Instructions to use AvoCahDoe/llama-2-13b-rlmpq-extreme-survival with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AvoCahDoe/llama-2-13b-rlmpq-extreme-survival with Transformers:

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

pipe = pipeline("text-generation", model="AvoCahDoe/llama-2-13b-rlmpq-extreme-survival")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AvoCahDoe/llama-2-13b-rlmpq-extreme-survival")
model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/llama-2-13b-rlmpq-extreme-survival", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AvoCahDoe/llama-2-13b-rlmpq-extreme-survival with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AvoCahDoe/llama-2-13b-rlmpq-extreme-survival"
# 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-2-13b-rlmpq-extreme-survival",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AvoCahDoe/llama-2-13b-rlmpq-extreme-survival

SGLang

How to use AvoCahDoe/llama-2-13b-rlmpq-extreme-survival 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-2-13b-rlmpq-extreme-survival" \
    --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-2-13b-rlmpq-extreme-survival",
		"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-2-13b-rlmpq-extreme-survival" \
        --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-2-13b-rlmpq-extreme-survival",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AvoCahDoe/llama-2-13b-rlmpq-extreme-survival with Docker Model Runner:
```
docker model run hf.co/AvoCahDoe/llama-2-13b-rlmpq-extreme-survival
```

llama-2-13b-rlmpq-extreme-survival / rlmpq_metrics.json

AvoCahDoe

RL-MPQ metadata refresh — 2026-06-11T18:53:54.474507

210dc3c verified about 2 months ago

Raw

History Blame

913 Bytes

	{
	"framework": "RL-NMP-Model-Quantasation",
	"method": "RL-MPQ",
	"repo_id": "AvoCahDoe/llama-2-13b-rlmpq-extreme-survival",
	"base_model": "meta-llama/Llama-2-13b-hf",
	"model_slug": "llama_2_13b",
	"scenario": "Extreme_Survival",
	"scenario_label": "Extreme Survival",
	"avg_bits_per_weight": 2.775,
	"compression_vs_fp16": 5.7658,
	"wikitext2_perplexity": 6.1148,
	"bit_distribution": {
	"2": 9,
	"3": 31
	},
	"quantization": {
	"type": "fake-quant-fp16",
	"group_size": 128,
	"scheme": "per-layer asymmetric group-wise",
	"packed_format": false
	},
	"exported_at": "2026-06-11T18:53:54.447898",
	"policy_source": "/workspace/RL-NMP-Model-Quantasation/phase3/models/llama_2_13b/results/Extreme_Survival_policy.json",
	"policy_path": "/workspace/RL-NMP-Model-Quantasation/phase3/models/llama_2_13b/results/Extreme_Survival_policy.json",
	"training_run": "20260610_202902"
	}