Text Generation
Transformers
Safetensors
deepseek_v4
agentic
code
smaug
abacusai
8-bit precision
fp8
Instructions to use abacusai/Smaug-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abacusai/Smaug-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="abacusai/Smaug-Flash")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("abacusai/Smaug-Flash") model = AutoModelForCausalLM.from_pretrained("abacusai/Smaug-Flash", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use abacusai/Smaug-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "abacusai/Smaug-Flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Smaug-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/abacusai/Smaug-Flash
- SGLang
How to use abacusai/Smaug-Flash with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "abacusai/Smaug-Flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Smaug-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "abacusai/Smaug-Flash" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "abacusai/Smaug-Flash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use abacusai/Smaug-Flash with Docker Model Runner:
docker model run hf.co/abacusai/Smaug-Flash
Commit History
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Figures: LiveBench Mathematics 90.1 (was 91.3), overall 77.4 / +3.2, matching the 2026-06-25 leaderboard
Verified signatureRegenerated the category-profile and vs-base charts from the published LiveBench numbers; the README delta text follows.fa82a64
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Update AutomationBench delta to +13.7 (38.8 vs 25.1)
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Update README.md
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Update README.md
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Update README.md
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Finalize evaluation: keep grouped bar chart
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card fixes: gen-config top_p 0.95 (match recommended sampling), Hidden Dimension label, three-stage LoRA row, sglang kv-cache-dtype fp8_e4m3, recipe URL -0731
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Drop 'agentic cells' jargon; normalize DeepSeek harness casing
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AA-LCR noise caveat stated once (Notes only)
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Fix caption/notes contradiction on starred rows, parity wording, SGLang flag mismatch
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Drop section cross-reference in evaluation intro
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Three vs-base figure options (bars/dumbbell/table); radar delta bars aligned with table style
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Terminal Bench 2.1 DeepSeek-harness score updated to 76.4 (+10.1); wrap harness labels
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Agentic-style structure: model summary, eval as section 3, delta values only, short eval notes
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Doc-style delta bars in radar; reorder sections to Mini structure; condense training detail
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Add vs-base and LiveBench category charts, Abacus header, tags, citation; replace eval table
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TB2.1 terminus-2: 70.8 (2026-09-02 remeasure, interleaved thinking; 69.7 at uniform 1x)
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DeepSWE row: bold 56.6, drop redundant footnote clause
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DeepSWE row: simplify label, base = vendor-card 54.4*
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eval: DeepSWE v1.1 official-harness score 56.6 (2026-09-02 full 113-task run)
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Card cleanup: replace inherited DeepSeek header with Smaug-Flash lead, add base_model metadata, fold usage/serving docs, add AA-LCR paired result, license/citation/contact hygiene
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Finalize DeepSWE row: paired parity (49.1 vs 50.9, n=109, p=1.0)
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Add technical report: training methodology, data curation, weight merging, evaluation
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Add files using upload-large-folder tool
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Upload README.md with huggingface_hub
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