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CryptoYogi
/
vazhi-v4_2-lora

Text Generation
PEFT
Safetensors
Transformers
lora
sft
trl
conversational
Model card Files Files and versions
xet
Community

Instructions to use CryptoYogi/vazhi-v4_2-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • PEFT

    How to use CryptoYogi/vazhi-v4_2-lora with PEFT:

    from peft import PeftModel
    from transformers import AutoModelForCausalLM
    
    base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
    model = PeftModel.from_pretrained(base_model, "CryptoYogi/vazhi-v4_2-lora")
  • Transformers

    How to use CryptoYogi/vazhi-v4_2-lora with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="CryptoYogi/vazhi-v4_2-lora")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("CryptoYogi/vazhi-v4_2-lora", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use CryptoYogi/vazhi-v4_2-lora with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "CryptoYogi/vazhi-v4_2-lora"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "CryptoYogi/vazhi-v4_2-lora",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/CryptoYogi/vazhi-v4_2-lora
  • SGLang

    How to use CryptoYogi/vazhi-v4_2-lora 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 "CryptoYogi/vazhi-v4_2-lora" \
        --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": "CryptoYogi/vazhi-v4_2-lora",
    		"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 "CryptoYogi/vazhi-v4_2-lora" \
            --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": "CryptoYogi/vazhi-v4_2-lora",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use CryptoYogi/vazhi-v4_2-lora with Docker Model Runner:

    docker model run hf.co/CryptoYogi/vazhi-v4_2-lora
vazhi-v4_2-lora
20.5 MB
Ctrl+K
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  • 1 contributor
History: 5 commits
CryptoYogi's picture
CryptoYogi
SFT v4.2 adapter: vanilla Qwen3-0.6B base (DAPT skipped), r=8, q_proj+v_proj, lr=5e-05, 2 epochs, 13083 samples
d81f79e verified 6 months ago
  • .gitattributes
    1.57 kB
    Training in progress, step 1635 6 months ago
  • README.md
    5.2 kB
    SFT v4.2 adapter: vanilla Qwen3-0.6B base (DAPT skipped), r=8, q_proj+v_proj, lr=5e-05, 2 epochs, 13083 samples 6 months ago
  • adapter_config.json
    968 Bytes
    Training in progress, step 1635 6 months ago
  • adapter_model.safetensors
    4.6 MB
    xet
    Model save 6 months ago
  • added_tokens.json
    707 Bytes
    Training in progress, step 1635 6 months ago
  • chat_template.jinja
    4.17 kB
    Training in progress, step 1635 6 months ago
  • merges.txt
    1.67 MB
    Training in progress, step 1635 6 months ago
  • special_tokens_map.json
    613 Bytes
    Training in progress, step 1635 6 months ago
  • tokenizer.json
    11.4 MB
    xet
    Training in progress, step 1635 6 months ago
  • tokenizer_config.json
    5.4 kB
    Training in progress, step 1635 6 months ago
  • training_args.bin
    6.23 kB
    xet
    Training in progress, step 1635 6 months ago
  • training_metadata.json
    528 Bytes
    SFT v4.2 adapter: vanilla Qwen3-0.6B base (DAPT skipped), r=8, q_proj+v_proj, lr=5e-05, 2 epochs, 13083 samples 6 months ago
  • vocab.json
    2.78 MB
    Training in progress, step 1635 6 months ago