Instructions to use ddtsoftware/Train06 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ddtsoftware/Train06 with Transformers:

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

pipe = pipeline("text-generation", model="ddtsoftware/Train06")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ddtsoftware/Train06")
model = AutoModelForCausalLM.from_pretrained("ddtsoftware/Train06", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ddtsoftware/Train06 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 ddtsoftware/Train06:Q8_0
# Run inference directly in the terminal:
llama cli -hf ddtsoftware/Train06:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ddtsoftware/Train06:Q8_0
# Run inference directly in the terminal:
llama cli -hf ddtsoftware/Train06:Q8_0

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 ddtsoftware/Train06:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ddtsoftware/Train06:Q8_0

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 ddtsoftware/Train06:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ddtsoftware/Train06:Q8_0

Use Docker

docker model run hf.co/ddtsoftware/Train06:Q8_0

LM Studio
Jan

vLLM

How to use ddtsoftware/Train06 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ddtsoftware/Train06"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ddtsoftware/Train06",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ddtsoftware/Train06:Q8_0

SGLang

How to use ddtsoftware/Train06 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 "ddtsoftware/Train06" \
    --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": "ddtsoftware/Train06",
		"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 "ddtsoftware/Train06" \
        --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": "ddtsoftware/Train06",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use ddtsoftware/Train06 with Ollama:
```
ollama run hf.co/ddtsoftware/Train06:Q8_0
```

Unsloth Studio

How to use ddtsoftware/Train06 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 ddtsoftware/Train06 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 ddtsoftware/Train06 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ddtsoftware/Train06 to start chatting

Docker Model Runner
How to use ddtsoftware/Train06 with Docker Model Runner:
```
docker model run hf.co/ddtsoftware/Train06:Q8_0
```

Lemonade

How to use ddtsoftware/Train06 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ddtsoftware/Train06:Q8_0

Run and chat with the model

lemonade run user.Train06-Q8_0

List all available models

lemonade list

Atomic Chat

Train06

File size: 737 Bytes

2805b33
3741998
37fe3a2

{
  "_name_or_path": "google/gemma-7b",
  "architectures": [
    "GemmaForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 2,
  "eos_token_id": 1,
  "head_dim": 256,
  "hidden_act": "gelu",
  "hidden_activation": "gelu_pytorch_tanh",
  "hidden_size": 3072,
  "initializer_range": 0.02,
  "intermediate_size": 24576,
  "max_position_embeddings": 8192,
  "model_type": "gemma",
  "num_attention_heads": 16,
  "num_hidden_layers": 28,
  "num_key_value_heads": 16,
  "pad_token_id": 0,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 10000.0,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.41.2",
  "unsloth_version": "2024.6",
  "use_cache": true,
  "vocab_size": 256000
}