Instructions to use moxin-org/Moxin-7B-LLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moxin-org/Moxin-7B-LLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="moxin-org/Moxin-7B-LLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("moxin-org/Moxin-7B-LLM")
model = AutoModelForCausalLM.from_pretrained("moxin-org/Moxin-7B-LLM")

llama-cpp-python

How to use moxin-org/Moxin-7B-LLM with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="moxin-org/Moxin-7B-LLM",
	filename="moxin-7B.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use moxin-org/Moxin-7B-LLM with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf moxin-org/Moxin-7B-LLM
# Run inference directly in the terminal:
llama-cli -hf moxin-org/Moxin-7B-LLM

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf moxin-org/Moxin-7B-LLM
# Run inference directly in the terminal:
llama-cli -hf moxin-org/Moxin-7B-LLM

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 moxin-org/Moxin-7B-LLM
# Run inference directly in the terminal:
./llama-cli -hf moxin-org/Moxin-7B-LLM

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 moxin-org/Moxin-7B-LLM
# Run inference directly in the terminal:
./build/bin/llama-cli -hf moxin-org/Moxin-7B-LLM

Use Docker

docker model run hf.co/moxin-org/Moxin-7B-LLM

LM Studio
Jan

vLLM

How to use moxin-org/Moxin-7B-LLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moxin-org/Moxin-7B-LLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moxin-org/Moxin-7B-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/moxin-org/Moxin-7B-LLM

SGLang

How to use moxin-org/Moxin-7B-LLM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "moxin-org/Moxin-7B-LLM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moxin-org/Moxin-7B-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "moxin-org/Moxin-7B-LLM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moxin-org/Moxin-7B-LLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use moxin-org/Moxin-7B-LLM with Ollama:
```
ollama run hf.co/moxin-org/Moxin-7B-LLM
```

Unsloth Studio new

How to use moxin-org/Moxin-7B-LLM 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 moxin-org/Moxin-7B-LLM 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 moxin-org/Moxin-7B-LLM to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for moxin-org/Moxin-7B-LLM to start chatting

Docker Model Runner
How to use moxin-org/Moxin-7B-LLM with Docker Model Runner:
```
docker model run hf.co/moxin-org/Moxin-7B-LLM
```

Lemonade

How to use moxin-org/Moxin-7B-LLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull moxin-org/Moxin-7B-LLM

Run and chat with the model

lemonade run user.Moxin-7B-LLM-{{QUANT_TAG}}

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Moxin 7B LLM

Model

You can download our base 7B model from this link and our chat 7B model from this link.

Inference

You can use the following code to run inference with the model. The model is saved under './model/' directory. Change the model directory accordingly or use the Huggingface link.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

torch.backends.cuda.enable_mem_efficient_sdp(False)
torch.backends.cuda.enable_flash_sdp(False)


model_name = 'moxin-org/Moxin-7B-LLM'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype=torch.bfloat16,
        device_map="auto",
        trust_remote_code=True,
    )

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer = tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "Can you explain the concept of regularization in machine learning?"

sequences = pipe(
    prompt,
    do_sample=True,
    max_new_tokens=1000,
    temperature=0.7,
    top_k=50,
    top_p=0.95,
    num_return_sequences=1,
)
print(sequences[0]['generated_text'])

Evaluation

We test the performance of our model with lm-evaluation-harness. The evaluation results on common datasets are shown below. We test on AI2 Reasoning Challenge (25-shot), HellaSwag (10-shot), MMLU (5-shot), and Winogrande (5-shot). We release the Moxin-7B-finetuned as our base model. We further finetune our base model on Tulu v2 to obtain our chat model.

Models	ARC-C	Hellaswag	MMLU	WinoGrade	Ave
Mistral-7B	57.59	83.25	62.42	78.77	70.51
LLaMA 3.1-8B	54.61	81.95	65.16	77.35	69.77
LLaMA 3-8B	55.46	82.09	65.29	77.82	70.17
LLaMA 2-7B	49.74	78.94	45.89	74.27	62.21
Qwen 2-7B	57.68	80.76	70.42	77.43	71.57
gemma-7b	56.48	82.31	63.02	78.3	70.03
internlm2.5-7b	54.78	79.7	68.17	80.9	70.89
Baichuan2-7B	47.87	73.89	54.13	70.8	61.67
Yi-1.5-9B	58.36	80.36	69.54	77.53	71.48
Moxin-7B-original	53.75	75.46	59.43	70.32	64.74
Moxin-7B-finetuned	59.47	83.08	60.97	78.69	70.55

We also test the zero shot performance on AI2 Reasoning Challenge (0-shot), AI2 Reasoning Easy (0-shot), HellaSwag (0-shot), PIQA (0-shot) and Winogrande (0-shot). The results are shown below.

Models	HellaSwag	WinoGrade	PIQA	ARC-E	ARC-C	Ave
Mistral-7B	80.39	73.4	82.15	78.28	52.22	73.29
LLaMA 2-7B	75.99	69.06	79.11	74.54	46.42	69.02
LLaMA 2-13B	79.37	72.22	80.52	77.4	49.06	71.71
LLaMA 3.1-8B	78.92	74.19	81.12	81.06	53.67	73.79
gemma-7b	80.45	73.72	80.9	79.97	54.1	73.83
Qwen v2-7B	78.9	72.38	79.98	74.71	50.09	71.21
internlm2.5-7b	79.14	77.9	80.52	76.16	51.37	73.02
Baichuan2-7B	72.25	67.17	77.26	72.98	42.15	66.36
Yi-1.5-9B	77.86	73.01	80.74	79.04	55.03	73.14
deepseek-7b	76.13	69.77	79.76	71.04	44.8	68.3
Moxin-7B-original	72.06	66.31	78.07	71.47	48.15	67.21
Moxin-7B-finetune	80.03	75.17	82.24	81.12	58.64	75.44

Citation

@article{zhao2024fully,
  title={Fully Open Source Moxin-7B Technical Report},
  author={Zhao, Pu and Shen, Xuan and Kong, Zhenglun and Shen, Yixin and Chang, Sung-En and Rupprecht, Timothy and Lu, Lei and Nan, Enfu and Yang, Changdi and He, Yumei and others},
  journal={arXiv preprint arXiv:2412.06845},
  year={2024}
}