Instructions to use AxeronAI/axeron-mf-29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AxeronAI/axeron-mf-29 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AxeronAI/axeron-mf-29")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AxeronAI/axeron-mf-29") model = AutoModelForCausalLM.from_pretrained("AxeronAI/axeron-mf-29", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AxeronAI/axeron-mf-29 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AxeronAI/axeron-mf-29" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AxeronAI/axeron-mf-29", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AxeronAI/axeron-mf-29
- SGLang
How to use AxeronAI/axeron-mf-29 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 "AxeronAI/axeron-mf-29" \ --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": "AxeronAI/axeron-mf-29", "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 "AxeronAI/axeron-mf-29" \ --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": "AxeronAI/axeron-mf-29", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AxeronAI/axeron-mf-29 with Docker Model Runner:
docker model run hf.co/AxeronAI/axeron-mf-29
| base_model: | |
| - meta-llama/Llama-3.1-8B-Instruct | |
| - meta-llama/Llama-3.1-8B | |
| library_name: transformers | |
| tags: | |
| - forgelm | |
| - forge | |
| # axeron-29 | |
| This is a forge of pre-trained language models created using [forgelm](https://github.com/AxeronAI/forgelm). | |
| ## Forge Details | |
| ### Forge Method | |
| This model was forged using the [SLERP](https://en.wikipedia.org/wiki/Slerp) forge method. | |
| ### Models Forged | |
| The following models were included in the forge: | |
| * [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | |
| * [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| base_model: meta-llama/Llama-3.1-8B | |
| dtype: bfloat16 | |
| forge_method: slerp | |
| modules: | |
| default: | |
| slices: | |
| - sources: | |
| - layer_range: [0, 32] | |
| model: meta-llama/Llama-3.1-8B | |
| - layer_range: [0, 32] | |
| model: meta-llama/Llama-3.1-8B-Instruct | |
| parameters: | |
| t: 0.5 | |
| tokenizer: | |
| source: base | |
| ``` | |