Instructions to use Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2 with Transformers:

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

pipe = pipeline("text-generation", model="Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2")
model = AutoModelForCausalLM.from_pretrained("Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2

SGLang

How to use Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2 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 "Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2" \
    --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": "Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2",
		"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 "Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2" \
        --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": "Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2 with Docker Model Runner:
```
docker model run hf.co/Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2
```

Exllamav2 quant (exl2 / 8.0 bpw) made with ExLlamaV2 v0.0.21

Other EXL2 quants:

Quant	Model Size	lm_head
2.2	4176 MB	6
2.5	4519 MB	6
3.0	5143 MB	6
3.5	5766 MB	6
3.75	6077 MB	6
4.0	6391 MB	6
4.25	6703 MB	6
5.0	7637 MB	6
6.0	8992 MB	8
6.5	9616 MB	8
8.0	11473 MB	8

Meta-Llama-3-12B-Instruct

Meta-Llama-3-12B-Instruct is a merge of the following models using LazyMergekit:

🏆 Evaluation

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
Meta-Llama-3-12B-Instruct	41.7	67.71	52.75	40.58	50.69
Meta-Llama-3-12B	29.46	68.01	41.02	35.57	43.52

🧩 Configuration

slices:
  - sources:
    - model: NousResearch/Meta-Llama-3-8B-Instruct
      layer_range: [0,9]
  - sources:
    - model: NousResearch/Meta-Llama-3-8B-Instruct
      layer_range: [5,14]
  - sources:
    - model: NousResearch/Meta-Llama-3-8B-Instruct
      layer_range: [10,19]
  - sources:
    - model: NousResearch/Meta-Llama-3-8B-Instruct
      layer_range: [15,24]
  - sources:
    - model: NousResearch/Meta-Llama-3-8B-Instruct
      layer_range: [20,32]
merge_method: passthrough
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/Meta-Llama-3-12B-Instruct"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 4

Model tree for Zoyd/mlabonne_Meta-Llama-3-12B-Instruct-8_0bpw_exl2

Base model

NousResearch/Meta-Llama-3-8B-Instruct

Quantized

(19)

this model