Instructions to use leafspark/wikichat-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leafspark/wikichat-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="leafspark/wikichat-v2")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("leafspark/wikichat-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use leafspark/wikichat-v2 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 leafspark/wikichat-v2:F32
# Run inference directly in the terminal:
llama cli -hf leafspark/wikichat-v2:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf leafspark/wikichat-v2:F32
# Run inference directly in the terminal:
llama cli -hf leafspark/wikichat-v2:F32

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 leafspark/wikichat-v2:F32
# Run inference directly in the terminal:
./llama-cli -hf leafspark/wikichat-v2:F32

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 leafspark/wikichat-v2:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf leafspark/wikichat-v2:F32

Use Docker

docker model run hf.co/leafspark/wikichat-v2:F32

LM Studio
Jan

vLLM

How to use leafspark/wikichat-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leafspark/wikichat-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leafspark/wikichat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/leafspark/wikichat-v2:F32

SGLang

How to use leafspark/wikichat-v2 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 "leafspark/wikichat-v2" \
    --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": "leafspark/wikichat-v2",
		"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 "leafspark/wikichat-v2" \
        --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": "leafspark/wikichat-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use leafspark/wikichat-v2 with Ollama:
```
ollama run hf.co/leafspark/wikichat-v2:F32
```
Unsloth Desktop
Docker Model Runner
How to use leafspark/wikichat-v2 with Docker Model Runner:
```
docker model run hf.co/leafspark/wikichat-v2:F32
```

Lemonade

How to use leafspark/wikichat-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull leafspark/wikichat-v2:F32

Run and chat with the model

lemonade run user.wikichat-v2-F32

List all available models

lemonade list

Atomic Chat

WikiChat-v0.2

Training in progress model to have conversations.

The GGUFs uploaded are full FP32 precision.

Using OpenOrca GPT-4 data + cosmopedia for some extra data + dolly15k for instruct

Model Details:

83.59M parameters (83591800)
8 attention heads
40 layers
384 embeddings size
4096/8192/16384 context (please use 2/4x RoPE scaling, may train a 16k finetuned version later)
Batch size 16
llama.cpp (train-text-from-scratch)

Prompt Format (Alpaca):

Instruction: {system}
Input: {prompt}
Response: {response}

Please structure your prompts in an instruct format for maximum performance.

Training Details:

1x RTX 3070 8GB (Infrencing speed: 80tok/s, full GPU offload)
1x Ryzen 3 3700x
96gb RAM
10 iterations
Loss Target = 2.5 to 3.0
Approx 480 samples/1M train tokens (>0.0001 epoches)
Training data = Refer to OpenOrca page

Notes:

The model isn't ready yet; this is to test tokenization of OpenOrca and a balance between training speed and model size

Example output:

User: What is the square root of 4?

Assistant: The square root of 4 is 2.

Downloads last month: 235

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

32-bit

View +2 variants

leafspark
/

wikichat-v2