Instructions to use MaziyarPanahi/Calme-12B-Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/Calme-12B-Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/Calme-12B-Instruct-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Calme-12B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Calme-12B-Instruct-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MaziyarPanahi/Calme-12B-Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/Calme-12B-Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/Calme-12B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/Calme-12B-Instruct-v0.1

SGLang

How to use MaziyarPanahi/Calme-12B-Instruct-v0.1 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 "MaziyarPanahi/Calme-12B-Instruct-v0.1" \
    --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": "MaziyarPanahi/Calme-12B-Instruct-v0.1",
		"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 "MaziyarPanahi/Calme-12B-Instruct-v0.1" \
        --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": "MaziyarPanahi/Calme-12B-Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MaziyarPanahi/Calme-12B-Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/Calme-12B-Instruct-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MaziyarPanahi/Calme-12B-Instruct-v0.1

Model Description

Calme-12B is a state-of-the-art language model with 12 billion parameters, merged and fine-tuned over high-quality datasets on top of Calme-7B-Instruct-v0.9. The Calme-7B models excel in generating text that resonates with clarity, calmness, and coherence.

How to Use

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

pipe = pipeline("text-generation", model="MaziyarPanahi/Calme-12B-Instruct-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Calme-12B-Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Calme-12B-Instruct-v0.1")

Quantized Models

I love how GGUF democratizes the use of Large Language Models (LLMs) on commodity hardware, more specifically, personal computers without any accelerated hardware. Because of this, I am committed to converting and quantizing any models I fine-tune to make them accessible to everyone!

GGUF (2/3/4/5/6/8 bits): MaziyarPanahi/Calme-12B-Instruct-v0.1-GGUF

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Safetensors

Model size

12B params

Tensor type

F16

Model tree for MaziyarPanahi/Calme-12B-Instruct-v0.1

Quantizations

2 models

Collection including MaziyarPanahi/Calme-12B-Instruct-v0.1

🇫🇷 Calme-7B

Collection

Calme fine-tuned models • 19 items • Updated about 13 hours ago • 7