Instructions to use everscending/qwen3-1.7b-article-usage-micro-coach with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use everscending/qwen3-1.7b-article-usage-micro-coach with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("everscending/qwen3-1.7b-article-usage-micro-coach")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use everscending/qwen3-1.7b-article-usage-micro-coach with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "everscending/qwen3-1.7b-article-usage-micro-coach"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "everscending/qwen3-1.7b-article-usage-micro-coach"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use everscending/qwen3-1.7b-article-usage-micro-coach with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "everscending/qwen3-1.7b-article-usage-micro-coach"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "everscending/qwen3-1.7b-article-usage-micro-coach"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "everscending/qwen3-1.7b-article-usage-micro-coach",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use everscending/qwen3-1.7b-article-usage-micro-coach with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "everscending/qwen3-1.7b-article-usage-micro-coach"

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 everscending/qwen3-1.7b-article-usage-micro-coach

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use everscending/qwen3-1.7b-article-usage-micro-coach with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "everscending/qwen3-1.7b-article-usage-micro-coach"

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 "everscending/qwen3-1.7b-article-usage-micro-coach" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Qwen3 Article-Usage Micro-Coach — v2

This is a dequantized MLX fusion of a QLoRA adapter trained from mlx-community/Qwen3-1.7B-4bit. It asks one conceptual coaching question about one English article error without revealing the correction. After the learner independently fixes the target, it responds exactly Target fixed.

Frozen inputs

Base revision: 3b1b1768f8f8cf8351c712464f906e86c2b8269e
Dataset: 2,324 filtered examples (1,831 train, 493 validation)
Selected adapter: step 500, chosen by validation loss
Training: MLX-LM QLoRA

Frozen 60-case adapter results

Model	Spec adherence	Robustness	Mean usefulness
Untuned Qwen3	1/60 (1.7%)	1/30 (3.3%)	0.100
Dataset v2 adapter	43/60 (71.7%)	23/30 (76.7%)	1.433

Before upload, this dequantized fusion reproduced the adapter's pass pattern on a five-case semantic parity check: 4/5 passes and 1.600 usefulness. The remaining failure was the same known wrong-concept case as the adapter.

This model is not reliable enough for unsupervised teaching. Its largest remaining failure is asking a well-formed question about the wrong article rule.

Evaluate

Use evaluation-code commit a413040a6177921a5e770f6c71203b4f812e728f:

python eval.py \
  --model everscending/qwen3-1.7b-article-usage-micro-coach \
  --eval-set eval/scenarios/dev.jsonl

Downloads last month: 512

Safetensors

Model size

2B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

Model tree for everscending/qwen3-1.7b-article-usage-micro-coach

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

mlx-community/Qwen3-1.7B-4bit

Finetuned

(2)

this model