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Model: jburnford/dyslexic-writer-qwen3-1.7b
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---
language: en
license: apache-2.0
tags:
- spelling-correction
- dyslexia
- text-correction
- education
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
---
# 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
```python
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](https://huggingface.co/jburnford/dyslexic-writer-qwen3-0.6b) | ~460 MB | 78.8% | Mobile/embedded |
| [Qwen3-1.7B](https://huggingface.co/jburnford/dyslexic-writer-qwen3-1.7b) | ~1.2 GB | 82.2% | Default |
| [Qwen3-4B](https://huggingface.co/jburnford/dyslexic-writer-qwen3-4b) | ~2.5 GB | 85.6% | Best quality |

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
{%- set ns.multi_step_tool = false %}
{%- set ns.last_query_index = index %}
{%- endif %}
{%- endfor %}
{%- for message in messages %}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and reasoning_content) %}
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- endif %}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
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],
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"tie_word_embeddings": true,
"transformers_version": "5.2.0",
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"vocab_size": 151936
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{
"model": "Qwen3-1.7B",
"total_examples": 5000,
"exact_match_accuracy": 0.8224,
"error_detection": {
"precision": 0.99457111834962,
"recall": 0.9803781662504459,
"f1": 0.9874236435501258,
"true_positives": 2748,
"false_positives": 15,
"true_negatives": 1364,
"false_negatives": 55
},
"has_error_accuracy": 0.7589063794531897,
"no_error_accuracy": 0.9891225525743292,
"inference_speed_tokens_per_sec": 75.49899709914501,
"total_inference_time_sec": 896.7403886318207,
"total_tokens_generated": 67703
}

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"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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