus-7b-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/internlm2-math-plus-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/internlm2-math-plus-7b-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/internlm2-math-plus-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/internlm2-math-plus-7b-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/internlm2-math-plus-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/internlm2-math-plus-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/internlm2-math-plus-7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/internlm2-math-plus-7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/internlm2-math-plus-7b-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/internlm2-math-plus-7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/internlm2-math-plus-7b-GGUF:Q4_K_M

Ollama
How to use QuantFactory/internlm2-math-plus-7b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/internlm2-math-plus-7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/internlm2-math-plus-7b-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/internlm2-math-plus-7b-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/internlm2-math-plus-7b-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/internlm2-math-plus-7b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/internlm2-math-plus-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/internlm2-math-plus-7b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/internlm2-math-plus-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/internlm2-math-plus-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.internlm2-math-plus-7b-GGUF-Q4_K_M

List all available models

lemonade list

InternLM-Math-Plus-GGUF

This is quantized version of internlm/internlm2-math-plus-7b created using llama.cpp

Model Description

News

[2024.05.24] We release updated version InternLM2-Math-Plus with 4 sizes and state-of-the-art performances including 1.8B, 7B, 20B, and 8x22B. We improve informal math reasoning performance (chain-of-thought and code-intepreter) and formal math reasoning performance (LEAN 4 translation and LEAN 4 theorem proving) significantly.
[2024.02.10] We add tech reports and citation reference.
[2024.01.31] We add MiniF2F results with evaluation codes!
[2024.01.29] We add checkpoints from ModelScope. Update results about majority voting and Code Intepreter. Tech report is on the way!
[2024.01.26] We add checkpoints from OpenXLab, which ease Chinese users to download!

Performance

Formal Math Reasoning

We evaluate the performance of InternLM2-Math-Plus on formal math reasoning benchmark MiniF2F-test. The evaluation setting is same as Llemma with LEAN 4.

Models	MiniF2F-test
ReProver	26.5
LLMStep	27.9
GPT-F	36.6
HTPS	41.0
Llemma-7B	26.2
Llemma-34B	25.8
InternLM2-Math-7B-Base	30.3
InternLM2-Math-20B-Base	29.5
InternLM2-Math-Plus-1.8B	38.9
InternLM2-Math-Plus-7B	43.4
InternLM2-Math-Plus-20B	42.6
InternLM2-Math-Plus-Mixtral8x22B	37.3

Informal Math Reasoning

We evaluate the performance of InternLM2-Math-Plus on informal math reasoning benchmark MATH and GSM8K. InternLM2-Math-Plus-1.8B outperforms MiniCPM-2B in the smallest size setting. InternLM2-Math-Plus-7B outperforms Deepseek-Math-7B-RL which is the state-of-the-art math reasoning open source model. InternLM2-Math-Plus-Mixtral8x22B achieves 68.5 on MATH (with Python) and 91.8 on GSM8K.

Model	MATH	MATH-Python	GSM8K
MiniCPM-2B	10.2	-	53.8
InternLM2-Math-Plus-1.8B	37.0	41.5	58.8
InternLM2-Math-7B	34.6	50.9	78.1
Deepseek-Math-7B-RL	51.7	58.8	88.2
InternLM2-Math-Plus-7B	53.0	59.7	85.8
InternLM2-Math-20B	37.7	54.3	82.6
InternLM2-Math-Plus-20B	53.8	61.8	87.7
Mixtral8x22B-Instruct-v0.1	41.8	-	78.6
Eurux-8x22B-NCA	49.0	-	-
InternLM2-Math-Plus-Mixtral8x22B	58.1	68.5	91.8

We also evaluate models on MathBench-A. InternLM2-Math-Plus-Mixtral8x22B has comparable performance compared to Claude 3 Opus.

Model	Arithmetic	Primary	Middle	High	College	Average
GPT-4o-0513	77.7	87.7	76.3	59.0	54.0	70.9
Claude 3 Opus	85.7	85.0	58.0	42.7	43.7	63.0
Qwen-Max-0428	72.3	86.3	65.0	45.0	27.3	59.2
Qwen-1.5-110B	70.3	82.3	64.0	47.3	28.0	58.4
Deepseek-V2	82.7	89.3	59.0	39.3	29.3	59.9
Llama-3-70B-Instruct	70.3	86.0	53.0	38.7	34.7	56.5
InternLM2-Math-Plus-Mixtral8x22B	77.5	82.0	63.6	50.3	36.8	62.0
InternLM2-Math-20B	58.7	70.0	43.7	24.7	12.7	42.0
InternLM2-Math-Plus-20B	65.8	79.7	59.5	47.6	24.8	55.5
Llama3-8B-Instruct	54.7	71.0	25.0	19.0	14.0	36.7
InternLM2-Math-7B	53.7	67.0	41.3	18.3	8.0	37.7
Deepseek-Math-7B-RL	68.0	83.3	44.3	33.0	23.0	50.3
InternLM2-Math-Plus-7B	61.4	78.3	52.5	40.5	21.7	50.9
MiniCPM-2B	49.3	51.7	18.0	8.7	3.7	26.3
InternLM2-Math-Plus-1.8B	43.0	43.3	25.4	18.9	4.7	27.1

Downloads last month: 65

GGUF

Model size

8B params

Architecture

internlm2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/internlm2-math-plus-7b-GGUF

Base model

internlm/internlm2-math-plus-7b

Quantized

(6)

this model