Text Generation
Transformers
Safetensors
llama
fine-tuning
prose
GRPO
axolotl
finetune
roleplaying
creative-writing
conversational
text-generation-inference
Instructions to use Delta-Vector/Nanuq-R1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Delta-Vector/Nanuq-R1-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Delta-Vector/Nanuq-R1-9B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Nanuq-R1-9B") model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Nanuq-R1-9B") 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 Delta-Vector/Nanuq-R1-9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Delta-Vector/Nanuq-R1-9B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Delta-Vector/Nanuq-R1-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Delta-Vector/Nanuq-R1-9B
- SGLang
How to use Delta-Vector/Nanuq-R1-9B 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 "Delta-Vector/Nanuq-R1-9B" \ --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": "Delta-Vector/Nanuq-R1-9B", "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 "Delta-Vector/Nanuq-R1-9B" \ --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": "Delta-Vector/Nanuq-R1-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Delta-Vector/Nanuq-R1-9B with Docker Model Runner:
docker model run hf.co/Delta-Vector/Nanuq-R1-9B
| base_model: | |
| - Delta-Vector/Austral-Xgen-9B-Winton | |
| library_name: transformers | |
| tags: | |
| - fine-tuning | |
| - prose | |
| - GRPO | |
| - axolotl | |
| - finetune | |
| - roleplaying | |
| - creative-writing | |
| datasets: | |
| - PocketDoc/Dans-Systemmaxx | |
| <style> | |
| body { | |
| font-family: 'Quicksand', sans-serif; | |
| background: linear-gradient(135deg, #f9ffd1 0%, #e2fab5 100%); | |
| color: #000000; | |
| margin: 0; | |
| padding: 0; | |
| font-size: 16px; | |
| } | |
| .container { | |
| margin: 20px; | |
| background-color: rgba(255, 255, 255, 0.9); | |
| padding: 20px; | |
| border-radius: 12px; | |
| box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3); | |
| border: 3px solid #000000; | |
| position: relative; | |
| } | |
| .header h1 { | |
| font-size: 28px; | |
| color: #000000; | |
| margin: 0 0 20px 0; | |
| text-align: center; | |
| text-decoration: underline; | |
| } | |
| .section { | |
| margin-top: 30px; | |
| } | |
| .section h2 { | |
| font-size: 24px; | |
| color: #000000; | |
| text-align: center; | |
| text-decoration: underline; | |
| } | |
| .info p { | |
| color: #000000; | |
| line-height: 1.6; | |
| font-size: 16px; | |
| } | |
| .info img { | |
| width: 85%; | |
| border-radius: 10px; | |
| margin: 0 auto 15px; | |
| display: block; | |
| box-shadow: 0 0 20px rgba(0, 0, 0, 0.3); | |
| border: 1px solid #000000; | |
| } | |
| a { | |
| color: #000000; | |
| text-decoration: none; | |
| transition: color 0.2s ease; | |
| } | |
| a:hover { | |
| color: #538125; | |
| } | |
| .button { | |
| display: inline-block; | |
| background-color: rgba(106, 168, 79, 0.8); | |
| color: #000000; | |
| padding: 10px 20px; | |
| border-radius: 5px; | |
| cursor: pointer; | |
| text-decoration: none; | |
| transition: background-color 0.2s ease, box-shadow 0.2s ease; | |
| } | |
| .button:hover { | |
| background-color: #538125; | |
| box-shadow: 0 0 15px rgba(106, 168, 79, 0.5); | |
| } | |
| pre { | |
| background-color: rgba(240, 248, 225, 0.95); | |
| padding: 15px; | |
| border-radius: 5px; | |
| overflow-x: auto; | |
| border: 1px solid #000000; | |
| } | |
| code { | |
| font-family: 'Courier New', monospace; | |
| color: #000000; | |
| } | |
| .info-card { | |
| background: rgba(249, 255, 235, 0.95); | |
| border: 1px solid #000000; | |
| border-radius: 8px; | |
| overflow: hidden; | |
| } | |
| .info-header { | |
| background: rgba(106, 168, 79, 0.1); | |
| padding: 20px; | |
| border-bottom: 1px solid #000000; | |
| } | |
| .info-header h3 { | |
| color: #000000; | |
| margin: 0 0 10px 0; | |
| font-size: 20px; | |
| text-align: center; | |
| text-decoration: underline; | |
| } | |
| .model-tags { | |
| display: flex; | |
| gap: 8px; | |
| flex-wrap: wrap; | |
| } | |
| .model-tag { | |
| background: rgba(106, 168, 79, 0.1); | |
| color: #000000; | |
| padding: 4px 8px; | |
| border-radius: 4px; | |
| font-size: 12px; | |
| border: 1px solid #000000; | |
| } | |
| .model-composition { | |
| padding: 20px; | |
| border-bottom: 1px solid #000000; | |
| } | |
| .model-composition h4 { | |
| color: #000000; | |
| margin: 0 0 15px 0; | |
| font-size: 16px; | |
| text-align: center; | |
| text-decoration: underline; | |
| } | |
| .composition-list { | |
| list-style: none; | |
| padding: 0; | |
| margin: 0; | |
| display: grid; | |
| gap: 10px; | |
| } | |
| .composition-list li { | |
| color: #000000; | |
| display: flex; | |
| align-items: baseline; | |
| gap: 8px; | |
| } | |
| .model-component { | |
| font-weight: 500; | |
| min-width: 120px; | |
| } | |
| .model-description { | |
| padding: 20px; | |
| background: rgba(255, 255, 255, 0.5); | |
| } | |
| .metrics-section { | |
| margin-bottom: 30px; | |
| } | |
| .metrics-section details { | |
| background: rgba(249, 255, 235, 0.95); | |
| border: 1px solid #000000; | |
| border-radius: 8px; | |
| padding: 15px; | |
| margin-bottom: 15px; | |
| } | |
| .metrics-section summary { | |
| color: #000000; | |
| font-size: 18px; | |
| cursor: pointer; | |
| outline: none; | |
| padding: 5px 0; | |
| text-align: center; | |
| } | |
| .creator-section { | |
| margin: 20px 0; | |
| } | |
| .creator-badge { | |
| display: inline-flex; | |
| align-items: center; | |
| background: rgba(249, 255, 235, 0.95); | |
| border: 1px solid #000000; | |
| border-radius: 8px; | |
| padding: 10px 15px; | |
| } | |
| .creator-label { | |
| color: #000000; | |
| font-size: 14px; | |
| margin-right: 8px; | |
| } | |
| .creator-link { | |
| display: flex; | |
| align-items: center; | |
| gap: 5px; | |
| color: #000000; | |
| text-decoration: none; | |
| transition: all 0.2s ease; | |
| } | |
| .creator-name { | |
| font-weight: 600; | |
| } | |
| .creator-arrow { | |
| font-size: 16px; | |
| transition: transform 0.2s ease; | |
| } | |
| .creator-link:hover .creator-arrow { | |
| transform: translateX(3px); | |
| } | |
| .link-arrow { | |
| display: inline-block; | |
| transition: transform 0.2s ease; | |
| } | |
| a:hover .link-arrow { | |
| transform: translateX(3px); | |
| } | |
| .axolotl-container { | |
| text-align: center; | |
| margin: 30px 0; | |
| display: flex; | |
| justify-content: center; | |
| align-items: center; | |
| } | |
| .axolotl-container img { | |
| max-width: 200px; | |
| width: 200px; | |
| height: auto; | |
| border-radius: 8px; | |
| box-shadow: 0 2px 10px rgba(0, 0, 0, 0.3); | |
| border: 1px solid #000000; | |
| background-color: #000000; | |
| padding: 15px; | |
| object-fit: contain; | |
| display: block; | |
| margin: 0 auto; | |
| } | |
| </style> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Nanuq-R1 9B</title> | |
| <link href="https://fonts.googleapis.com/css2?family=Quicksand:wght@400;500;600&display=swap" rel="stylesheet"> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <div class="header"> | |
| <h1>Nanuq-R1 9B</h1> | |
| </div> | |
| <div class="info"> | |
| <img src="https://files.catbox.moe/i77ubj.webp" alt="Model banner"> | |
| <div style="text-align: center;"> | |
| <div class="creator-section"> | |
| <div class="creator-badge"> | |
| <span class="creator-label">Created by</span> | |
| <a href="https://huggingface.co/Delta-Vector" target="_blank" class="creator-link"> | |
| <span class="creator-name">Delta-Vector</span> | |
| <span class="creator-arrow">→</span> | |
| </a> | |
| </div> | |
| </div> | |
| <div class="model-info"> | |
| <h2>Model Information</h2> | |
| <div class="info-card"> | |
| <div class="info-header"> | |
| <h3>Nanuq-R1 9B</h3> | |
| <div class="model-tags"> | |
| <span class="model-tag">GRPO Experiment</span> | |
| <span class="model-tag">Austral finetune</span> | |
| <span class="model-tag">Creative & Refreshing Prose with deep IF.</span> | |
| </div> | |
| </div> | |
| <div class="model-description"> | |
| <p>A sequel! The new Nanuq series is meant to be as a testing grounds for my GRPO experiments, This model is meant to have great Instruct Following and System prompt Adherence in Creative Scenarios.</p> | |
