Instructions to use Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci with Transformers:

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

pipe = pipeline("text-generation", model="Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci")
model = AutoModelForCausalLM.from_pretrained("Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci")
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

vLLM

How to use Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci

SGLang

How to use Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci 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 "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci" \
    --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": "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci",
		"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 "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci" \
        --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": "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci with Docker Model Runner:
```
docker model run hf.co/Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DeepSeek-R1-Distill-Qwen-7B Fine-tuned for AIMO Math Problems

This model is a fine-tuned version of deepseek-ai/DeepSeek-R1-Distill-Qwen-7B on the Floppanacci/QWQ-LongCOT-AIMO dataset.

Model Description

The model was fine-tuned to improve performance on mathematical reasoning tasks, particularly those involving step-by-step solutions (Chain-of-Thought) similar to problems found in the AI Mathematical Olympiad (AIMO) competition.

It's trained on a dataset containing ~30k math questions paired with detailed solutions.

An AWQ quantized version is also available for faster inference and reduced memory usage.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Floppanacci/DeepSeek-R1-Distill-Qwen-7B-Floppanacci"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, # or torch.float16
    device_map="auto"
)

# Example Prompt (adjust based on how the model expects input)
prompt = "Question: What is the value of $2+2$? Answer:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.7, do_sample=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)