Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

DavidAU
/
Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning

Text Generation
Transformers
Safetensors
NeMo
mistral
finetune
creative
creative writing
fiction writing
plot generation
sub-plot generation
story generation
scene continue
storytelling
fiction story
science fiction
romance
all genres
story
writing
vivid prose
vivid writing
fiction
roleplaying
bfloat16
swearing
rp
mistral nemo
horror
unsloth
context 128k-256k
conversational
text-generation-inference
Model card Files Files and versions
xet
Community
1

Instructions to use DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning")
    model = AutoModelForCausalLM.from_pretrained("DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning", 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=40)
    print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
  • NeMo

    How to use DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning with NeMo:

    # tag did not correspond to a valid NeMo domain.
  • Inference
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning
  • SGLang

    How to use DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning 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 "DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning" \
        --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": "DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning",
    		"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 "DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning" \
            --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": "DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Unsloth Studio

    How to use DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning with Unsloth Studio:

    Install Unsloth Studio (macOS, Linux, WSL)
    curl -fsSL https://unsloth.ai/install.sh | sh
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning to start chatting
    Install Unsloth Studio (Windows)
    irm https://unsloth.ai/install.ps1 | iex
    # Run unsloth studio
    unsloth studio -H 0.0.0.0 -p 8888
    # Then open http://localhost:8888 in your browser
    # Search for DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning",
        max_seq_length=2048,
    )
  • Docker Model Runner

    How to use DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning with Docker Model Runner:

    docker model run hf.co/DavidAU/Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning
Mistral-Nemo-Instruct-2407-12B-Thinking-HI-Claude-Opus-High-Reasoning
24.5 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 15 commits
DavidAU's picture
DavidAU
Update README.md
c01b87a verified 7 months ago
  • .gitattributes
    1.62 kB
    Upload matrix-neo.gif 8 months ago
  • README.md
    54 kB
    Update README.md 7 months ago
  • chat_template.jinja
    1.08 kB
    Upload folder using huggingface_hub 8 months ago
  • config.json
    730 Bytes
    Upload folder using huggingface_hub 8 months ago
  • generation_config.json
    121 Bytes
    Upload folder using huggingface_hub 8 months ago
  • matrix-neo.gif
    1.77 MB
    xet
    Upload matrix-neo.gif 8 months ago
  • model-00001-of-00005.safetensors
    4.87 GB
    xet
    Upload folder using huggingface_hub 8 months ago
  • model-00002-of-00005.safetensors
    4.91 GB
    xet
    Upload folder using huggingface_hub 8 months ago
  • model-00003-of-00005.safetensors
    4.91 GB
    xet
    Upload folder using huggingface_hub 8 months ago
  • model-00004-of-00005.safetensors
    4.91 GB
    xet
    Upload folder using huggingface_hub 8 months ago
  • model-00005-of-00005.safetensors
    4.91 GB
    xet
    Upload folder using huggingface_hub 8 months ago
  • model.safetensors.index.json
    30.3 kB
    Upload folder using huggingface_hub 8 months ago
  • special_tokens_map.json
    437 Bytes
    Upload folder using huggingface_hub 8 months ago
  • tokenizer.json
    17.1 MB
    xet
    Upload folder using huggingface_hub 8 months ago
  • tokenizer_config.json
    185 kB
    Upload folder using huggingface_hub 8 months ago