Text Generation
Transformers
TensorBoard
Safetensors
GGUF
PEFT
mistral
Trained with AutoTrain
text-generation-inference
conversational
imatrix
Instructions to use Tilo15/cosmic-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tilo15/cosmic-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tilo15/cosmic-2") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Tilo15/cosmic-2") model = AutoModelForCausalLM.from_pretrained("Tilo15/cosmic-2", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - PEFT
How to use Tilo15/cosmic-2 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Tilo15/cosmic-2 with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Tilo15/cosmic-2:IQ1_M # Run inference directly in the terminal: llama cli -hf Tilo15/cosmic-2:IQ1_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Tilo15/cosmic-2:IQ1_M # Run inference directly in the terminal: llama cli -hf Tilo15/cosmic-2:IQ1_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Tilo15/cosmic-2:IQ1_M # Run inference directly in the terminal: ./llama-cli -hf Tilo15/cosmic-2:IQ1_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Tilo15/cosmic-2:IQ1_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Tilo15/cosmic-2:IQ1_M
Use Docker
docker model run hf.co/Tilo15/cosmic-2:IQ1_M
- LM Studio
- Jan
- vLLM
How to use Tilo15/cosmic-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tilo15/cosmic-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tilo15/cosmic-2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Tilo15/cosmic-2:IQ1_M
- SGLang
How to use Tilo15/cosmic-2 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 "Tilo15/cosmic-2" \ --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": "Tilo15/cosmic-2", "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 "Tilo15/cosmic-2" \ --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": "Tilo15/cosmic-2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Tilo15/cosmic-2 with Ollama:
ollama run hf.co/Tilo15/cosmic-2:IQ1_M
- Unsloth Studio
How to use Tilo15/cosmic-2 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 Tilo15/cosmic-2 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 Tilo15/cosmic-2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Tilo15/cosmic-2 to start chatting
- Docker Model Runner
How to use Tilo15/cosmic-2 with Docker Model Runner:
docker model run hf.co/Tilo15/cosmic-2:IQ1_M
- Lemonade
How to use Tilo15/cosmic-2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Tilo15/cosmic-2:IQ1_M
Run and chat with the model
lemonade run user.cosmic-2-IQ1_M
List all available models
lemonade list
- Atomic Chat
| { | |
| "model": "Tilo15/big-text-3", | |
| "project_name": "cosmic-2", | |
| "data_path": "cosmic-2/autotrain-data", | |
| "train_split": "train", | |
| "valid_split": null, | |
| "add_eos_token": true, | |
| "block_size": 1024, | |
| "model_max_length": 2048, | |
| "padding": "right", | |
| "trainer": "sft", | |
| "use_flash_attention_2": false, | |
| "log": "tensorboard", | |
| "disable_gradient_checkpointing": false, | |
| "logging_steps": -1, | |
| "eval_strategy": "epoch", | |
| "save_total_limit": 1, | |
| "auto_find_batch_size": false, | |
| "mixed_precision": "fp16", | |
| "lr": 3e-05, | |
| "epochs": 3, | |
| "batch_size": 2, | |
| "warmup_ratio": 0.1, | |
| "gradient_accumulation": 4, | |
| "optimizer": "adamw_torch", | |
| "scheduler": "linear", | |
| "weight_decay": 0.0, | |
| "max_grad_norm": 1.0, | |
| "seed": 42, | |
| "chat_template": "none", | |
| "quantization": "int4", | |
| "target_modules": "all-linear", | |
| "merge_adapter": false, | |
| "peft": true, | |
| "lora_r": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "model_ref": null, | |
| "dpo_beta": 0.1, | |
| "max_prompt_length": 128, | |
| "max_completion_length": null, | |
| "prompt_text_column": "autotrain_prompt", | |
| "text_column": "autotrain_text", | |
| "rejected_text_column": "autotrain_rejected_text", | |
| "push_to_hub": true, | |
| "username": "Tilo15", | |
| "unsloth": true | |
| } |