Instructions to use prithivMLmods/Calcium-Opus-14B-Elite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Calcium-Opus-14B-Elite with Transformers:

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

pipe = pipeline("text-generation", model="prithivMLmods/Calcium-Opus-14B-Elite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Calcium-Opus-14B-Elite")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Calcium-Opus-14B-Elite")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Calcium-Opus-14B-Elite with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Calcium-Opus-14B-Elite"
# 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/Calcium-Opus-14B-Elite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Calcium-Opus-14B-Elite

SGLang

How to use prithivMLmods/Calcium-Opus-14B-Elite 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/Calcium-Opus-14B-Elite" \
    --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/Calcium-Opus-14B-Elite",
		"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/Calcium-Opus-14B-Elite" \
        --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/Calcium-Opus-14B-Elite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Calcium-Opus-14B-Elite with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Calcium-Opus-14B-Elite
```

prithivMLmods commited on Feb 23, 2025

Commit

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@@ -215,19 +215,7 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 6. **Dependency on High-Quality Prompts**:
    Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.
-# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Calcium-Opus-14B-Elite-details)!
-Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-14B-Elite&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
-|      Metric       |Value (%)|
-|-------------------|--------:|
-|**Average**        |    38.25|
-|IFEval (0-Shot)    |    60.64|
-|BBH (3-Shot)       |    46.53|
-|MATH Lvl 5 (4-Shot)|    37.08|
-|GPQA (0-shot)      |    16.44|
-|MuSR (0-shot)      |    20.95|
-|MMLU-PRO (5-shot)  |    47.85|
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Calcium-Opus-14B-Elite-details)!
 Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-14B-Elite&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!

 6. **Dependency on High-Quality Prompts**:
    Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
 Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/prithivMLmods__Calcium-Opus-14B-Elite-details)!
 Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FCalcium-Opus-14B-Elite&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!