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pszemraj
/
stablelm-7b-sft-v7e3-autogptq-4bit-128g

Text Generation
Transformers
English
gpt_neox
gptq
auto-gptq
quantization
sft
openassistant
Model card Files Files and versions
xet
Community

Instructions to use pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForCausalLM
    
    tokenizer = AutoTokenizer.from_pretrained("pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g")
    model = AutoModelForCausalLM.from_pretrained("pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g
  • SGLang

    How to use pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g 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 "pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g" \
        --host 0.0.0.0 \
        --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    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 "pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g" \
            --host 0.0.0.0 \
            --port 30000
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:30000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g with Docker Model Runner:

    docker model run hf.co/pszemraj/stablelm-7b-sft-v7e3-autogptq-4bit-128g
stablelm-7b-sft-v7e3-autogptq-4bit-128g
10.6 GB
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  • 1 contributor
History: 1 commit
pszemraj's picture
pszemraj
Super-squash branch 'main' using huggingface_hub
246c1fa verified 5 months ago
  • .gitattributes
    1.61 kB
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  • LOG_quant.log
    23 kB
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  • README.md
    2.12 kB
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  • config.json
    657 Bytes
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  • generation_config.json
    217 Bytes
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  • gptq_model-4bit-128g.bin

    Detected Pickle imports (7)

    • "torch.FloatStorage",
    • "torch.HalfStorage",
    • "torch.IntStorage",
    • "collections.OrderedDict",
    • "torch.BoolStorage",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.BFloat16Storage"

    What is a pickle import?

    5.28 GB
    xet
    Super-squash branch 'main' using huggingface_hub 5 months ago
  • gptq_model-4bit-128g.safetensors
    5.28 GB
    xet
    Super-squash branch 'main' using huggingface_hub 5 months ago
  • quantize_config.json
    122 Bytes
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  • special_tokens_map.json
    303 Bytes
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  • tokenizer.json
    2.11 MB
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  • tokenizer_config.json
    237 Bytes
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