Instructions to use hflog/Locutusque-OpenCerebrum-2.0-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hflog/Locutusque-OpenCerebrum-2.0-7B with Transformers:

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

pipe = pipeline("text-generation", model="hflog/Locutusque-OpenCerebrum-2.0-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("hflog/Locutusque-OpenCerebrum-2.0-7B")
model = AutoModelForMultimodalLM.from_pretrained("hflog/Locutusque-OpenCerebrum-2.0-7B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hflog/Locutusque-OpenCerebrum-2.0-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hflog/Locutusque-OpenCerebrum-2.0-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hflog/Locutusque-OpenCerebrum-2.0-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hflog/Locutusque-OpenCerebrum-2.0-7B

SGLang

How to use hflog/Locutusque-OpenCerebrum-2.0-7B 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 "hflog/Locutusque-OpenCerebrum-2.0-7B" \
    --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": "hflog/Locutusque-OpenCerebrum-2.0-7B",
		"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 "hflog/Locutusque-OpenCerebrum-2.0-7B" \
        --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": "hflog/Locutusque-OpenCerebrum-2.0-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hflog/Locutusque-OpenCerebrum-2.0-7B with Docker Model Runner:
```
docker model run hf.co/hflog/Locutusque-OpenCerebrum-2.0-7B
```

OpenCerebrum-2.0-7B

OpenCerebrum-2.0-7B is an open-source language model fine-tuned from the alpindale/Mistral-7B-v0.2-hf base model on a diverse dataset aimed at replicating capabilities of Aether Research's proprietary Cerebrum model.

The model was fine-tuned with SFT and DPO on approximately 7,000 examples across 15 data sources spanning coding, math, science, multi-turn conversation, RAG, reasoning, and general instruction-following. The goal was to assemble public datasets that could help the model achieve strong performance on benchmarks where Cerebrum excels.

Model Details

Base Model: alpindale/Mistral-7B-v0.2-hf
Parameters: 7 billion
Fine-Tuning Dataset Size: ~7,000 examples
Fine-Tuning Data: Advanced in-house curation techniques at Cognitive Computations, with 15 different data sources for DPO and SFT.
Language: English
License: Apache 2.0

Intended Use

OpenCerebrum-2.0-7B is intended to be a powerful open-source model for coding, math, science, and general question-answering and text generation tasks. Its diverse fine-tuning data aims to equip it with broad knowledge and reasoning capabilities.

However, as an open-source replica trained on a subset of data compared to the original Cerebrum, it may not match Cerebrum's full performance. Additionally, biases and limitations of the fine-tuning data may be reflected in the model's outputs.

Limitations and Biases

The model may have biases and limitations inherited from its fine-tuning datasets. Thorough testing is needed to characterize these.
As the model is based on a 7B parameter model, it has computational and memory constraints compared to larger models.

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16