Instructions to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo 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 WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0
# Run inference directly in the terminal:
llama cli -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0
# Run inference directly in the terminal:
llama cli -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo: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 WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo: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 WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Use Docker

docker model run hf.co/WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

LM Studio
Jan

vLLM

How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Ollama
How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with Ollama:
```
ollama run hf.co/WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0
```

Unsloth Studio

How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo 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 WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo 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 WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo to start chatting

How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with Docker Model Runner:
```
docker model run hf.co/WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0
```

Lemonade

How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Run and chat with the model

lemonade run user.Hourai-a150m-7.8b-TrainingCodes-Demo-Q8_0

List all available models

lemonade list

Hermes Agent

How to use WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo:Q8_0

Run Hermes

hermes

Atomic Chat

hou-rai (蓬莱) a150m 7.8b Trainig codes | Other

~ a model for demo 2026: Qwen3moe architecture 150m * 52 expert 7.8b ~

Model Details

base model: Qwen/Qwen3-0.6B (to slice 150m), to qwen3moe architecture use mergekit-moe

file tree

# ==================================================
./scripts
  ./merge_150m_step1_distill.py
  ./merge_150m_step2_wikipedia.py
  ./merge_150m_step3_aozora.py
  ./merge_150m_step3_aozora_fix.py

  ./train_peft_150m_step1_distll.py
  ./train_peft_150m_step2_wikipedia.py
  ./train_peft_150m_step3_aozora.py
  ./train_peft_150m_step3_aozora_fix.py

# ==================================================
# datasets
# ==================================================
./datasets
  ./Japanese-Wikipedia-202506
    ./
  ./aozorabunko-clean/aozorabunko-dedupe-clean.jsonl.gz
  ./Zero_SFT_Ja_v3.5_Reasoning/answers_bare.jsonl
  ./Synthetic-JP-EN-Coding-Dataset-801k/train-00000-of-00013.parquet
  ./wizardlm8x22b-logical-math-coding-sft-ja/train-00000-of-00002.parquet

./mergekit/
  ./config.yml
  ./split1.yml

# ==================================================
# model weights
# ==================================================

./qwen3-150x52m-7.8b
  ./chat_template.jinja
  ./config.json
  ./mergekit_moe_config.yml
  ./model.safetensors.index.json
  ./model-00001-of-00002.safetensors
  ./model-00002-of-00002.safetensors
  ./tokenizer.json
  ./tokenizer_config.json

./qwen3-150x52m-7.8b-step1-distll
  ./chat_template.jinja
  ./config.json
  ./generation_config.json
  ./model.safetensors
  ./tokenizer.json
  ./tokenizer_config.json

./qwen3-150x52m-7.8b-step2-wikipedia
  ./chat_template.jinja
  ./config.json
  ./generation_config.json
  ./model.safetensors
  ./tokenizer.json
  ./tokenizer_config.json

./qwen3-150x52m-7.8b-step3-aozora
  ./chat_template.jinja
  ./config.json
  ./generation_config.json
  ./model.safetensors
  ./tokenizer.json
  ./tokenizer_config.json

./qwen3-150x52m-7.8b-step3-aozora-fix
  ./chat_template.jinja
  ./config.json
  ./generation_config.json
  ./model.safetensors
  ./tokenizer.json
  ./tokenizer_config.json

# ==================================================
# model gguf (can work upstream llama.cpp)
# ==================================================

Model Description

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

Dataset:

raw_text | books:

OmniAICreator/Japanese-Wikipedia-202506
globis-university/aozorabunko-clean

chat | reasoning:

DataPilot/Zero_SFT_Ja_v3.5_Reasoning

math | coding:

llm-jp/wizardlm8x22b-logical-math-coding-sft-ja
Aratako/Synthetic-JP-EN-Coding-Dataset-801k

Training Procedure

Preprocessing [optional]

None

Training Hyperparameters

Training regime: [More Information Needed]

qwen3-150x52m-step1-distill

model (30000steps checkpoints)

dataset

DataPilot/Zero_SFT_Ja_v3.5_Reasoning

#### lora config

  task_type=TaskType.CAUSAL_LM,
  r=16,
  lora_alpha=32,
  target_modules=[
      "q_proj",
      "k_proj",
      "v_proj",
      "o_proj",
      "gate_proj",
      "up_proj",
      "down_proj",
      "gate_up_proj"
  ]
  lora_dropout=0,
  bias="none",

#### transformer trainer config
  per_device_train_batch_size=1,
  gradient_accumulation_steps=4, 
  warmup_steps=5,
  max_steps=50000,
  learning_rate=2e-4,
  optim="adamw_8bit",             
  weight_decay=0.001,
  lr_scheduler_type="linear",
  seed=3407,
  fp16=False,                     
  bf16=True,                     
  remove_unused_columns=False

qwen3-150x52m-step2-wikipedia

model (10000steps checkpoints)

dataset

DataPilot/Zero_SFT_Ja_v3.5_Reasoning [:30000]
OmniAICreator/Japanese-Wikipedia-202506 [:30000]

