Qwen3.5-9B-OmniCoder-Claude-Polaris-Text-dwq4-mlx

391
2
license:apache-2.0
by
nightmedia
Language Model
OTHER
9B params
New
391 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
21GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
9GB+ RAM

Training Data Analysis

๐ŸŸก Average (4.3/10)

Researched training datasets used by Qwen3.5-9B-OmniCoder-Claude-Polaris-Text-dwq4-mlx with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

common crawl
๐Ÿ”ด 2.5/10
general
science
Key Strengths
  • โ€ขScale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • โ€ขDiversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • โ€ขComprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • โ€ขBiased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • โ€ขLarge-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
๐ŸŸก 5/10
science
multilingual
Key Strengths
  • โ€ขHigh-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • โ€ขMultilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • โ€ขStructured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • โ€ขLanguage Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • โ€ขBiased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
๐ŸŸก 5.5/10
science
reasoning
Key Strengths
  • โ€ขScientific Authority: Peer-reviewed content from established repository
  • โ€ขDomain-Specific: Specialized vocabulary and concepts
  • โ€ขMathematical Content: Includes complex equations and notation
Considerations
  • โ€ขSpecialized: Primarily technical and mathematical content
  • โ€ขEnglish-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Use with mlxbash
pip install mlx-lm
Use with mlxpython
from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-9B-OmniCoder-Claude-Polaris-Text-dwq4-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

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

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