Qwen3.5-122B-A10B-AWQ
755
6
license:apache-2.0
by
QuantTrio
Image Model
OTHER
122B params
New
755 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
273GB+ RAM
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
114GB+ RAM
Training Data Analysis
🟡 Average (5.2/10)
Researched training datasets used by Qwen3.5-122B-A10B-AWQ with quality assessment
Specialized For
code
general
science
multilingual
Training Datasets (3)
the pile
🟢 8/10
code
general
science
multilingual
Key Strengths
- •Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
- •Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
- •Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
common crawl
🔴 2.5/10
general
science
Key Strengths
- •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
- •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
- •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
- •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
- •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
- •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
- •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
- •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
- •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
- •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
Explore our comprehensive training dataset analysis
View All DatasetsCode Examples
Note: important for old containers, or could face run time error likebashvllm
pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly
# Note: important for old containers, or could face run time error like
# subprocess.CalledProcessError:
# Command '['ninja', '-v', '-C',
# '/root/.cache/flashinfer/0.6.3/90a/cached_ops/trtllm_comm',
# '-f', '/root/.cache/flashinfer/0.6.3/90a/cached_ops/trtllm_comm/build.ninja']'
# returned non-zero exit status 1.
rm -rf ~/.cache/flashinfer
# upgrade transformers so that applications could properly execute tool calls
pip install -U "transformers @ git+https://github.com/huggingface/transformers.git@f2ba019"
# locate modeling_rope_utils.py line 651 to fix a simple bug
TF_FILE="$(python -m pip show transformers | awk -F': ' '/^Location:/{print $2}')/transformers/modeling_rope_utils.py" && echo "$TF_FILE"
NEW_LINE=' ignore_keys_at_rope_validation = set(ignore_keys_at_rope_validation) | {"partial_rotary_factor"}' \
perl -i.bak -pe 'if ($. == 651) { $_ = $ENV{NEW_LINE} . "\n" }' "$TF_FILE"bash
uv pip install 'git+https://github.com/sgl-project/sglang.git#subdirectory=python&egg=sglang[all]'bashvllm
uv pip install vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightlybash
pip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"bash
transformers serve --force-model Qwen/Qwen3.5-122B-A10B --port 8000 --continuous-batchingSet the following accordinglybash
pip install -U openai
# Set the following accordingly
export OPENAI_BASE_URL="http://localhost:8000/v1"
export OPENAI_API_KEY="EMPTY"Deploy This Model
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