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DavidAU
/
LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning

Text Generation
Transformers
Safetensors
lfm2
thinking
reasoning
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
horror
unsloth
context 128k
conversational
Model card Files Files and versions
xet
Community
1

Instructions to use DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning")
    model = AutoModelForCausalLM.from_pretrained("DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning")
    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 DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning
  • SGLang

    How to use DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Unsloth Studio new

    How to use DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-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/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning to start chatting
    Load model with FastModel
    pip install unsloth
    from unsloth import FastModel
    model, tokenizer = FastModel.from_pretrained(
        model_name="DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning",
        max_seq_length=2048,
    )
  • Docker Model Runner

    How to use DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning with Docker Model Runner:

    docker model run hf.co/DavidAU/LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning
LFM2.5-1.2B-Instruct-Thinking-Claude-High-Reasoning
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  • 1 contributor
History: 12 commits
DavidAU's picture
DavidAU
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