Instructions to use marcuscedricridia/QwQ-R1984-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use marcuscedricridia/QwQ-R1984-32B with Transformers:

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

pipe = pipeline("text-generation", model="marcuscedricridia/QwQ-R1984-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("marcuscedricridia/QwQ-R1984-32B")
model = AutoModelForMultimodalLM.from_pretrained("marcuscedricridia/QwQ-R1984-32B")
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 marcuscedricridia/QwQ-R1984-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "marcuscedricridia/QwQ-R1984-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "marcuscedricridia/QwQ-R1984-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/marcuscedricridia/QwQ-R1984-32B

SGLang

How to use marcuscedricridia/QwQ-R1984-32B 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 "marcuscedricridia/QwQ-R1984-32B" \
    --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": "marcuscedricridia/QwQ-R1984-32B",
		"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 "marcuscedricridia/QwQ-R1984-32B" \
        --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": "marcuscedricridia/QwQ-R1984-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use marcuscedricridia/QwQ-R1984-32B with Docker Model Runner:
```
docker model run hf.co/marcuscedricridia/QwQ-R1984-32B
```

QwQ-R1984-32B / README.md

marcuscedricridia

Re-release VIDraft/QwQ-R1984-32B

3bc2f55 verified about 1 year ago

preview code

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- Qwen/QwQ-32B
	tags:
	- abliterated
	- uncensored
	- SEARCH
	library_name: transformers
	---

	# VIDraft/QwQ-R1984-32B

	QwQ is the reasoning model of the Qwen series. Compared with conventional instruction-tuned models, QwQ, which is capable of thinking and reasoning, can achieve significantly enhanced performance in downstream tasks, especially hard problems. QwQ-32B is the medium-sized reasoning model, which is capable of achieving competitive performance against state-of-the-art reasoning models, e.g., DeepSeek-R1, o1-mini. QwQ-R1984-32B is an enhanced version based on QwQ-32B that incorporates additional features such as uncensored capabilities and deep research functionality. This allows for more unrestricted responses and in-depth information provision based on real-time web searches.


	# This repo contains the QwQ-R1984-32B model, which has the following features:


	- Type: Reasoning-enhanced Causal Language Model
	- Training Stage: Pretraining, Supervised Finetuning, Reinforcement Learning, and Uncensoring
	- Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
	- Number of Parameters: 32.5B
	- Number of Parameters (Non-Embedding): 31.0B
	- Number of Layers: 64
	- Number of Attention Heads (GQA): 40 for Q and 8 for KV
	- Context Length: 8,000 tokens
	- Additional Features:
	- Deep research capabilities via web search
	- Uncensored response generation


	# Quickstart

	Here provides a code snippet with apply_chat_template to show you how to load the tokenizer and model and how to generate contents.

	```py

	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "VIDraft/QwQ-R1984-32B"

	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype="auto",
	device_map="auto"
	)
	tokenizer = AutoTokenizer.from_pretrained(model_name)

	prompt = "How many r's are in the word \"strawberry\""
	messages = [
	{"role": "user", "content": prompt}
	]
	text = tokenizer.apply_chat_template(
	messages,
	tokenize=False,
	add_generation_prompt=True
	)

	model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

	generated_ids = model.generate(
	**model_inputs,
	max_new_tokens=32768
	)
	generated_ids = [
	output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
	]

	response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
	print(response)

	```