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

Libraries

How to use AvoCahDoe/llama-2-13b-rlmpq-high-fidelity with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Use Docker

docker model run hf.co/AvoCahDoe/llama-2-13b-rlmpq-high-fidelity

SGLang

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

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

AvoCahDoe commited on Jun 11

Commit

cc76575

verified ·

1 Parent(s): 671f392

RL-MPQ metadata refresh — 2026-06-11T18:53:57.788133

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README.md +1 -0

README.md CHANGED Viewed

@@ -36,6 +36,7 @@ Standalone **RL-MPQ** (Reinforcement Learning Mixed-Precision Quantization) chec
 | **Bit distribution** | `{'4': 13, '8': 27}` |
 | **Format** | Fake-quant FP16 + `rlmpq_policy.json` |
 ## Usage

 | **Bit distribution** | `{'4': 13, '8': 27}` |
 | **Format** | Fake-quant FP16 + `rlmpq_policy.json` |
+**Collection:** [RL-MPQ — Llama 2 13B](https://huggingface.co/collections/AvoCahDoe/rl-mpq-llama-2-13b-6a2b04386dd0303714a19782) — all five scenarios for Llama 2 13B.
 ## Usage