Text Generation
Transformers
Safetensors
GGUF
llama
trl
sft
Generated from Trainer
conversational
text-generation-inference
Instructions to use nkasmanoff/nature-buddy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nkasmanoff/nature-buddy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nkasmanoff/nature-buddy") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nkasmanoff/nature-buddy") model = AutoModelForCausalLM.from_pretrained("nkasmanoff/nature-buddy") 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]:])) - llama-cpp-python
How to use nkasmanoff/nature-buddy with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="nkasmanoff/nature-buddy", filename="nature-buddy-0.135b-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use nkasmanoff/nature-buddy 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 nkasmanoff/nature-buddy:F16 # Run inference directly in the terminal: llama cli -hf nkasmanoff/nature-buddy:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nkasmanoff/nature-buddy:F16 # Run inference directly in the terminal: llama cli -hf nkasmanoff/nature-buddy: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 nkasmanoff/nature-buddy:F16 # Run inference directly in the terminal: ./llama-cli -hf nkasmanoff/nature-buddy: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 nkasmanoff/nature-buddy:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf nkasmanoff/nature-buddy:F16
Use Docker
docker model run hf.co/nkasmanoff/nature-buddy:F16
- LM Studio
- Jan
- vLLM
How to use nkasmanoff/nature-buddy with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nkasmanoff/nature-buddy" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nkasmanoff/nature-buddy", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/nkasmanoff/nature-buddy:F16
- SGLang
How to use nkasmanoff/nature-buddy 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 "nkasmanoff/nature-buddy" \ --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": "nkasmanoff/nature-buddy", "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 "nkasmanoff/nature-buddy" \ --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": "nkasmanoff/nature-buddy", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use nkasmanoff/nature-buddy with Ollama:
ollama run hf.co/nkasmanoff/nature-buddy:F16
- Unsloth Studio
How to use nkasmanoff/nature-buddy 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 nkasmanoff/nature-buddy 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 nkasmanoff/nature-buddy to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for nkasmanoff/nature-buddy to start chatting
- Atomic Chat new
- Docker Model Runner
How to use nkasmanoff/nature-buddy with Docker Model Runner:
docker model run hf.co/nkasmanoff/nature-buddy:F16
- Lemonade
How to use nkasmanoff/nature-buddy with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nkasmanoff/nature-buddy:F16
Run and chat with the model
lemonade run user.nature-buddy-F16
List all available models
lemonade list
nature-buddy
This model is a fine-tuned version of Qwen/Qwen2.5-0.5B on the None dataset.
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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 5
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.2.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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