Instructions to use AvoCahDoe/llama-2-7b-rlmpq-aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AvoCahDoe/llama-2-7b-rlmpq-aggressive with Transformers:

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

pipe = pipeline("text-generation", model="AvoCahDoe/llama-2-7b-rlmpq-aggressive")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq-aggressive")
model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq-aggressive", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AvoCahDoe/llama-2-7b-rlmpq-aggressive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AvoCahDoe/llama-2-7b-rlmpq-aggressive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/llama-2-7b-rlmpq-aggressive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq-aggressive

SGLang

How to use AvoCahDoe/llama-2-7b-rlmpq-aggressive 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 "AvoCahDoe/llama-2-7b-rlmpq-aggressive" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/llama-2-7b-rlmpq-aggressive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AvoCahDoe/llama-2-7b-rlmpq-aggressive" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/llama-2-7b-rlmpq-aggressive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AvoCahDoe/llama-2-7b-rlmpq-aggressive with Docker Model Runner:
```
docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq-aggressive
```

llama-2-7b-rlmpq-aggressive / model.safetensors

AvoCahDoe

RL-MPQ Aggressive — 2026-06-11T16:10:40.772530

02e5188 verified about 2 months ago

Download

History Blame Contribute Delete

13.5 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 6f8941b020f11a6b3c709bef9ec72e558c76b1db6587b2b49fe509bd26193742
Size of remote file:: 13.5 GB
SHA256:: 9e97f67c62e52fd779b89df09067718e8084f1844f4fa0f2592e621e2be71b36

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.