Instructions to use ajibawa-2023/carl-33b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajibawa-2023/carl-33b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ajibawa-2023/carl-33b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ajibawa-2023/carl-33b") model = AutoModelForCausalLM.from_pretrained("ajibawa-2023/carl-33b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ajibawa-2023/carl-33b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ajibawa-2023/carl-33b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ajibawa-2023/carl-33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ajibawa-2023/carl-33b
- SGLang
How to use ajibawa-2023/carl-33b 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 "ajibawa-2023/carl-33b" \ --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": "ajibawa-2023/carl-33b", "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 "ajibawa-2023/carl-33b" \ --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": "ajibawa-2023/carl-33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ajibawa-2023/carl-33b with Docker Model Runner:
docker model run hf.co/ajibawa-2023/carl-33b
Commit ·
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Parent(s): 096e032
Update README.md
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README.md
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**Training:**
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Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 75 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 by Meta.
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**Example Prompt:**
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```
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**Training:**
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Entire dataset was trained on Azure 4 x A100 80GB. For 3 epoch, training took 75 hours. DeepSpeed codebase was used for training purpose. This was trained on Llama-1 by Meta.
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**GPTQ & GGML**
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GPTQ: [TheBloke]https://huggingface.co/TheBloke/Carl-33B-GPTQ)
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GGML: [TheBloke](https://huggingface.co/TheBloke/Carl-13B-GGML)
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Special Thanks to [TheBloke](https://huggingface.co/TheBloke) for guiding me and making these models available.
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**Example Prompt:**
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```
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