Instructions to use AvoCahDoe/llama-2-7b-rlmpq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AvoCahDoe/llama-2-7b-rlmpq with Transformers:

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

pipe = pipeline("text-generation", model="AvoCahDoe/llama-2-7b-rlmpq")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq")
model = AutoModelForCausalLM.from_pretrained("AvoCahDoe/llama-2-7b-rlmpq", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AvoCahDoe/llama-2-7b-rlmpq with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AvoCahDoe/llama-2-7b-rlmpq"
# 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-2-7b-rlmpq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq

SGLang

How to use AvoCahDoe/llama-2-7b-rlmpq 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-2-7b-rlmpq" \
    --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-2-7b-rlmpq",
		"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-2-7b-rlmpq" \
        --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-2-7b-rlmpq",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AvoCahDoe/llama-2-7b-rlmpq with Docker Model Runner:
```
docker model run hf.co/AvoCahDoe/llama-2-7b-rlmpq
```

llama-2-7b-rlmpq

File size: 759 Bytes

ca8cb5e

{
  "slug": "llama_2_7b",
  "hf_id": "meta-llama/Llama-2-7b-hf",
  "scenario": "Conservative",
  "params": {
    "lambda_mse": 1.0,
    "lambda_bit": 0.15,
    "max_mse_clip": 1.0,
    "target_bits": 5.5,
    "lambda_budget": 3.0
  },
  "policy": [
    8,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    4,
    8,
    8,
    8,
    8,
    8,
    8,
    8,
    8
  ],
  "num_layers": 32,
  "avg_bits": 5.125,
  "total_reward": -3.726647,
  "compression_ratio": 3.122,
  "approx_size_gb": 4.484,
  "bit_distribution": {
    "4": 23,
    "8": 9
  },
  "avg_mse_per_bit": {
    "8": 0.00010723,
    "4": 0.3228678
  },
  "validation_elapsed_s": 0.0151
}