#### lora config

  task_type=TaskType.CAUSAL_LM,
  r=16,
  lora_alpha=32,
  target_modules=[
      "q_proj",
      "k_proj",
      "v_proj",
      "o_proj",
      "gate_proj",
      "up_proj",
      "down_proj",
      "gate_up_proj"
  ]
  lora_dropout=0,
  bias="none",

#### transformer trainer config
  per_device_train_batch_size=1,
  gradient_accumulation_steps=4, 
  warmup_steps=5,
  max_steps=50000,
  learning_rate=2e-4,
  optim="adamw_8bit",             
  weight_decay=0.001,
  lr_scheduler_type="linear",
  seed=3407,
  fp16=False,                     
  bf16=True,                     
  remove_unused_columns=False

qwen3-150x52m-step3-aozora

model (15000steps checkpoints)

dataset

DataPilot/Zero_SFT_Ja_v3.5_Reasoning [:30000]
OmniAICreator/Japanese-Wikipedia-202506 [:30000]
globis-university/aozorabunko-clean [:15000]

#### lora config

  task_type=TaskType.CAUSAL_LM,
  r=16,
  lora_alpha=32,
  target_modules=[
      "q_proj",
      "k_proj",
      "v_proj",
      "o_proj",
      "gate_proj",
      "up_proj",
      "down_proj",
      "gate_up_proj"
  ]
  lora_dropout=0,
  bias="none",

#### transformer trainer config
  per_device_train_batch_size=1,
  gradient_accumulation_steps=4, 
  warmup_steps=5,
  max_steps=50000,
  learning_rate=2e-4,
  optim="adamw_8bit",             
  weight_decay=0.001,
  lr_scheduler_type="linear",
  seed=3407,
  fp16=False,                     
  bf16=True,                     
  remove_unused_columns=False

qwen3-150x52m-step3-aozora-fix

model (45000steps checkpoints)

dataset

DataPilot/Zero_SFT_Ja_v3.5_Reasoning [:30000]
OmniAICreator/Japanese-Wikipedia-202506 [:30000]
globis-university/aozorabunko-clean [:15000]

#### lora config
  task_type=TaskType.CAUSAL_LM,
  r=16,
  lora_alpha=32,
  target_modules=[
      "q_proj",
      "k_proj",
      "v_proj",
      "o_proj",
      "gate_proj",
      "up_proj",
      "down_proj",
      "gate_up_proj"
  ]
  lora_dropout=0,
  bias="none",

#### SFTtrainer SFTConfig
      max_length=max_8192,
      per_device_train_batch_size=1,
      gradient_accumulation_steps=1,
      warmup_steps=0,
      max_steps=60000,
      learning_rate=5e-4,
      logging_steps=5,
      optim="adamw_8bit",            
      weight_decay=0.0005,
      lr_scheduler_type="linear",
      seed=3407,
      report_to="none",
      bf16=True,                    
      remove_unused_columns=False,
      completion_only_loss=True

qwen3-150x52m-step4_math

dataset

DataPilot/Zero_SFT_Ja_v3.5_Reasoning [:10000]
OmniAICreator/Japanese-Wikipedia-202506 [:10000]
globis-university/aozorabunko-clean [:10000]
llm-jp/wizardlm8x22b-logical-math-coding-sft-ja [:10000]
Aratako/Synthetic-JP-EN-Coding-Dataset-801k [:10000]

#### lora config
  task_type=TaskType.CAUSAL_LM,
  r=16,
  lora_alpha=32,
  target_modules=[
      "q_proj",
      "k_proj",
      "v_proj",
      "o_proj",
      "gate_proj",
      "up_proj",
      "down_proj",
      "gate_up_proj"
  ]
  lora_dropout=0,
  bias="none",

#### SFTtrainer SFTConfig
      max_length=8192,
      per_device_train_batch_size=1,
      gradient_accumulation_steps=1,
      warmup_steps=0,
      max_steps=60000,
      learning_rate=5e-4,
      logging_steps=5,
      optim="adamw_8bit",            
      weight_decay=0.0005,
      lr_scheduler_type="linear",
      seed=3407,
      report_to="none",
      bf16=True,                    
      remove_unused_columns=False,
      completion_only_loss=True

qwen3-150x52m-step4-math-fix

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

Qwen3Moe

Compute Infrastructure

local pc

Hardware

Nvidia RTX 3060 * 1

Software

peft, trl

hou-rai (蓬莱) 2 Trainig codes | Other

Technical Specifications [optional]

Model Architecture and Objective

Qwen3Next

Compute Infrastructure

local pc

Hardware

Nvidia RTX 3060 * 1

Software

peft, trl

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

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More Information [optional]

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Model Card Authors [optional]

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Model Card Contact

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Downloads last month: 238

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Model size

4B params

Architecture

qwen3moe

Hardware compatibility

8-bit

View +1 variant

Model tree for WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo

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Finetuned

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Datasets used to train WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo

Paper for WariHima/Hourai-a150m-7.8b-TrainingCodes-Demo

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 60