Instructions to use Technoculture/MT7Bi-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Technoculture/MT7Bi-dpo with Transformers:

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

pipe = pipeline("text-generation", model="Technoculture/MT7Bi-dpo")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Technoculture/MT7Bi-dpo")
model = AutoModelForCausalLM.from_pretrained("Technoculture/MT7Bi-dpo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Technoculture/MT7Bi-dpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Technoculture/MT7Bi-dpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Technoculture/MT7Bi-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Technoculture/MT7Bi-dpo

SGLang

How to use Technoculture/MT7Bi-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 "Technoculture/MT7Bi-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": "Technoculture/MT7Bi-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 "Technoculture/MT7Bi-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": "Technoculture/MT7Bi-dpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Technoculture/MT7Bi-dpo with Docker Model Runner:
```
docker model run hf.co/Technoculture/MT7Bi-dpo
```

MT7Bi-dpo

Technoculture/MT7Bi-sft (base) + Technoculture/MT7Bi-alpha-dpo-v0.2 (adapter)

Open LLM Leaderboard

Model Name	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
Orca-2-7b	78.4	76.1	53.7	52.4	74.2	47.2
LLAMA-2-7b	43.2	77.1	44.4	38.7	69.5	16
MT7Bi-sft	54.1	75.11	-	43.08	72.14	15.54
MT7bi-dpo	54.69	75.89	52.82	45.48	71.58	25.93

Downloads last month: 16

Safetensors

Model size

7B params

Tensor type

F32

Model tree for Technoculture/MT7Bi-dpo

Base model

Technoculture/MT7Bi-sft

Finetuned

(1)

this model