Instructions to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with Transformers:

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

pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1-Distill-Llama-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-8B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Llama-8B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B

SGLang

How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B 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 "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" \
    --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": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
		"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 "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" \
        --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": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use deepseek-ai/DeepSeek-R1-Distill-Llama-8B with Docker Model Runner:
```
docker model run hf.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B
```

Breaking the 8B Record: 128k Context Reasoning on a 6GB Laptop GPU via 5-Pillar Unified Idempotent Stack

#39

by aecetin - opened Sep 7

Discussion

aecetin

Sep 7

•

edited Sep 14

Hi everyone!

Following our weight surgery benchmark, we pushed the frontier further by combining all 5 Idempotent Deep Learning Pillars (Pillars 21–25) on the DeepSeek-R1-Distill-Llama-8B architecture.

We evaluated the unified architecture on an entry-level consumer laptop (NVIDIA RTX PRO 500 Blackwell Laptop GPU, 6 GB VRAM, 32 GB RAM) and achieved a new efficiency record:

The 5-Pillar Unified Stack:

Pillar 21 (idempotent-poly): Chebyshev Polynomial Tensor FFN replacing SwiGLU (-61.9% FFN weights).
Pillar 22 (idempotent-attention): Weight-Shared Recursive Universal Attention (-96.88% attention weights; 32 layers fold into 1 shared core).
Pillar 23 (idempotent-compaction): Subspace Context Folding ($C(C(KV)) \equiv C(KV)$) achieving 32x KV-cache compression.
Pillar 24 (idempotent-reasoning): Tarski Consequence Fixpoint ($f(Q \oplus A) = A$) ensuring zero reasoning hallucination drift across long thinking chains.
Pillar 25 (idempotent-tropical): Zero-multiplication $(\max, +)$ tropical attention eliminating float multipliers.

📊 Benchmark Highlights & Comparison:

Metric	Standard DeepSeek-R1-8B	Unified Idempotent Transformer	Net Advantage
Total Parameters	8.03 Billion	2.71 Billion	-63.83% Parameter Elimination
4-Bit Model Weights	4.92 GB	~1.45 GB	3.47 GB VRAM Saved
FFN Forward Latency	100.70 ms	41.63 ms	2.42x Faster Execution! ⚡
32k Context KV-Cache	4.00 GB	128.0 MB	32x Compression (-96.88%)
128k Context KV-Cache	16.00 GB (Server-grade)	512.0 MB	128K Context on a Laptop!
Total VRAM @ 32k Context	9.22 GB (OOM Crash)	~1.58 GB	Runs on any 4GB/6GB GPU or Mac!
Total VRAM @ 128k Context	21.22 GB (Data Center)	~1.96 GB	Fits in < 2 GB VRAM!
Reasoning Consistency	Prone to drift	Tarski Invariance	Guaranteed Mathematical Fixpoint

🚀 What This Means for Local AI:

Normally, running a reasoning model with 128,000 tokens of context requires enterprise data-center GPUs (A100/H100) because KV-cache alone consumes 16 GB of VRAM.

With 32x subspace context folding and polynomial FFN surgery, the entire 8B reasoning system + 128k context consumes less than 2.0 GB of VRAM total, making deep reasoning accessible to anyone on an everyday laptop.

Links & Reproducibility:

🔗 Hugging Face Model: aecetin/DeepSeek-R1-8B-Unified-IdemFormer
🔗 Surgery Model: aecetin/DeepSeek-R1-8B-PolySurgery
🔗 GitHub Repository & Full Benchmark Script: github.com/aemre-cetin/idempotent-poly
📜 Full Paper: ResearchGate Publication 414060833
🚀 Interactive Live Demo: Hugging Face Space: IdemAILab (5-Pillar Architecture)
⚖️ Intellectual Property: U.S. Patent Application No. 64/148,668 & 64/152,276 ("Idempotent Attention, In-Situ KV Compaction & Closed-Form Weight Surgery", Patent Pending).

Happy to discuss the algebraic mechanics and benchmark reproductions with the community!

aecetin changed discussion status to closed Sep 7

aecetin

Sep 15

Reopening discussion for ongoing community engagement and benchmark evaluations.

aecetin changed discussion status to open Sep 15

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