Instructions to use catid/cat-llama-3-8b-instruct-aqlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use catid/cat-llama-3-8b-instruct-aqlm with Transformers:

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

pipe = pipeline("text-generation", model="catid/cat-llama-3-8b-instruct-aqlm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("catid/cat-llama-3-8b-instruct-aqlm")
model = AutoModelForCausalLM.from_pretrained("catid/cat-llama-3-8b-instruct-aqlm")
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 catid/cat-llama-3-8b-instruct-aqlm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/catid/cat-llama-3-8b-instruct-aqlm

SGLang

How to use catid/cat-llama-3-8b-instruct-aqlm 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 "catid/cat-llama-3-8b-instruct-aqlm" \
    --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": "catid/cat-llama-3-8b-instruct-aqlm",
		"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 "catid/cat-llama-3-8b-instruct-aqlm" \
        --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": "catid/cat-llama-3-8b-instruct-aqlm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use catid/cat-llama-3-8b-instruct-aqlm with Docker Model Runner:
```
docker model run hf.co/catid/cat-llama-3-8b-instruct-aqlm
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

AI Model Name: Llama 3 8B "Built with Meta Llama 3" https://llama.meta.com/llama3/license/

Full walkthrough to reproduce these results here: https://github.com/catid/AQLM/blob/main/catid_readme.md

Baseline evaluation results:

hf (pretrained=meta-llama/Meta-Llama-3-8B-Instruct), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 16
|    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|winogrande   |      1|none  |     0|acc     |0.7198|±  |0.0126|
|piqa         |      1|none  |     0|acc     |0.7873|±  |0.0095|
|             |       |none  |     0|acc_norm|0.7867|±  |0.0096|
|hellaswag    |      1|none  |     0|acc     |0.5767|±  |0.0049|
|             |       |none  |     0|acc_norm|0.7585|±  |0.0043|
|arc_easy     |      1|none  |     0|acc     |0.8140|±  |0.0080|
|             |       |none  |     0|acc_norm|0.7971|±  |0.0083|
|arc_challenge|      1|none  |     0|acc     |0.5290|±  |0.0146|
|             |       |none  |     0|acc_norm|0.5674|±  |0.0145|

This repo evaluation results (AQLM with global fine-tuning):

hf (pretrained=catid/cat-llama-3-8b-instruct-aqlm), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 16
|    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|-------------|------:|------|-----:|--------|-----:|---|-----:|
|winogrande   |      1|none  |     0|acc     |0.7119|±  |0.0127|
|piqa         |      1|none  |     0|acc     |0.7807|±  |0.0097|
|             |       |none  |     0|acc_norm|0.7824|±  |0.0096|
|hellaswag    |      1|none  |     0|acc     |0.5716|±  |0.0049|
|             |       |none  |     0|acc_norm|0.7539|±  |0.0043|
|arc_easy     |      1|none  |     0|acc     |0.8152|±  |0.0080|
|             |       |none  |     0|acc_norm|0.7866|±  |0.0084|
|arc_challenge|      1|none  |     0|acc     |0.5043|±  |0.0146|
|             |       |none  |     0|acc_norm|0.5555|±  |0.0145|

To reproduce evaluation results:

git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness

conda create -n lmeval python=3.10 -y && conda activate lmeval
pip install -e .
pip install accelerate aqlm"[gpu,cpu]"

accelerate launch lm_eval --model hf \
    --model_args pretrained=catid/cat-llama-3-8b-instruct-aqlm \
    --tasks winogrande,piqa,hellaswag,arc_easy,arc_challenge \
    --batch_size 16

You can run this model as a transformers model using https://github.com/oobabooga/text-generation-webui

Downloads last month: 10

Safetensors

Model size

3B params

Tensor type

F16

I16