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rostlabs
/
rost-1b-instruct-v2

Text Generation
Transformers
Safetensors
Romanian
English
rost
romanian
bilingual
nanochat
conversational
custom_code
Model card Files Files and versions
xet
Community

Instructions to use rostlabs/rost-1b-instruct-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use rostlabs/rost-1b-instruct-v2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="rostlabs/rost-1b-instruct-v2", trust_remote_code=True)
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("rostlabs/rost-1b-instruct-v2", trust_remote_code=True, device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • vLLM

    How to use rostlabs/rost-1b-instruct-v2 with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "rostlabs/rost-1b-instruct-v2"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "rostlabs/rost-1b-instruct-v2",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/rostlabs/rost-1b-instruct-v2
  • SGLang

    How to use rostlabs/rost-1b-instruct-v2 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 "rostlabs/rost-1b-instruct-v2" \
        --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": "rostlabs/rost-1b-instruct-v2",
    		"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 "rostlabs/rost-1b-instruct-v2" \
            --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": "rostlabs/rost-1b-instruct-v2",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use rostlabs/rost-1b-instruct-v2 with Docker Model Runner:

    docker model run hf.co/rostlabs/rost-1b-instruct-v2
rost-1b-instruct-v2
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  • 1 contributor
History: 7 commits
stefaniancu's picture
stefaniancu
Update README.md
53597ee verified 4 days ago
  • tokenizer
    Packaging: token IDs, raw checkpoint, nanochat tokenizer 4 days ago
  • .gitattributes
    1.52 kB
    initial commit 4 days ago
  • README.md
    12.2 kB
    Update README.md 4 days ago
  • config.json
    404 Bytes
    RoST-1B-Instruct-v2: frozen SFT-v2 arm C, step 365 4 days ago
  • configuration_rost.py
    2.61 kB
    RoST-1B-Instruct-v2: frozen SFT-v2 arm C, step 365 4 days ago
  • generation_config.json
    130 Bytes
    Packaging: token IDs, raw checkpoint, nanochat tokenizer 4 days ago
  • meta_000365.json
    1.64 kB
    Packaging: token IDs, raw checkpoint, nanochat tokenizer 4 days ago
  • model.safetensors
    2.77 GB
    xet
    RoST-1B-Instruct-v2: frozen SFT-v2 arm C, step 365 4 days ago
  • model_000365.pt
    4.23 GB
    xet
    Packaging: token IDs, raw checkpoint, nanochat tokenizer 4 days ago
  • modeling_rost.py
    14.9 kB
    RoST-1B-Instruct-v2: frozen SFT-v2 arm C, step 365 4 days ago
  • tokenizer.json
    2.35 MB
    RoST-1B-Instruct-v2: frozen SFT-v2 arm C, step 365 4 days ago
  • tokenizer_config.json
    776 Bytes
    RoST-1B-Instruct-v2: frozen SFT-v2 arm C, step 365 4 days ago