Instructions to use nathishdev/destiin-email-agent-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nathishdev/destiin-email-agent-v3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-3b-instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "nathishdev/destiin-email-agent-v3") - Transformers
How to use nathishdev/destiin-email-agent-v3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nathishdev/destiin-email-agent-v3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nathishdev/destiin-email-agent-v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use nathishdev/destiin-email-agent-v3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nathishdev/destiin-email-agent-v3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nathishdev/destiin-email-agent-v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nathishdev/destiin-email-agent-v3
- SGLang
How to use nathishdev/destiin-email-agent-v3 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 "nathishdev/destiin-email-agent-v3" \ --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": "nathishdev/destiin-email-agent-v3", "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 "nathishdev/destiin-email-agent-v3" \ --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": "nathishdev/destiin-email-agent-v3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use nathishdev/destiin-email-agent-v3 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 nathishdev/destiin-email-agent-v3 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 nathishdev/destiin-email-agent-v3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nathishdev/destiin-email-agent-v3 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="nathishdev/destiin-email-agent-v3", max_seq_length=2048, ) - Docker Model Runner
How to use nathishdev/destiin-email-agent-v3 with Docker Model Runner:
docker model run hf.co/nathishdev/destiin-email-agent-v3
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 3.0, | |
| "eval_steps": 500, | |
| "global_step": 252, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.12012012012012012, | |
| "grad_norm": 0.6892135739326477, | |
| "learning_rate": 9e-05, | |
| "loss": 0.7404771327972413, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 0.24024024024024024, | |
| "grad_norm": 0.6579736471176147, | |
| "learning_rate": 9.965912123286424e-05, | |
| "loss": 0.6771039962768555, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.36036036036036034, | |
| "grad_norm": 0.6196520328521729, | |
| "learning_rate": 9.84867408817022e-05, | |
| "loss": 0.5915464878082275, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 0.4804804804804805, | |
| "grad_norm": 0.641204833984375, | |
| "learning_rate": 9.649837585596444e-05, | |
| "loss": 0.47760705947875975, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.6006006006006006, | |
| "grad_norm": 0.7001290321350098, | |
| "learning_rate": 9.372748840448361e-05, | |
| "loss": 0.4462123394012451, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.7207207207207207, | |
| "grad_norm": 0.8124980926513672, | |
| "learning_rate": 9.022070986717379e-05, | |
| "loss": 0.44390106201171875, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 0.8408408408408409, | |
| "grad_norm": 0.7991748452186584, | |
| "learning_rate": 8.603705591494916e-05, | |
| "loss": 0.36723716259002687, | |
| "step": 70 | |
| }, | |
| { | |
| "epoch": 0.960960960960961, | |
| "grad_norm": 0.6478794813156128, | |
| "learning_rate": 8.124693337340092e-05, | |
| "loss": 0.35840682983398436, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 1.072072072072072, | |
| "grad_norm": 0.6842944025993347, | |
| "learning_rate": 7.593095534442388e-05, | |
| "loss": 0.31162774562835693, | |
| "step": 90 | |
| }, | |
| { | |
| "epoch": 1.1921921921921923, | |
| "grad_norm": 0.8257659077644348, | |
| "learning_rate": 7.017858456614284e-05, | |
| "loss": 0.2912218332290649, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 1.3123123123123124, | |
| "grad_norm": 1.2205647230148315, | |
| "learning_rate": 6.408662784207149e-05, | |
| "loss": 0.2723785638809204, | |
| "step": 110 | |
| }, | |
| { | |
| "epoch": 1.4324324324324325, | |
| "grad_norm": 0.6746923923492432, | |
| "learning_rate": 5.775760687679603e-05, | |
| "loss": 0.2832528352737427, | |
| "step": 120 | |
| }, | |
| { | |
| "epoch": 1.5525525525525525, | |
| "grad_norm": 0.7994391322135925, | |
| "learning_rate": 5.129803293543392e-05, | |
| "loss": 0.2766345262527466, | |
| "step": 130 | |
| }, | |
| { | |
| "epoch": 1.6726726726726726, | |
| "grad_norm": 1.2023299932479858, | |
| "learning_rate": 4.481661436267057e-05, | |
| "loss": 0.24401814937591554, | |
| "step": 140 | |
| }, | |
| { | |
| "epoch": 1.7927927927927927, | |
| "grad_norm": 0.793181836605072, | |
| "learning_rate": 3.842242712708444e-05, | |
| "loss": 0.2846501588821411, | |
| "step": 150 | |
| }, | |
| { | |
| "epoch": 1.9129129129129128, | |
| "grad_norm": 0.768040657043457, | |
| "learning_rate": 3.222307917871977e-05, | |
| "loss": 0.2684438467025757, | |
| "step": 160 | |
| }, | |
| { | |
| "epoch": 2.024024024024024, | |
| "grad_norm": 0.673546314239502, | |
| "learning_rate": 2.632289951198285e-05, | |
| "loss": 0.23772306442260743, | |
| "step": 170 | |
| }, | |
| { | |
| "epoch": 2.144144144144144, | |
| "grad_norm": 0.7624714374542236, | |
| "learning_rate": 2.0821182410171636e-05, | |
| "loss": 0.17041124105453492, | |
| "step": 180 | |
| }, | |
| { | |
| "epoch": 2.264264264264264, | |
| "grad_norm": 0.5443869829177856, | |
| "learning_rate": 1.5810516419294652e-05, | |
| "loss": 0.1947704553604126, | |
| "step": 190 | |
| }, | |
| { | |
| "epoch": 2.3843843843843846, | |
| "grad_norm": 1.2833149433135986, | |
| "learning_rate": 1.1375226172924341e-05, | |
| "loss": 0.19111917018890381, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 2.5045045045045047, | |
| "grad_norm": 1.1426626443862915, | |
| "learning_rate": 7.589953290656532e-06, | |
| "loss": 0.20885732173919677, | |
| "step": 210 | |
| }, | |
| { | |
| "epoch": 2.6246246246246248, | |
| "grad_norm": 0.6546122431755066, | |
| "learning_rate": 4.5184002322740785e-06, | |
| "loss": 0.19033702611923217, | |
| "step": 220 | |
| }, | |
| { | |
| "epoch": 2.744744744744745, | |
| "grad_norm": 0.9988263249397278, | |
| "learning_rate": 2.212258247327059e-06, | |
| "loss": 0.13631391525268555, | |
| "step": 230 | |
| }, | |
| { | |
| "epoch": 2.864864864864865, | |
| "grad_norm": 1.021765112876892, | |
| "learning_rate": 7.103374616951042e-07, | |
| "loss": 0.20375282764434816, | |
| "step": 240 | |
| }, | |
| { | |
| "epoch": 2.984984984984985, | |
| "grad_norm": 0.867516815662384, | |
| "learning_rate": 3.791374092859679e-08, | |
| "loss": 0.18663368225097657, | |
| "step": 250 | |
| } | |
| ], | |
| "logging_steps": 10, | |
| "max_steps": 252, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": true | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 2.6648567674454016e+16, | |
| "train_batch_size": 1, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |