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 / README.md

AvoCahDoe

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

259c1c6 verified 3 months ago

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metadata

license: gemma
base_model: google/gemma-2-9b
pipeline_tag: text-generation
language:
  - en
tags:
  - gemma
  - text-generation
  - rl-mpq
  - mixed-precision
  - quantization
  - fake-quantization
  - balanced
library_name: transformers
datasets:
  - wikitext
widget:
  - text: The capital of France is

Gemma 2 9B — RL-MPQ Balanced

Standalone RL-MPQ (Reinforcement Learning Mixed-Precision Quantization) checkpoint for the Balanced scenario — a quantized variant of google/gemma-2-9b.

Field	Value
Base model	google/gemma-2-9b
Scenario	Balanced
Avg bits / weight	4.2857
Compression vs FP16	3.7333×
WikiText-2 PPL	127.0798
Layers	42
Bit distribution	`{'4': 39, '8': 3}`
Format	Fake-quant FP16 + `rlmpq_policy.json`

Collection: RL-MPQ — Gemma 2 9B — all five scenarios for Gemma 2 9B.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "AvoCahDoe/gemma-2-9b-rlmpq-balanced"

model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="float16")
tokenizer = AutoTokenizer.from_pretrained(repo)

Other Gemma 2 9B scenarios

Scenario	Avg bits	Compression	WikiText-2 PPL
Aggressive	3.6667	4.3636x	162.8437
Conservative	5.1429	3.1111x	116.5244
Extreme Survival	2.7857	5.7436x	424.7991
High Fidelity	7.0476	2.2703x	104.8098

Grouped archive (all scenarios in one repo): AvoCahDoe/gemma-2-9b-rlmpq

Method

Phase 3 — PPO agent assigns per-layer bit widths under the Balanced reward target.
Phase 4 — Policy replayed on real weights; WikiText-2 perplexity validates quality.
Export — Fake-quantized FP16 weights compatible with Hugging Face Transformers.

Files

File	Description
`config.json`	Llama architecture + RL-MPQ metadata
`model.safetensors`	Fake-quantized weights
`rlmpq_policy.json`	Per-layer bit-width policy
`rlmpq_metrics.json`	Validation & PPL summary

Citation

@misc{rlmpq_gemma_2_9b_balanced_2026,
  title  = {RL-MPQ Balanced: Gemma 2 9B Mixed-Precision Quantization},
  author = {AvoCahDoe},
  year   = {2026},
  url    = {https://huggingface.co/AvoCahDoe/gemma-2-9b-rlmpq-balanced}
}