Instructions to use Azazelle/Calliope-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azazelle/Calliope-7b with Transformers:

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

pipe = pipeline("text-generation", model="Azazelle/Calliope-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azazelle/Calliope-7b")
model = AutoModelForCausalLM.from_pretrained("Azazelle/Calliope-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Azazelle/Calliope-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Azazelle/Calliope-7b

SGLang

How to use Azazelle/Calliope-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 "Azazelle/Calliope-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": "Azazelle/Calliope-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 "Azazelle/Calliope-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": "Azazelle/Calliope-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Azazelle/Calliope-7b with Docker Model Runner:
```
docker model run hf.co/Azazelle/Calliope-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Calliope-7b

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the rescaled_sample merge method using mistralai/Mistral-7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Nexusflow/Starling-LM-7B-beta
    parameters:
      weight: 0.5
      density: 0.5
  - model: Eric111/openchat-3.5-0106-128k-DPO_dpo-binarized-NeuralTrix-7B
    parameters:
      weight: 0.3
      density: 0.3
  - model: paulml/OmniBeagleSquaredMBX-v3-7B
    parameters:
      weight: 0.2
      density: 0.2
  - model: chargoddard/servile-harpsichord-cdpo
    parameters:
      weight: 0.3
      density: 0.3
merge_method: rescaled_sample
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16

Downloads last month: 7

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Azazelle/Calliope-7b

Eric111/openchat-3.5-0106-128k-DPO_dpo-binarized-NeuralTrix-7B

Nexusflow/Starling-LM-7B-beta

chargoddard/servile-harpsichord-cdpo

mistralai/Mistral-7B-v0.1

paulml/OmniBeagleSquaredMBX-v3-7B

Merge model

this model

Quantizations

2 models