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DivyaRani
/
TallyAssistant

Text Generation
Transformers
Safetensors
English
gpt2
distilgpt2
knowledge-distillation
tally
accounting
conversational
business
transformer
language-model
text-generation-inference
Model card Files Files and versions
xet
Community

Instructions to use DivyaRani/TallyAssistant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use DivyaRani/TallyAssistant with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="DivyaRani/TallyAssistant")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("DivyaRani/TallyAssistant")
    model = AutoModelForCausalLM.from_pretrained("DivyaRani/TallyAssistant")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use DivyaRani/TallyAssistant with vLLM:

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

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

    How to use DivyaRani/TallyAssistant with Docker Model Runner:

    docker model run hf.co/DivyaRani/TallyAssistant
TallyAssistant
331 MB
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  • 1 contributor
History: 3 commits
DivyaRani's picture
DivyaRani
Create README.md
0ea66b0 verified 9 months ago
  • .gitattributes
    1.52 kB
    initial commit 9 months ago
  • README.md
    1.6 kB
    Create README.md 9 months ago
  • config.json
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  • evaluation_responses.json
    117 kB
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  • generation_config.json
    119 Bytes
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  • merges.txt
    456 kB
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  • model.safetensors
    328 MB
    xet
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  • special_tokens_map.json
    131 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    476 Bytes
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  • training_config.json
    1.41 kB
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  • vocab.json
    798 kB
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