Instructions to use queefwath/Qwen3-0.6B-10X-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use queefwath/Qwen3-0.6B-10X-Instruct 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 queefwath/Qwen3-0.6B-10X-Instruct
# Run inference directly in the terminal:
llama cli -hf queefwath/Qwen3-0.6B-10X-Instruct

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf queefwath/Qwen3-0.6B-10X-Instruct
# Run inference directly in the terminal:
llama cli -hf queefwath/Qwen3-0.6B-10X-Instruct

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 queefwath/Qwen3-0.6B-10X-Instruct
# Run inference directly in the terminal:
./llama-cli -hf queefwath/Qwen3-0.6B-10X-Instruct

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 queefwath/Qwen3-0.6B-10X-Instruct
# Run inference directly in the terminal:
./build/bin/llama-cli -hf queefwath/Qwen3-0.6B-10X-Instruct

Use Docker

docker model run hf.co/queefwath/Qwen3-0.6B-10X-Instruct

LM Studio
Jan

vLLM

How to use queefwath/Qwen3-0.6B-10X-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "queefwath/Qwen3-0.6B-10X-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "queefwath/Qwen3-0.6B-10X-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/queefwath/Qwen3-0.6B-10X-Instruct

Ollama
How to use queefwath/Qwen3-0.6B-10X-Instruct with Ollama:
```
ollama run hf.co/queefwath/Qwen3-0.6B-10X-Instruct
```
Unsloth Desktop

How to use queefwath/Qwen3-0.6B-10X-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf queefwath/Qwen3-0.6B-10X-Instruct

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "queefwath/Qwen3-0.6B-10X-Instruct"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use queefwath/Qwen3-0.6B-10X-Instruct with Docker Model Runner:
```
docker model run hf.co/queefwath/Qwen3-0.6B-10X-Instruct
```

Lemonade

How to use queefwath/Qwen3-0.6B-10X-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull queefwath/Qwen3-0.6B-10X-Instruct

Run and chat with the model

lemonade run user.Qwen3-0.6B-10X-Instruct-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use queefwath/Qwen3-0.6B-10X-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf queefwath/Qwen3-0.6B-10X-Instruct

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 queefwath/Qwen3-0.6B-10X-Instruct

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use queefwath/Qwen3-0.6B-10X-Instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf queefwath/Qwen3-0.6B-10X-Instruct

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 "queefwath/Qwen3-0.6B-10X-Instruct" \
  --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"

🚀 10X Technologies — Qwen3-0.6B-10X-Instruct

This is the official fine-tuned Qwen3-0.6B-10X-Instruct model by Pikachu Global Technologies Private Limited (10X Technologies), Ongole, Andhra Pradesh, India.

The model is specialized for 10X Technologies domain knowledge, on-device Indic language understanding, and edge-first architecture (Akshara Tokenizers, Libre OS, and LUCA Smart Speaker).

📌 Model Details

Base Architecture: Qwen3-0.6B / Qwen2.5-0.5B-Instruct
Tuning Method: LoRA SFT ($r=16, \alpha=32$) permanently merged into base weights (merge_and_unload).
Context Length: 2048 tokens
Chat Format: Qwen ChatML (<|im_start|>system...<|im_end|>)
Parameters: ~502 Million

📊 Dataset & Distillation Pipeline

Total Samples: 1,562 curated ChatML conversations.
Data Blend:
- 80% 10X Distilled Knowledge (1,250 samples across 4 personas: Investor/Analyst, Educator/Parent, Engineer/Researcher, Adversarial Boundary).
- 20% General Replay Data (312 samples) to eliminate catastrophic forgetting.
Quality Assurance:
- Pruned all 49 placeholder/unverified claims ([[VERIFY]]).
- Zero unverifiable superlatives (strictly truthful).
- Explicit DPDP compliance & on-premise data sovereignty.

🚀 Quickstart Usage

Using Hugging Face Transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "YOUR_USERNAME/Qwen3-0.6B-10X-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

messages = [
    {
        "role": "system",
        "content": "You are the official 10X Technologies AI assistant. You speak with precision, candor, and specificity, strictly adhering to verified company facts without using unverifiable superlatives."
    },
    {
        "role": "user",
        "content": "Why are purpose-built tokenizers necessary for Telugu?"
    }
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.3)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

🏢 About 10X Technologies

Company: Pikachu Global Technologies Private Limited (10X Technologies)
Headquarters: Ongole, Andhra Pradesh, India
Mission: Making computers actually speak Indian languages, not just support them.

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Safetensors

Model size

0.5B params

Tensor type

F32

Model tree for queefwath/Qwen3-0.6B-10X-Instruct

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