Instructions to use algorythmtechnologies/Warren-8B-Uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use algorythmtechnologies/Warren-8B-Uncensored with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/teamspace/studios/this_studio/downloaded_model") model = PeftModel.from_pretrained(base_model, "algorythmtechnologies/Warren-8B-Uncensored") - Transformers
How to use algorythmtechnologies/Warren-8B-Uncensored with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="algorythmtechnologies/Warren-8B-Uncensored") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("algorythmtechnologies/Warren-8B-Uncensored", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use algorythmtechnologies/Warren-8B-Uncensored with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "algorythmtechnologies/Warren-8B-Uncensored" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "algorythmtechnologies/Warren-8B-Uncensored", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/algorythmtechnologies/Warren-8B-Uncensored
- SGLang
How to use algorythmtechnologies/Warren-8B-Uncensored 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 "algorythmtechnologies/Warren-8B-Uncensored" \ --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": "algorythmtechnologies/Warren-8B-Uncensored", "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 "algorythmtechnologies/Warren-8B-Uncensored" \ --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": "algorythmtechnologies/Warren-8B-Uncensored", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use algorythmtechnologies/Warren-8B-Uncensored with Docker Model Runner:
docker model run hf.co/algorythmtechnologies/Warren-8B-Uncensored
Download checkpoint-10/trainer_state.json from algorythmtechnologies/Warren-8B-Uncensored: direct link, hf CLI and curl.
- Browser
- Download file 2.39 kB
-
https://huggingface.co/algorythmtechnologies/Warren-8B-Uncensored/resolve/main/checkpoint-10/trainer_state.json
- Command line
-
hf download hf://algorythmtechnologies/Warren-8B-Uncensored/checkpoint-10/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/algorythmtechnologies/Warren-8B-Uncensored/resolve/main/checkpoint-10/trainer_state.json
2.39 kB
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 2.0, | |
| "eval_steps": 500, | |
| "global_step": 10, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.21052631578947367, | |
| "grad_norm": 0.8595283031463623, | |
| "learning_rate": 0.0002, | |
| "loss": 2.2998, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.42105263157894735, | |
| "grad_norm": 0.9670653939247131, | |
| "learning_rate": 0.0001866666666666667, | |
| "loss": 2.8014, | |
| "step": 2 | |
| }, | |
| { | |
| "epoch": 0.631578947368421, | |
| "grad_norm": 0.7496209144592285, | |
| "learning_rate": 0.00017333333333333334, | |
| "loss": 1.8851, | |
| "step": 3 | |
| }, | |
| { | |
| "epoch": 0.8421052631578947, | |
| "grad_norm": 1.8216716051101685, | |
| "learning_rate": 0.00016, | |
| "loss": 2.5663, | |
| "step": 4 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "grad_norm": 0.9130350947380066, | |
| "learning_rate": 0.00014666666666666666, | |
| "loss": 2.4712, | |
| "step": 5 | |
| }, | |
| { | |
| "epoch": 1.2105263157894737, | |
| "grad_norm": 0.5203133225440979, | |
| "learning_rate": 0.00013333333333333334, | |
| "loss": 1.5523, | |
| "step": 6 | |
| }, | |
| { | |
| "epoch": 1.4210526315789473, | |
| "grad_norm": 0.7877249121665955, | |
| "learning_rate": 0.00012, | |
| "loss": 1.8315, | |
| "step": 7 | |
| }, | |
| { | |
| "epoch": 1.631578947368421, | |
| "grad_norm": 0.924498975276947, | |
| "learning_rate": 0.00010666666666666667, | |
| "loss": 2.1605, | |
| "step": 8 | |
| }, | |
| { | |
| "epoch": 1.8421052631578947, | |
| "grad_norm": 0.7224551439285278, | |
| "learning_rate": 9.333333333333334e-05, | |
| "loss": 2.2001, | |
| "step": 9 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "grad_norm": 0.7880566120147705, | |
| "learning_rate": 8e-05, | |
| "loss": 2.0703, | |
| "step": 10 | |
| } | |
| ], | |
| "logging_steps": 1, | |
| "max_steps": 15, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 3, | |
| "save_steps": 10, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1179259170201600.0, | |
| "train_batch_size": 1, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |