Instructions to use androlike/astramix_l2_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use androlike/astramix_l2_7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="androlike/astramix_l2_7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("androlike/astramix_l2_7b") model = AutoModelForCausalLM.from_pretrained("androlike/astramix_l2_7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use androlike/astramix_l2_7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "androlike/astramix_l2_7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "androlike/astramix_l2_7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/androlike/astramix_l2_7b
- SGLang
How to use androlike/astramix_l2_7b 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 "androlike/astramix_l2_7b" \ --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": "androlike/astramix_l2_7b", "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 "androlike/astramix_l2_7b" \ --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": "androlike/astramix_l2_7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use androlike/astramix_l2_7b with Docker Model Runner:
docker model run hf.co/androlike/astramix_l2_7b
Introduction
Astramix is a merge of various Llama-2-7b finetune models, using ties-merge method, thanks for Chargoddard. Subsequently, lora merge script was used, created by zarakiquemparte (link to the Hugging Face profile).
Approximate rating for this model, noticed in short-time use:
- Great roleplay capabilities, limited only by model parameters.
- Poor quality reasoning, because of the model limitations.
- Almost censorship absence (yet some parts can be generated in the output).
Feel free to test the model.
Following base model used for merge: Llama-2-7B-fp16
List of models used for merge:
Then, two LoRAs was merged into basemix model, using script specified above:
- limarp-llama2-v2 (Licensed under AGPLv3)
- airoboros-lmoe-7b-2.1 (Utilizing creative version)
Here is quantized versions of the model:
I suggest using Alpaca instruct format:
### Instruction:
(your instruct prompt is here)
### Response:
Limitations and risks
Llama2 and its derivatives (finetunes) is licensed under LLama 2 Community License, various finetunes or (Q)LoRAs has appropriate licenses depending on used datasets in finetuning or training Low-Rank Adaptations. This mix can generate heavily biased output, which aren't suitable for minors or common audience due to using limarp in the merge.
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