Instructions to use MaziyarPanahi/calme-2.1-llama3.1-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/calme-2.1-llama3.1-70b with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-llama3.1-70b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-llama3.1-70b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-llama3.1-70b")
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

vLLM

How to use MaziyarPanahi/calme-2.1-llama3.1-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/calme-2.1-llama3.1-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/calme-2.1-llama3.1-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/calme-2.1-llama3.1-70b

SGLang

How to use MaziyarPanahi/calme-2.1-llama3.1-70b 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-2.1-llama3.1-70b" \
    --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": "MaziyarPanahi/calme-2.1-llama3.1-70b",
		"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 "MaziyarPanahi/calme-2.1-llama3.1-70b" \
        --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": "MaziyarPanahi/calme-2.1-llama3.1-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/calme-2.1-llama3.1-70b with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/calme-2.1-llama3.1-70b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

MaziyarPanahi/calme-2.1-llama3.1-70b

This model is a fine-tuned version of the powerful meta-llama/Meta-Llama-3.1-70B-Instruct, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.

Use Cases

This model is suitable for a wide range of applications, including but not limited to:

Advanced question-answering systems
Intelligent chatbots and virtual assistants
Content generation and summarization
Code generation and analysis
Complex problem-solving and decision support

⚡ Quantized GGUF

All GGUF models are available here: MaziyarPanahi/calme-2.1-llama3.1-70b-GGUF

🏆 Open LLM Leaderboard Evaluation Results

coming soon!

This model uses ChatML prompt template:

<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}

How to use


# Use a pipeline as a high-level helper

from transformers import pipeline

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-llama3.1-70b")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-llama3.1-70b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-llama3.1-70b")

Ethical Considerations

As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	34.34
IFEval (0-Shot)	84.34
BBH (3-Shot)	48.55
MATH Lvl 5 (4-Shot)	1.44
GPQA (0-shot)	10.40
MuSR (0-shot)	13.72
MMLU-PRO (5-shot)	47.58

Downloads last month: 9

Safetensors

Model size

71B params

Tensor type

F32

Model tree for MaziyarPanahi/calme-2.1-llama3.1-70b

Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.1-70B-Instruct

Finetuned

(90)

this model

Quantizations

3 models

Dataset used to train MaziyarPanahi/calme-2.1-llama3.1-70b

Collections including MaziyarPanahi/calme-2.1-llama3.1-70b

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

84.340
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

48.550
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

1.440
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

10.400
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

13.720
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

47.580