Instructions to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with Transformers:

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

pipe = pipeline("text-generation", model="AndriLawrence/Qwen-3B-Intent-Microplan-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AndriLawrence/Qwen-3B-Intent-Microplan-v2")
model = AutoModelForCausalLM.from_pretrained("AndriLawrence/Qwen-3B-Intent-Microplan-v2", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with PEFT:
```
Task type is invalid.
```
Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AndriLawrence/Qwen-3B-Intent-Microplan-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 AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

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 AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

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 AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

Use Docker

docker model run hf.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AndriLawrence/Qwen-3B-Intent-Microplan-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AndriLawrence/Qwen-3B-Intent-Microplan-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

SGLang

How to use AndriLawrence/Qwen-3B-Intent-Microplan-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 "AndriLawrence/Qwen-3B-Intent-Microplan-v2" \
    --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": "AndriLawrence/Qwen-3B-Intent-Microplan-v2",
		"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 "AndriLawrence/Qwen-3B-Intent-Microplan-v2" \
        --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": "AndriLawrence/Qwen-3B-Intent-Microplan-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with Ollama:
```
ollama run hf.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M
```

Unsloth Studio

How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 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 AndriLawrence/Qwen-3B-Intent-Microplan-v2 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 AndriLawrence/Qwen-3B-Intent-Microplan-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AndriLawrence/Qwen-3B-Intent-Microplan-v2 to start chatting

How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

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": "AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

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 "AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M" \
  --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 AndriLawrence/Qwen-3B-Intent-Microplan-v2 with Docker Model Runner:
```
docker model run hf.co/AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M
```

Lemonade

How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-3B-Intent-Microplan-v2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AndriLawrence/Qwen-3B-Intent-Microplan-v2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

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 AndriLawrence/Qwen-3B-Intent-Microplan-v2:Q4_K_M

Run Hermes

hermes

Atomic Chat

AndriLawrence commited on Nov 5, 2025

Commit

d8ac4f9

verified ·

1 Parent(s): 78dfe83

Update README.md

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README.md +5 -3

README.md CHANGED Viewed

@@ -69,6 +69,8 @@ base_model:
 # AndriLawrence/Qwen-3B-Intent-Microplan-v2
 **English-only** finetune of **Qwen2.5-3B-Instruct** for **intent + microplan–driven NPC dialog**.
 The model reads a structured **CONTEXT JSON** (environment, relationship, mood, signals) and produces:
@@ -95,7 +97,7 @@ The model reads a structured **CONTEXT JSON** (environment, relationship, mood,
 ## 📦 Assets
   * **LoRA adapters (PEFT, SFT)** → `checkpoints/adapter_final`
-  * **Merged FP16** → `merged/sft-fp16`
   * **GGUF quants (llama.cpp / llama-cpp-python)** → `gguf/sft-q6_k.gguf`, `gguf/sft-q4_k_m.gguf`
   * **GGUF Style Fine-tune (Example)** → `gguf/rin_style.gguf` (See fine-tuning section)
@@ -182,7 +184,7 @@ They balance creativity with JSON stability for Rin:
 ```json
 {
-  "temperature": 0.92,
   "top_p": 0.90,
   "top_k": 40,
   "repetition_penalty": 1.05,
@@ -284,7 +286,7 @@ print(tok.decode(out[0], skip_special_tokens=True))
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
-MODEL = "AndriLawrence/Qwen-3B-Intent-Microplan-v2/merged/sft-fp16"
 tok = AutoTokenizer.from_pretrained(MODEL, use_fast=True, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(

 # AndriLawrence/Qwen-3B-Intent-Microplan-v2
+“Local-first 3B model for VR / game companions that outputs strict {dialog, intent, microplan} JSON from a CONTEXT event.”
 **English-only** finetune of **Qwen2.5-3B-Instruct** for **intent + microplan–driven NPC dialog**.
 The model reads a structured **CONTEXT JSON** (environment, relationship, mood, signals) and produces:
 ## 📦 Assets
   * **LoRA adapters (PEFT, SFT)** → `checkpoints/adapter_final`
+  * **Merged FP16** → `./`
   * **GGUF quants (llama.cpp / llama-cpp-python)** → `gguf/sft-q6_k.gguf`, `gguf/sft-q4_k_m.gguf`
   * **GGUF Style Fine-tune (Example)** → `gguf/rin_style.gguf` (See fine-tuning section)
 ```json
 {
+  "temperature": 0.65,
   "top_p": 0.90,
   "top_k": 40,
   "repetition_penalty": 1.05,
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
+MODEL = "AndriLawrence/Qwen-3B-Intent-Microplan-v2/"
 tok = AutoTokenizer.from_pretrained(MODEL, use_fast=True, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(