Instructions to use IFM/AmberChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/AmberChat with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/AmberChat")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IFM/AmberChat") model = AutoModelForCausalLM.from_pretrained("IFM/AmberChat", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/AmberChat with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/AmberChat" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/AmberChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/AmberChat
- SGLang
How to use IFM/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 "IFM/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": "IFM/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 "IFM/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": "IFM/AmberChat", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/AmberChat with Docker Model Runner:
docker model run hf.co/IFM/AmberChat
| license: apache-2.0 | |
| datasets: | |
| - WizardLM/WizardLM_evol_instruct_V2_196k | |
| - icybee/share_gpt_90k_v1 | |
| language: | |
| - en | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| tags: | |
| - nlp | |
| - llm | |
| # AmberChat | |
| We present AmberChat, an instruction following model finetuned from [LLM360/Amber](https://huggingface.co/LLM360/Amber). | |
| ## Model Description | |
| - **Model type:** Language model with the same architecture as LLaMA-7B | |
| - **Language(s) (NLP):** English | |
| - **License:** Apache 2.0 | |
| - **Resources for more information:** | |
| - [Research paper](https://arxiv.org/) | |
| - [GitHub Repo](https://github.com/LLM360) | |
| - [Amber pretraining data](https://huggingface.co/) | |
| # Loading AmberChat | |
| ```python | |
| from transformers import LlamaTokenizer, LlamaForCausalLM | |
| tokenizer = LlamaTokenizer.from_pretrained("LLM360/AmberChat") | |
| model = LlamaForCausalLM.from_pretrained("LLM360/AmberChat") | |
| input_text = "How old are you?" | |
| input_ids = tokenizer(input_text, return_tensors="pt").input_ids | |
| outputs = model.generate(input_ids) | |
| print(tokenizer.decode(outputs[0])) | |
| ``` | |
| # AmberChat Finetuning Details | |
| ## DataMix | |
| | Subset | Number of rows | License | | |
| | ----------- | ----------- | ----------- | | |
| | WizardLM/WizardLM_evol_instruct_V2_196k | 143k | | | |
| | icybee/share_gpt_90k_v1 | 90k | cc0-1.0 | | |
| | Total | 233k | | | |
| ## Hyperparameters | |
| | Hyperparameter | Value | | |
| | ----------- | ----------- | | |
| | Total Parameters | 6.7B | | |
| | Hidden Size | 4096 | | |
| | Intermediate Size (MLPs) | 11008 | | |
| | Number of Attention Heads | 32 | | |
| | Number of Hidden Lyaers | 32 | | |
| | RMSNorm ɛ | 1e^-6 | | |
| | Max Seq Length | 2048 | | |
| | Vocab Size | 32000 | | |
| # Evaluation | |
| | Model | MT-Bench | | |
| |------------------------------------------------------|------------------------------------------------------------| | |
| | LLM360/Amber 359 | 2.48750 | | |
| | **LLM360/AmberChat** | **5.428125** | | |
| # Citation | |
| **BibTeX:** | |
| ```bibtex | |
| @article{xxx, | |
| title={XXX}, | |
| author={XXX}, | |
| journal={XXX}, | |
| year={2023} | |
| } | |
| ``` |