Instructions to use Locutusque/OpenCerebrum-1.0-7b-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/OpenCerebrum-1.0-7b-DPO with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/OpenCerebrum-1.0-7b-DPO")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/OpenCerebrum-1.0-7b-DPO")
model = AutoModelForCausalLM.from_pretrained("Locutusque/OpenCerebrum-1.0-7b-DPO", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Locutusque/OpenCerebrum-1.0-7b-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/OpenCerebrum-1.0-7b-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/OpenCerebrum-1.0-7b-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/OpenCerebrum-1.0-7b-DPO

SGLang

How to use Locutusque/OpenCerebrum-1.0-7b-DPO 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 "Locutusque/OpenCerebrum-1.0-7b-DPO" \
    --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": "Locutusque/OpenCerebrum-1.0-7b-DPO",
		"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 "Locutusque/OpenCerebrum-1.0-7b-DPO" \
        --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": "Locutusque/OpenCerebrum-1.0-7b-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/OpenCerebrum-1.0-7b-DPO with Docker Model Runner:
```
docker model run hf.co/Locutusque/OpenCerebrum-1.0-7b-DPO
```

Add quant links

by bartowski - opened Mar 27, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-0

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README.md +5 -0

README.md CHANGED Viewed

@@ -30,6 +30,11 @@ The model was fine-tuned on approximately 21,000 examples across 6 datasets span
 - **Language:** English
 - **License:** Apache 2.0
 ## Intended Use
 OpenCerebrum-1.0-7B-DPO 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.

 - **Language:** English
 - **License:** Apache 2.0
+## Quants
+- **ExLlamaV2:** https://huggingface.co/bartowski/OpenCerebrum-1.0-7b-DPO-exl2
+- **GGUF:** https://huggingface.co/bartowski/OpenCerebrum-1.0-7b-DPO-GGUF
 ## Intended Use
 OpenCerebrum-1.0-7B-DPO 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.