Instructions to use maldv/Awqward2.5-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maldv/Awqward2.5-32B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="maldv/Awqward2.5-32B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maldv/Awqward2.5-32B-Instruct")
model = AutoModelForCausalLM.from_pretrained("maldv/Awqward2.5-32B-Instruct")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maldv/Awqward2.5-32B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maldv/Awqward2.5-32B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maldv/Awqward2.5-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maldv/Awqward2.5-32B-Instruct

SGLang

How to use maldv/Awqward2.5-32B-Instruct 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 "maldv/Awqward2.5-32B-Instruct" \
    --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": "maldv/Awqward2.5-32B-Instruct",
		"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 "maldv/Awqward2.5-32B-Instruct" \
        --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": "maldv/Awqward2.5-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use maldv/Awqward2.5-32B-Instruct with Docker Model Runner:
```
docker model run hf.co/maldv/Awqward2.5-32B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GGUF Quants

iMat Quants

Awqward 2.5 32B Instruct

Awqward 2.5 32B Instruct is a normalized denoised fourier interpolation of the following models:

output_base_model: "Qwen/Qwen2.5-32B-Instruct"
finetune_merge:
  - { "model": "Qwen/QwQ-32B-Preview", "base": "Qwen/Qwen2.5-32B", "alpha": 0.7, "is_input": true }
  - { "model": "rombodawg/Rombos-LLM-V2.5-Qwen-32b", "base": "Qwen/Qwen2.5-32B-Instruct", "alpha": 0.5 }
  - { "model": "AiCloser/Qwen2.5-32B-AGI", "base": "Qwen/Qwen2.5-32B-Instruct", "alpha": 0.5, "is_output": true }
  - { "model": "EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2", "base": "Qwen/Qwen2.5-32B", "alpha": 0.5 }

In other words, all of these models get warped and interpolated in signal space, and then jammed back on top of the instruct model.

What is this?

QwQ is a really nifty model, but it was giving me problems with xml output - which is what I use for my thought tokens. So, I thought... lets just merge it in!

I first attempted to do this using Qwen2.5-Coder-32B/Qwen2.5-Coder-32B-Instruct, but after analysis, they are not directly homologous through either Qwen2.5 or Qwen2.5-Instruct. This was quite a surprise, and makes me wonder what the model speciation tree looks like.

Initial Results

I didn't do much testing yet, but so far so good.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{awqward2.5-32b-instruct,
    title = {Awqward 2.5 32B Instruct},
    url = {https://huggingface.co/maldv/awqward-2.5-32b-instruct},
    author = {Praxis Maldevide},
    month = {December},
    year = {2024}
}