Instructions to use IFM/Amber with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/Amber with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/Amber")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IFM/Amber") model = AutoModelForCausalLM.from_pretrained("IFM/Amber", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/Amber with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/Amber" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/Amber", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/Amber
- SGLang
How to use IFM/Amber 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 "IFM/Amber" \ --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": "IFM/Amber", "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 "IFM/Amber" \ --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": "IFM/Amber", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/Amber with Docker Model Runner:
docker model run hf.co/IFM/Amber
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<center><img src="amber_logo.png" alt="amber logo" width="150"/></center>
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We present Amber, the first model in the LLM360 family. Amber is an
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7B English language model with the LLaMA architecture.
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## Evaluations
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| Metric | Score |
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| [Checkpoint 356](https://huggingface.co/LLM360/Amber/tree/ckpt_356) | [Checkpoint 351](https://huggingface.co/LLM360/Amber/tree/ckpt_351) |
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| [Checkpoint 355](https://huggingface.co/LLM360/Amber/tree/ckpt_355) | [Checkpoint 350](https://huggingface.co/LLM360/Amber/tree/ckpt_350) |
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| [Checkpoint 354](https://huggingface.co/LLM360/Amber/tree/ckpt_354) | [Checkpoint 349](https://huggingface.co/LLM360/Amber/tree/ckpt_349) |
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- To downloading other checkpoints, change the branch from 'main' to the checkpoint you want (e.g. 'ckpt_000').
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- This is completed on the 'Files and versions' tab (to the right of the Model Card).
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# 🟠 Amber Training Details
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## Datasets and Mix
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<center><img src="amber_logo.png" alt="amber logo" width="150"/></center>
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We present Amber, the first model in the LLM360 family. Amber is an
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7B English language model with the LLaMA architecture. 360 model checkpoints and the full data sequence are available under the Apache 2.0 license.
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## Evaluations
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| Metric | Score |
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| [Checkpoint 356](https://huggingface.co/LLM360/Amber/tree/ckpt_356) | [Checkpoint 351](https://huggingface.co/LLM360/Amber/tree/ckpt_351) |
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| [Checkpoint 355](https://huggingface.co/LLM360/Amber/tree/ckpt_355) | [Checkpoint 350](https://huggingface.co/LLM360/Amber/tree/ckpt_350) |
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| [Checkpoint 354](https://huggingface.co/LLM360/Amber/tree/ckpt_354) | [Checkpoint 349](https://huggingface.co/LLM360/Amber/tree/ckpt_349) |
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- 360 checkpoints are available for download
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- To downloading other checkpoints, change the branch from 'main' to the checkpoint you want (e.g. 'ckpt_000').
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- This is completed on the 'Files and versions' tab (to the right of the Model Card).
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# 🟠 Amber Training Details
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## Datasets and Mix
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[Fully processed Amber pretraining data](https://huggingface.co/datasets/LLM360/AmberDatasets)
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| Subset | Tokens (Billion) |
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| Arxiv | 30.00 |
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