Instructions to use LLM360/AmberChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM360/AmberChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM360/AmberChat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LLM360/AmberChat") model = AutoModelForCausalLM.from_pretrained("LLM360/AmberChat") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LLM360/AmberChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM360/AmberChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM360/AmberChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM360/AmberChat
- SGLang
How to use LLM360/AmberChat 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 "LLM360/AmberChat" \ --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": "LLM360/AmberChat", "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 "LLM360/AmberChat" \ --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": "LLM360/AmberChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM360/AmberChat with Docker Model Runner:
docker model run hf.co/LLM360/AmberChat
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We present AmberChat, an instruction following model finetuned from [LLM360/Amber](https://huggingface.co/LLM360/Amber).
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## Model Description
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- **Model type:** Language model with the same architecture as LLaMA-7B
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# Evaluation
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| Model | MT-Bench |
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| **LLM360/AmberChat** | **5.428125** |
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| [LLM360/Amber](https://huggingface.co/LLM360/Amber) | 2.48750 |
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| [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) | 5.17 |
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| [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat) | 5.42 |
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| [Nous-Hermes-13B](https://huggingface.co/NousResearch/Nous-Hermes-13b) | 5.51 |
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# Using Quantized Models with Ollama
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We present AmberChat, an instruction following model finetuned from [LLM360/Amber](https://huggingface.co/LLM360/Amber).
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# Evaluation
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| Model | MT-Bench |
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| **LLM360/AmberChat** | **5.428125** |
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| [LLM360/Amber](https://huggingface.co/LLM360/Amber) | 2.48750 |
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| [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) | 5.17 |
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| [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat) | 5.42 |
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| [Nous-Hermes-13B](https://huggingface.co/NousResearch/Nous-Hermes-13b) | 5.51 |
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## Model Description
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- **Model type:** Language model with the same architecture as LLaMA-7B
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| model_max_length | 2048 |
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# Using Quantized Models with Ollama
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