Instructions to use QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill with Transformers:

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

pipe = pipeline("text-generation", model="QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill

SGLang

How to use QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill 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 "QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill" \
    --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": "QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill",
		"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 "QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill" \
        --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": "QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill with Docker Model Runner:
```
docker model run hf.co/QwerkyAI/QRe-Llama-3-3B-Instruct-QDistill
```

QwerkyLlamaMambaHybrid

Hybrid Mamba-Transformer model from Qwerky AI.

Requirements

CUDA-compatible GPU
Python 3.8+
PyTorch 2.0+
transformers, safetensors, mamba-ssm, causal-conv1d, flash-attn

Installation

pip install transformers torch safetensors
pip install flash-attn mamba-ssm causal-conv1d --no-build-isolation

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QwerkyAI/Qwick-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
    "QwerkyAI/Qwick-3B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

inputs = tokenizer("Hello, how are you?", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0]))

Model Files

config.json - Model configuration with auto_map
modeling_qwerky_llama_mamba_hybrid.py - Custom modeling class
configuration_qwerky_llama_mamba_hybrid.py - Custom configuration class
model.safetensors - Model weights

License

See LICENSE file for details.

Downloads last month: 15

Safetensors

Model size

4B params

Tensor type

BF16