Instructions to use radi-cho/Meta-Llama-3-8B-Instruct-FLUTE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use radi-cho/Meta-Llama-3-8B-Instruct-FLUTE with Transformers:

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

pipe = pipeline("text-generation", model="radi-cho/Meta-Llama-3-8B-Instruct-FLUTE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("radi-cho/Meta-Llama-3-8B-Instruct-FLUTE")
model = AutoModelForCausalLM.from_pretrained("radi-cho/Meta-Llama-3-8B-Instruct-FLUTE")
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 radi-cho/Meta-Llama-3-8B-Instruct-FLUTE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "radi-cho/Meta-Llama-3-8B-Instruct-FLUTE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "radi-cho/Meta-Llama-3-8B-Instruct-FLUTE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/radi-cho/Meta-Llama-3-8B-Instruct-FLUTE

SGLang

How to use radi-cho/Meta-Llama-3-8B-Instruct-FLUTE 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 "radi-cho/Meta-Llama-3-8B-Instruct-FLUTE" \
    --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": "radi-cho/Meta-Llama-3-8B-Instruct-FLUTE",
		"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 "radi-cho/Meta-Llama-3-8B-Instruct-FLUTE" \
        --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": "radi-cho/Meta-Llama-3-8B-Instruct-FLUTE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use radi-cho/Meta-Llama-3-8B-Instruct-FLUTE with Docker Model Runner:
```
docker model run hf.co/radi-cho/Meta-Llama-3-8B-Instruct-FLUTE
```

	Wiki	C4
W4G64	6.78	10.61
W3G64	7.75	12.28

Revisions available in this repository:

main (W4G64, scales learned);
nfl_w3g64 (W3G64, scales learned);
nf_w4g64 (W4G64, scales not learned);
nf_w3g64 (W3G64, scales not learned);

Evaluations are provided for models with learned scales.
Check the base Meta-Llama-3-8B-FLUTE for lm-eval-harness benchmarks.

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

F16

F32

I16

Collection including radi-cho/Meta-Llama-3-8B-Instruct-FLUTE

LLaMA FLUTE

Collection

8 items • Updated Aug 4, 2024 • 1