Instructions to use awilliamson/wholism with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use awilliamson/wholism with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="awilliamson/wholism")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("awilliamson/wholism") model = AutoModelForCausalLM.from_pretrained("awilliamson/wholism") - llama-cpp-python
How to use awilliamson/wholism with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="awilliamson/wholism", filename="ggml-model-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use awilliamson/wholism 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 awilliamson/wholism:F16 # Run inference directly in the terminal: llama cli -hf awilliamson/wholism:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf awilliamson/wholism:F16 # Run inference directly in the terminal: llama cli -hf awilliamson/wholism:F16
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 awilliamson/wholism:F16 # Run inference directly in the terminal: ./llama-cli -hf awilliamson/wholism:F16
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 awilliamson/wholism:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf awilliamson/wholism:F16
Use Docker
docker model run hf.co/awilliamson/wholism:F16
- LM Studio
- Jan
- vLLM
How to use awilliamson/wholism with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "awilliamson/wholism" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "awilliamson/wholism", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/awilliamson/wholism:F16
- SGLang
How to use awilliamson/wholism 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 "awilliamson/wholism" \ --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": "awilliamson/wholism", "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 "awilliamson/wholism" \ --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": "awilliamson/wholism", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use awilliamson/wholism with Ollama:
ollama run hf.co/awilliamson/wholism:F16
- Unsloth Studio
How to use awilliamson/wholism 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 awilliamson/wholism 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 awilliamson/wholism to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for awilliamson/wholism to start chatting
- Atomic Chat new
- Docker Model Runner
How to use awilliamson/wholism with Docker Model Runner:
docker model run hf.co/awilliamson/wholism:F16
- Lemonade
How to use awilliamson/wholism with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull awilliamson/wholism:F16
Run and chat with the model
lemonade run user.wholism-F16
List all available models
lemonade list
library_name: peft
tags:
- generated_from_trainer
base_model: awilliamson/tinyllama-slider
model-index:
- name: empathy-lora-out
results: []
empathy-lora-out
This model is a fine-tuned version of awilliamson/tinyllama-slider on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2988
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.5961 | 0.03 | 1 | 1.5286 |
| 1.6946 | 0.27 | 8 | 1.5283 |
| 1.5327 | 0.54 | 16 | 1.5186 |
| 1.4197 | 0.81 | 24 | 1.4726 |
| 1.4979 | 1.03 | 32 | 1.4323 |
| 1.3198 | 1.3 | 40 | 1.3997 |
| 1.4632 | 1.57 | 48 | 1.3762 |
| 1.3486 | 1.84 | 56 | 1.3547 |
| 1.3901 | 2.08 | 64 | 1.3386 |
| 1.3964 | 2.35 | 72 | 1.3291 |
| 1.3844 | 2.62 | 80 | 1.3184 |
| 1.2799 | 2.89 | 88 | 1.3108 |
| 1.3213 | 3.13 | 96 | 1.3061 |
| 1.0652 | 3.39 | 104 | 1.3050 |
| 1.1837 | 3.66 | 112 | 1.3024 |
| 1.4713 | 3.93 | 120 | 1.2992 |
| 1.3074 | 4.17 | 128 | 1.2979 |
| 1.2039 | 4.44 | 136 | 1.2993 |
| 1.1574 | 4.71 | 144 | 1.2988 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
Framework versions
- PEFT 0.6.0