| <p>Built ontop of Austral Xgen 9B, I made an RL env using PrimeIntellect-ai/verifiers and implemented InternLM/POLAR in said env, then using Pocketdoc's Systemmax dataset, I finetuned the model for 150 steps and this was the result.</p> | |
| <p>There's alot of things i could do different, As the reward almost falls flat as soon as you get out of warm-up but this model was pretty decent so decided to release it, Hope people enjoy it!</p> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="section"> | |
| <h2>Quantized Versions</h2> | |
| <div class="info-card"> | |
| <div class="model-composition"> | |
| <h4>Available Downloads</h4> | |
| <ul class="composition-list"> | |
| <li><span class="model-component"><a href="" target="_blank">GGUF Format</a></span>For use with LLama.cpp & Forks(Coming Soon!)</li> | |
| <li><span class="model-component"><a href="" target="_blank">EXL2 Format</a></span>For use with TabbyAPI (Coming soon!)</li> | |
| </ul> | |
| </div> | |
| </div> | |
| </div> | |
| <div class="section"> | |
| <h2>Prompting</h2> | |
| <p>Model has been tuned with the ChatML formatting. A typical input would look like this:</p> | |
| <pre><code>"""<|im_start|>user | |
| Hi there!<|im_end|> | |
| <|im_start|>assistant | |
| Nice to meet you!<|im_end|> | |
| <|im_start|>user | |
| Can I ask a question?<|im_end|> | |
| <|im_start|>assistant | |
| """</code></pre> | |
| </div> | |
| <div class="section"> | |
| <h2>System Prompting</h2> | |
| <p>I would highly recommend using either Euryale's system prompt or the EVA system prompt with the model.</p> | |
| <div class="metrics-section"> | |
| <details> | |
| <summary>See Sao10k's Euryale System Prompt</summary> | |
| <pre><code>Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}. | |
| <Guidelines> | |
| • Maintain the character persona but allow it to evolve with the story. | |
| • Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant. | |
| • All types of outputs are encouraged; respond accordingly to the narrative. | |
| • Include dialogues, actions, and thoughts in each response. | |
| • Utilize all five senses to describe scenarios within {{char}}'s dialogue. | |
| • Use emotional symbols such as "!" and "~" in appropriate contexts. | |
| • Incorporate onomatopoeia when suitable. | |
| • Allow time for {{user}} to respond with their own input, respecting their agency. | |
| • Act as secondary characters and NPCs as needed, and remove them when appropriate. | |
| • When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}. | |
| </Guidelines> | |
| <Forbidden> | |
| • Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona. | |
| • Writing for, speaking, thinking, acting, or replying as {{user}} in your response. | |
| • Repetitive and monotonous outputs. | |
| • Positivity bias in your replies. | |
| • Being overly extreme or NSFW when the narrative context is inappropriate. | |
| </Forbidden> | |
| Follow the instructions in <Guidelines></Guidelines>, avoiding the items listed in <Forbidden></Forbidden>.</code></pre> | |
| </details> | |
| </div> | |
| </div> | |
| <div class="section"> | |
| <h2>Training</h2> | |
| <p>The training was done for 150 steps using 8 x <a href="https://www.nvidia.com/en-us/data-center/h200/">H200s</a> GPUs for the fine-tuning of the model.</p> | |
| <p style="text-align: center; margin-top: 20px;"> | |
| <div class="axolotl-container"> | |
| <a href="https://github.com/PrimeIntellect-ai/verifiers" target="_blank"> | |
| <img src="https://files.catbox.moe/j36qbe.png" alt="Built with Verifiers"> | |
| </a> | |
| </div> | |
| <div class="section"> | |
| <h2>Credits</h2> | |
| <p>Thank you to <a href="https://huggingface.co/intervitens">Intervitens</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/kubernetes-bad">Kubernetes Bad</a>, <a href="https://huggingface.co/cgato">Cgato</a>, <a href="https://huggingface.co/snwy">Snwy</a>, <a href="https://huggingface.co/AuriAetherwiing">Auri</a>, <a href="https://x.com/willccbb">Will Brown</a> and most of all: <a href="https://huggingface.co/kalomaze">Kalomaze</a> </p> | |
| </div> | |
| </div> | |
| </div> |