Instructions to use jburnford/dyslexic-writer-qwen3-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jburnford/dyslexic-writer-qwen3-1.7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jburnford/dyslexic-writer-qwen3-1.7b",
	filename="Qwen3-1.7B-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jburnford/dyslexic-writer-qwen3-1.7b 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 jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jburnford/dyslexic-writer-qwen3-1.7b: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 jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jburnford/dyslexic-writer-qwen3-1.7b: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 jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M

Use Docker

docker model run hf.co/jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M

LM Studio
Jan

vLLM

How to use jburnford/dyslexic-writer-qwen3-1.7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jburnford/dyslexic-writer-qwen3-1.7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jburnford/dyslexic-writer-qwen3-1.7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M

Ollama
How to use jburnford/dyslexic-writer-qwen3-1.7b with Ollama:
```
ollama run hf.co/jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M
```

Unsloth Studio

How to use jburnford/dyslexic-writer-qwen3-1.7b 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 jburnford/dyslexic-writer-qwen3-1.7b 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 jburnford/dyslexic-writer-qwen3-1.7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jburnford/dyslexic-writer-qwen3-1.7b to start chatting

How to use jburnford/dyslexic-writer-qwen3-1.7b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jburnford/dyslexic-writer-qwen3-1.7b: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": "jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jburnford/dyslexic-writer-qwen3-1.7b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jburnford/dyslexic-writer-qwen3-1.7b: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 jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jburnford/dyslexic-writer-qwen3-1.7b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jburnford/dyslexic-writer-qwen3-1.7b: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 "jburnford/dyslexic-writer-qwen3-1.7b: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 jburnford/dyslexic-writer-qwen3-1.7b with Docker Model Runner:
```
docker model run hf.co/jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M
```

Lemonade

How to use jburnford/dyslexic-writer-qwen3-1.7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jburnford/dyslexic-writer-qwen3-1.7b:Q4_K_M

Run and chat with the model

lemonade run user.dyslexic-writer-qwen3-1.7b-Q4_K_M

List all available models

lemonade list

Dyslexic Writer - Qwen3-1.7B

Fine-tuned Qwen/Qwen3-1.7B for spelling and grammar correction, optimized for dyslexic writers.

Performance

Metric	Score
Exact Match Accuracy	82.2%
Error Fix Rate	75.9%
No-Error Preservation	98.9%
F1 Score	98.7%

Trained on ~495K examples including word pairs, sentence corrections, and paragraph-level error injection from synthetic stories.

Usage

With Ollama (GGUF)

Download the Q4_K_M GGUF and create a Modelfile:

FROM ./dyslexic-writer-qwen3-1.7b-q4_k_m.gguf

PARAMETER temperature 0
PARAMETER num_predict 256

SYSTEM You are a spelling correction assistant.

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
<think>

</think>

"""

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("jburnford/dyslexic-writer-qwen3-1.7b")
tokenizer = AutoTokenizer.from_pretrained("jburnford/dyslexic-writer-qwen3-1.7b")

messages = [
    {"role": "system", "content": "You are a spelling correction assistant."},
    {"role": "user", "content": "Fix any spelling mistakes in this text. If there are no mistakes, output the text unchanged.\n\nI went to teh store."},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Variants

Model	GGUF Q4_K_M	Exact Match	Best For
Qwen3-0.6B	~460 MB	78.8%	Mobile/embedded
Qwen3-1.7B	~1.2 GB	82.2%	Default
Qwen3-4B	~2.5 GB	85.6%	Best quality

Downloads last month: 45

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for jburnford/dyslexic-writer-qwen3-1.7b

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(324)

this model

Quantizations

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