Instructions to use md13/fia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use md13/fia with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meituan-longcat/LongCat-Flash-Lite") model = PeftModel.from_pretrained(base_model, "md13/fia") - Transformers
How to use md13/fia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="md13/fia") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("md13/fia", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use md13/fia with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "md13/fia" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "md13/fia", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/md13/fia
- SGLang
How to use md13/fia 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 "md13/fia" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "md13/fia", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "md13/fia" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "md13/fia", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use md13/fia with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for md13/fia to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for md13/fia to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for md13/fia to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="md13/fia", max_seq_length=2048, ) - Docker Model Runner
How to use md13/fia with Docker Model Runner:
docker model run hf.co/md13/fia
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 1.0, | |
| "eval_steps": 500, | |
| "global_step": 109, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.09195402298850575, | |
| "grad_norm": 1.2017886638641357, | |
| "learning_rate": 9e-06, | |
| "loss": 0.7641, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 0.1839080459770115, | |
| "grad_norm": 0.3533852696418762, | |
| "learning_rate": 1.9e-05, | |
| "loss": 0.6854, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.27586206896551724, | |
| "grad_norm": 0.3782903254032135, | |
| "learning_rate": 2.9e-05, | |
| "loss": 0.5897, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 0.367816091954023, | |
| "grad_norm": 0.3457210958003998, | |
| "learning_rate": 3.9000000000000006e-05, | |
| "loss": 0.499, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.45977011494252873, | |
| "grad_norm": 0.39099201560020447, | |
| "learning_rate": 4.9e-05, | |
| "loss": 0.5542, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.5517241379310345, | |
| "grad_norm": 0.3361814618110657, | |
| "learning_rate": 4.7183801876414294e-05, | |
| "loss": 0.4896, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 0.6436781609195402, | |
| "grad_norm": 0.26046183705329895, | |
| "learning_rate": 3.826052270299356e-05, | |
| "loss": 0.5246, | |
| "step": 70 | |
| }, | |
| { | |
| "epoch": 0.735632183908046, | |
| "grad_norm": 0.3294391334056854, | |
| "learning_rate": 2.566551303594437e-05, | |
| "loss": 0.5291, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 0.8275862068965517, | |
| "grad_norm": 0.330019474029541, | |
| "learning_rate": 1.2886228241683749e-05, | |
| "loss": 0.5042, | |
| "step": 90 | |
| }, | |
| { | |
| "epoch": 0.9195402298850575, | |
| "grad_norm": 0.30832400918006897, | |
| "learning_rate": 3.4611479651548457e-06, | |
| "loss": 0.5151, | |
| "step": 100 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 109, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 50, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 2.0348514000317645e+17, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |