Instructions to use AvoCahDoe/gemma-2-9b-rlmpq-balanced with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AvoCahDoe/gemma-2-9b-rlmpq-balanced with Transformers:

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

pipe = pipeline("text-generation", model="AvoCahDoe/gemma-2-9b-rlmpq-balanced")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AvoCahDoe/gemma-2-9b-rlmpq-balanced")
model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/gemma-2-9b-rlmpq-balanced", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AvoCahDoe/gemma-2-9b-rlmpq-balanced with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AvoCahDoe/gemma-2-9b-rlmpq-balanced"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/gemma-2-9b-rlmpq-balanced",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AvoCahDoe/gemma-2-9b-rlmpq-balanced

SGLang

How to use AvoCahDoe/gemma-2-9b-rlmpq-balanced 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/gemma-2-9b-rlmpq-balanced" \
    --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/gemma-2-9b-rlmpq-balanced",
		"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/gemma-2-9b-rlmpq-balanced" \
        --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/gemma-2-9b-rlmpq-balanced",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AvoCahDoe/gemma-2-9b-rlmpq-balanced with Docker Model Runner:
```
docker model run hf.co/AvoCahDoe/gemma-2-9b-rlmpq-balanced
```

gemma-2-9b-rlmpq-balanced / rlmpq_metrics.json

AvoCahDoe

RL-MPQ metadata refresh — 2026-06-11T20:42:01.157442

4a7d6d0 verified 3 months ago

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	{
	"framework": "RL-NMP-Model-Quantasation",
	"method": "RL-MPQ",
	"repo_id": "AvoCahDoe/gemma-2-9b-rlmpq-balanced",
	"base_model": "google/gemma-2-9b",
	"model_slug": "gemma_2_9b",
	"scenario": "Balanced",
	"scenario_label": "Balanced",
	"avg_bits_per_weight": 4.2857,
	"compression_vs_fp16": 3.7333,
	"wikitext2_perplexity": 127.0798,
	"bit_distribution": {
	"4": 39,
	"8": 3
	},
	"quantization": {
	"type": "fake-quant-fp16",
	"group_size": 128,
	"scheme": "per-layer asymmetric group-wise",
	"packed_format": false
	},
	"exported_at": "2026-06-11T20:42:01.095304",
	"policy_source": "/workspace/RL-NMP-Model-Quantasation/phase3/models/gemma_2_9b/results/Balanced_policy.json",
	"policy_path": "/workspace/RL-NMP-Model-Quantasation/phase3/models/gemma_2_9b/results/Balanced_policy.json",
	"training_run": "20260610_202902"
	}