Instructions to use Katsugachi/Wakaran-1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Katsugachi/Wakaran-1.2 with Transformers:

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

pipe = pipeline("text-generation", model="Katsugachi/Wakaran-1.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Katsugachi/Wakaran-1.2")
model = AutoModelForCausalLM.from_pretrained("Katsugachi/Wakaran-1.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Katsugachi/Wakaran-1.2 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 Katsugachi/Wakaran-1.2:Q1_0
# Run inference directly in the terminal:
llama cli -hf Katsugachi/Wakaran-1.2:Q1_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Katsugachi/Wakaran-1.2:Q1_0
# Run inference directly in the terminal:
llama cli -hf Katsugachi/Wakaran-1.2:Q1_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 Katsugachi/Wakaran-1.2:Q1_0
# Run inference directly in the terminal:
./llama-cli -hf Katsugachi/Wakaran-1.2:Q1_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 Katsugachi/Wakaran-1.2:Q1_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Katsugachi/Wakaran-1.2:Q1_0

Use Docker

docker model run hf.co/Katsugachi/Wakaran-1.2:Q1_0

LM Studio
Jan

vLLM

How to use Katsugachi/Wakaran-1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Katsugachi/Wakaran-1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Katsugachi/Wakaran-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Katsugachi/Wakaran-1.2:Q1_0

SGLang

How to use Katsugachi/Wakaran-1.2 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 "Katsugachi/Wakaran-1.2" \
    --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": "Katsugachi/Wakaran-1.2",
		"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 "Katsugachi/Wakaran-1.2" \
        --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": "Katsugachi/Wakaran-1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Katsugachi/Wakaran-1.2 with Ollama:
```
ollama run hf.co/Katsugachi/Wakaran-1.2:Q1_0
```
Unsloth Desktop
Docker Model Runner
How to use Katsugachi/Wakaran-1.2 with Docker Model Runner:
```
docker model run hf.co/Katsugachi/Wakaran-1.2:Q1_0
```

Lemonade

How to use Katsugachi/Wakaran-1.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Katsugachi/Wakaran-1.2:Q1_0

Run and chat with the model

lemonade run user.Wakaran-1.2-Q1_0

List all available models

lemonade list

Atomic Chat

Katsugachi commited on Jul 18

Commit

a3cd5ee

verified ·

1 Parent(s): 364687e

Upload 4 files

Browse files

Files changed (4) hide show

config.json +28 -0
model.safetensors +3 -0
special_tokens_map.json +5 -0
tokenizer_config.json +7 -0

config.json ADDED Viewed

	@@ -0,0 +1,28 @@

+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 6,
+  "hidden_act": "silu",
+  "hidden_size": 96,
+  "initializer_range": 0.02,
+  "intermediate_size": 256,
+  "max_position_embeddings": 100000000,
+  "model_type": "llama",
+  "num_attention_heads": 3,
+  "num_hidden_layers": 2,
+  "num_key_value_heads": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.38.0",
+  "use_cache": true,
+  "vocab_size": 4096
+}

model.safetensors ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:372cde8d8ad5cab6732c44963240b7b5bbbe5ff0269733e72c1301e25e3129fd
+size 4034544

special_tokens_map.json ADDED Viewed

	@@ -0,0 +1,5 @@

+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1,7 @@

+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "unk_token": "<unk>",
+  "model_max_length": 512,
+  "tokenizer_class": "LlamaTokenizer"
+}