Instructions to use unsloth/r1-1776-distill-llama-70b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/r1-1776-distill-llama-70b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="unsloth/r1-1776-distill-llama-70b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unsloth/r1-1776-distill-llama-70b-GGUF")
model = AutoModelForCausalLM.from_pretrained("unsloth/r1-1776-distill-llama-70b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use unsloth/r1-1776-distill-llama-70b-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use unsloth/r1-1776-distill-llama-70b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unsloth/r1-1776-distill-llama-70b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unsloth/r1-1776-distill-llama-70b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M

SGLang

How to use unsloth/r1-1776-distill-llama-70b-GGUF 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 "unsloth/r1-1776-distill-llama-70b-GGUF" \
    --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": "unsloth/r1-1776-distill-llama-70b-GGUF",
		"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 "unsloth/r1-1776-distill-llama-70b-GGUF" \
        --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": "unsloth/r1-1776-distill-llama-70b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use unsloth/r1-1776-distill-llama-70b-GGUF with Ollama:
```
ollama run hf.co/unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use unsloth/r1-1776-distill-llama-70b-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M
```

Lemonade

How to use unsloth/r1-1776-distill-llama-70b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/r1-1776-distill-llama-70b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.r1-1776-distill-llama-70b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

See our collection for versions of Deepseek-R1 including GGUF & 4-bit formats.

Unsloth's r1-1776 2-bit Dynamic Quants is selectively quantized, greatly improving accuracy over standard 1-bit/2-bit.

Finetune your own Reasoning model like R1 with Unsloth!

We have a free Google Colab notebook for turning Llama 3.1 (8B) into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports	Free Notebooks	Performance	Memory use
GRPO with Phi-4 (14B)	▶️ Start on Colab	2x faster	80% less
Llama-3.2 (3B)	▶️ Start on Colab	2.4x faster	58% less
Llama-3.2 (11B vision)	▶️ Start on Colab	2x faster	60% less
Qwen2 VL (7B)	▶️ Start on Colab	1.8x faster	60% less
Qwen2.5 (7B)	▶️ Start on Colab	2x faster	60% less
Llama-3.1 (8B)	▶️ Start on Colab	2.4x faster	58% less
Phi-3.5 (mini)	▶️ Start on Colab	2x faster	50% less
Gemma 2 (9B)	▶️ Start on Colab	2.4x faster	58% less
Mistral (7B)	▶️ Start on Colab	2.2x faster	62% less

This Llama 3.2 conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

R1 1776 Distill Llama 70B

Blog link: https://perplexity.ai/hub/blog/open-sourcing-r1-1776

This is a Llama 70B distilled version of R1 1776.

R1 1776 is a DeepSeek-R1 reasoning model that has been post-trained by Perplexity AI to remove Chinese Communist Party censorship. The model provides unbiased, accurate, and factual information while maintaining high reasoning capabilities.

Evals

To ensure our model remains fully “uncensored” and capable of engaging with a broad spectrum of sensitive topics, we curated a diverse, multilingual evaluation set of over a 1000 of examples that comprehensively cover such subjects. We then use human annotators as well as carefully designed LLM judges to measure the likelihood a model will evade or provide overly sanitized responses to the queries.

We also ensured that the model’s math and reasoning abilities remained intact after the decensoring process. Evaluations on multiple benchmarks showed that our post-trained model performed on par with the base R1 model, indicating that the decensoring had no impact on its core reasoning capabilities.

Benchmark	R1-Distill-Llama-70B	R1-1776-Distill-Llama-70B
China Censorship	80.53	0.2
Internal Benchmarks (avg)	47.64	48.4
AIME 2024	70	70
MATH-500	94.5	94.8
MMLU	88.52 *	88.40
DROP	84.55 *	84.83
GPQA	65.2	65.05

* Evaluated by Perplexity AI since they were not reported in the paper.

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GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for unsloth/r1-1776-distill-llama-70b-GGUF

Base model

perplexity-ai/r1-1776-distill-llama-70b

Quantized

(18)

this model

Paper for unsloth/r1-1776-distill-llama-70b-GGUF

DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Paper • 2501.12948 • Published Jan 22, 2025 • 462