Instructions to use chargoddard/servile-harpsichord-cdpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chargoddard/servile-harpsichord-cdpo with Transformers:

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

pipe = pipeline("text-generation", model="chargoddard/servile-harpsichord-cdpo")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chargoddard/servile-harpsichord-cdpo")
model = AutoModelForCausalLM.from_pretrained("chargoddard/servile-harpsichord-cdpo", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chargoddard/servile-harpsichord-cdpo with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/chargoddard/servile-harpsichord-cdpo

SGLang

How to use chargoddard/servile-harpsichord-cdpo 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 "chargoddard/servile-harpsichord-cdpo" \
    --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": "chargoddard/servile-harpsichord-cdpo",
		"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 "chargoddard/servile-harpsichord-cdpo" \
        --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": "chargoddard/servile-harpsichord-cdpo",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chargoddard/servile-harpsichord-cdpo with Docker Model Runner:
```
docker model run hf.co/chargoddard/servile-harpsichord-cdpo
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Trained on a different random sampling of the same datasets used by loyal-piano-m7, then with cDPO on a blend of RLHF datasets.

Several intermediate checkpoints (of cDPO training) are on branches.

Uses the Alpaca prompt format.

Downloads last month: 77

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for chargoddard/servile-harpsichord-cdpo

Merges

1 model

Quantizations

1 model

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