Instructions to use prithivMLmods/Calme-Ties-78B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Calme-Ties-78B with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Calme-Ties-78B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Calme-Ties-78B")
model = AutoModelForMultimodalLM.from_pretrained("prithivMLmods/Calme-Ties-78B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Calme-Ties-78B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Calme-Ties-78B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Calme-Ties-78B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Calme-Ties-78B

SGLang

How to use prithivMLmods/Calme-Ties-78B 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 "prithivMLmods/Calme-Ties-78B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Calme-Ties-78B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "prithivMLmods/Calme-Ties-78B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Calme-Ties-78B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Calme-Ties-78B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Calme-Ties-78B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Calme-Ties-78B

Calme-Ties-78B is a 78-billion-parameter model merged using the TIES methodology, based on the Qwen2 architecture. It integrates two sub-base models: calme-3.2-instruct-78B by MaziyarPanahi and CalmeRys-78B-Orpo-v0.1 by dfurman, which serves as the base model. The merging process assigns equal weight and density to both models, with additional parameters enabling normalization and int8 masking. The model operates using the bfloat16 data type.

Model	Model Name	Model Link
Base Model	CalmeRys-78B-Orpo-v0.1	CalmeRys-78B-Orpo-v0.1
Model 1	calme-3.2-instruct-78B	calme-3.2-instruct-78B
Model 2	CalmeRys-78B-Orpo-v0.1	CalmeRys-78B-Orpo-v0.1

Merged Models

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

Merge Method

This model was merged using the TIES merge method using dfurman/CalmeRys-78B-Orpo-v0.1 as a base.

Models Merged

The following models were included in the merge:

MaziyarPanahi/calme-3.2-instruct-78b

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: MaziyarPanahi/calme-3.2-instruct-78b
    parameters:
      weight: 1
      density: 1
merge_method: ties
base_model: dfurman/CalmeRys-78B-Orpo-v0.1
parameters:
  weight: 1
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16