Instructions to use MaziyarPanahi/calme-2.2-llama3.1-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/calme-2.2-llama3.1-70b with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.2-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-2.2-llama3.1-70b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.2-llama3.1-70b", device_map="auto")
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 Settings

vLLM

How to use MaziyarPanahi/calme-2.2-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-2.2-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-2.2-llama3.1-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/calme-2.2-llama3.1-70b

SGLang

How to use MaziyarPanahi/calme-2.2-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-2.2-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-2.2-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-2.2-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-2.2-llama3.1-70b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/calme-2.2-llama3.1-70b with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/calme-2.2-llama3.1-70b
```

MaziyarPanahi commited on Sep 29, 2024

Commit

e3ada17

verified ·

1 Parent(s): ace93d1

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -14

README.md CHANGED Viewed

@@ -135,9 +135,19 @@ This model is suitable for a wide range of applications, including but not limit
 coming soon!
-# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-coming soon!
 This model uses `ChatML` prompt template:
@@ -179,16 +189,4 @@ model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.2-llama3.1-7
 # 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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__calme-2.2-llama3.1-70b)
-|      Metric       |Value|
-|-------------------|----:|
-|Avg.               |36.39|
-|IFEval (0-Shot)    |85.93|
-|BBH (3-Shot)       |54.21|
-|MATH Lvl 5 (4-Shot)| 2.11|
-|GPQA (0-shot)      | 9.96|
-|MuSR (0-shot)      |17.07|
-|MMLU-PRO (5-shot)  |49.05|

 coming soon!
+# 🏆 [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/details_MaziyarPanahi__calme-2.2-llama3.1-70b)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |36.39|
+|IFEval (0-Shot)    |85.93|
+|BBH (3-Shot)       |54.21|
+|MATH Lvl 5 (4-Shot)| 2.11|
+|GPQA (0-shot)      | 9.96|
+|MuSR (0-shot)      |17.07|
+|MMLU-PRO (5-shot)  |49.05|
 This model uses `ChatML` prompt template:
 # 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.