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

Libraries

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

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

pipe = pipeline("text-generation", model="AvoCahDoe/qwen2-5-7b-rlmpq-balanced")
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-balanced")
model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/qwen2-5-7b-rlmpq-balanced", 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-balanced 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-balanced"
# 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-balanced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use AvoCahDoe/qwen2-5-7b-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/qwen2-5-7b-rlmpq-balanced" \
    --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-balanced",
		"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-balanced" \
        --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-balanced",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

qwen2-5-7b-rlmpq-balanced

File size: 954 Bytes

986dde3

{
  "slug": "qwen2_5_7b",
  "hf_id": "Qwen/Qwen2.5-7B",
  "scenario": "Balanced",
  "params": {
    "target_bits": 3.25,
    "lambda_lag": 18,
    "lambda_mse": 1.5,
    "lambda_bit": 0.55
  },
  "policy": [
    4,
    4,
    4,
    4,
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    4,
    4,
    4,
    4,
    4,
    4,
    3,
    3,
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    3,
    3,
    3,
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  ],
  "num_layers": 28,
  "avg_bits": 3.3929,
  "total_reward": -15.768258,
  "compression_ratio": 4.7158,
  "approx_size_gb": 2.969,
  "bit_distribution": {
    "3": 17,
    "4": 11
  },
  "avg_mse_per_bit": {
    "4": 1.182e-05,
    "3": 5.426e-05
  },
  "validation_elapsed_s": 0.0163,
  "policy_source": "/workspace/RL-NMP-Model-Quantasation/phase3/models/qwen2_5_7b/results/Balanced_policy.json",
  "policy_path": "/workspace/RL-NMP-Model-Quantasation/phase3/models/qwen2_5_7b/results/Balanced_policy.json",
  "training_run": "20260610_202902"
}