Instructions to use casperhansen/llama-3.3-70b-instruct-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use casperhansen/llama-3.3-70b-instruct-awq with Transformers:

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

pipe = pipeline("text-generation", model="casperhansen/llama-3.3-70b-instruct-awq")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("casperhansen/llama-3.3-70b-instruct-awq")
model = AutoModelForCausalLM.from_pretrained("casperhansen/llama-3.3-70b-instruct-awq")
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 casperhansen/llama-3.3-70b-instruct-awq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "casperhansen/llama-3.3-70b-instruct-awq"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "casperhansen/llama-3.3-70b-instruct-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/casperhansen/llama-3.3-70b-instruct-awq

SGLang

How to use casperhansen/llama-3.3-70b-instruct-awq 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 "casperhansen/llama-3.3-70b-instruct-awq" \
    --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": "casperhansen/llama-3.3-70b-instruct-awq",
		"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 "casperhansen/llama-3.3-70b-instruct-awq" \
        --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": "casperhansen/llama-3.3-70b-instruct-awq",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use casperhansen/llama-3.3-70b-instruct-awq with Docker Model Runner:
```
docker model run hf.co/casperhansen/llama-3.3-70b-instruct-awq
```

This is the AWQ version of the Llama 3.3 70B Instruct model. Find more info here: https://github.com/casper-hansen/AutoAWQ.

Model Information

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

Model developer: Meta

Model Architecture: Llama 3.3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

	Training Data	Params	Input modalities	Output modalities	Context length	GQA	Token count	Knowledge cutoff
Llama 3.3 (text only)	A new mix of publicly available online data.	70B	Multilingual Text	Multilingual Text and code	128k	Yes	15T+	December 2023

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Llama 3.3 model. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model Release Date:

70B Instruct: December 6, 2024

Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.

License A custom commercial license, the Llama 3.3 Community License Agreement, is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/LICENSE

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3.3 in applications, please go here.

Benchmark

Category	Benchmark	# Shots	Metric	Llama 3.1 8B Instruct	Llama 3.1 70B Instruct	Llama-3.3 70B Instruct	Llama 3.1 405B Instruct
	MMLU (CoT)	0	macro_avg/acc	73.0	86.0	86.0	88.6
	MMLU Pro (CoT)	5	macro_avg/acc	48.3	66.4	68.9	73.3
Steerability	IFEval			80.4	87.5	92.1	88.6
Reasoning	GPQA Diamond (CoT)	0	acc	31.8	48.0	50.5	49.0
Code	HumanEval	0	pass@1	72.6	80.5	88.4	89.0
	MBPP EvalPlus (base)	0	pass@1	72.8	86.0	87.6	88.6
Math	MATH (CoT)	0	sympy_intersection_score	51.9	68.0	77.0	73.8
Tool Use	BFCL v2	0	overall_ast_summary/macro_avg/valid	65.4	77.5	77.3	81.1
Multilingual	MGSM	0	em	68.9	86.9	91.1	91.6

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Safetensors

Model size

71B params

Tensor type

I32

F16

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