Instructions to use AvoCahDoe/qwen2-5-7b-rlmpq-aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AvoCahDoe/qwen2-5-7b-rlmpq-aggressive with Transformers:

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

pipe = pipeline("text-generation", model="AvoCahDoe/qwen2-5-7b-rlmpq-aggressive")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AvoCahDoe/qwen2-5-7b-rlmpq-aggressive")
model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/qwen2-5-7b-rlmpq-aggressive", device_map="auto")
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 Settings

vLLM

How to use AvoCahDoe/qwen2-5-7b-rlmpq-aggressive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AvoCahDoe/qwen2-5-7b-rlmpq-aggressive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/qwen2-5-7b-rlmpq-aggressive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AvoCahDoe/qwen2-5-7b-rlmpq-aggressive

SGLang

How to use AvoCahDoe/qwen2-5-7b-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/qwen2-5-7b-rlmpq-aggressive" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/qwen2-5-7b-rlmpq-aggressive",
		"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 "AvoCahDoe/qwen2-5-7b-rlmpq-aggressive" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AvoCahDoe/qwen2-5-7b-rlmpq-aggressive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AvoCahDoe/qwen2-5-7b-rlmpq-aggressive with Docker Model Runner:
```
docker model run hf.co/AvoCahDoe/qwen2-5-7b-rlmpq-aggressive
```

qwen2-5-7b-rlmpq-aggressive / config.json

AvoCahDoe

RL-MPQ Aggressive — 2026-06-11T21:10:07.790343

83951ed verified about 2 months ago

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	{
	"architectures": [
	"Qwen2ForCausalLM"
	],
	"attention_dropout": 0.0,
	"bos_token_id": 151643,
	"dtype": "bfloat16",
	"eos_token_id": 151643,
	"hidden_act": "silu",
	"hidden_size": 3584,
	"initializer_range": 0.02,
	"intermediate_size": 18944,
	"layer_types": [
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention",
	"full_attention"
	],
	"max_position_embeddings": 131072,
	"max_window_layers": 28,
	"model_type": "qwen2",
	"num_attention_heads": 28,
	"num_hidden_layers": 28,
	"num_key_value_heads": 4,
	"pad_token_id": null,
	"rms_norm_eps": 1e-06,
	"rope_parameters": {
	"rope_theta": 1000000.0,
	"rope_type": "default",
	"type": "default"
	},
	"sliding_window": null,
	"tie_word_embeddings": false,
	"transformers_version": "5.11.0",
	"use_cache": true,
	"use_mrope": false,
	"use_sliding_window": false,
	"vocab_size": 152064,
	"base_model": "Qwen/Qwen2.5-7B",
	"quantization": {
	"method": "rl-mpq",
	"format": "fake-quant-fp16",
	"scenario": "Aggressive",
	"avg_bits": 3.1429,
	"description": "Per-layer mixed bit-width via PPO-trained policy (not GPTQ/AWQ)"
	},
	"rlmpq": {
	"framework": "RL-NMP-Model-Quantasation",
	"scenario": "Aggressive",
	"scenario_label": "Aggressive",
	"repo_id": "AvoCahDoe/qwen2-5-7b-rlmpq-aggressive",
	"grouped_repo": "AvoCahDoe/qwen2-5-7b-rlmpq",
	"collection_title": "RL-MPQ \u2014 Qwen 2.5 7B"
	}
	}