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Phi-3.5-mini-instruct-Autoround-Safetensors
simaai/Phi-3.5-mini-instruct-Autoround-Safetensors
Phi-3.5-mini-instruct-Autoround-Safetensors is a text generation model from simaai. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Pre-LLiMa Hugging Face checkpoint based on microsoft/Phi-3.5-mini-instruct at source revision 2fe192450127e6a83f7441aef6e3ca586c338b77. It remains subject to the base model's license, intended use, and limitations.
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Model card
From the Hugging Face model README
Phi-3.5-mini-instruct Autoround
Pre-LLiMa Hugging Face checkpoint based on microsoft/Phi-3.5-mini-instruct at source revision 2fe192450127e6a83f7441aef6e3ca586c338b77. It remains subject to the base model's license, intended use, and limitations.
Quantization
| Component | Method | Weight format | Details |
|---|---|---|---|
| Decoder Linear layers | AutoRound | symmetric INT4, G256 | 128 targets; lm_head excluded |
lm_head | GPTQ | symmetric INT4, G256 | static act-order, block size 128, dampening 0.01 |
| Mixed-precision exceptions | — | source dtype | non-Linear parameters remain at source precision |
Calibration used HuggingFaceH4/ultrachat_200k (dataset revision not captured), train_sft[:512], rendered with the source chat template. Token IDs were deterministically concatenated and packed into 512 full 1024-token spans (no shuffle); AutoRound used 200 iterations and batch size 1.
Evaluation
Full wikitext-2-raw-v1 evaluation used the wikitext lm-eval task, no example limit, batch size 1, CUDA, and the same evaluator for source and quantized checkpoints on 2026-07-19.
| Checkpoint | Word perplexity | Status |
|---|---|---|
microsoft/Phi-3.5-mini-instruct source | 10.552125 | Full run |
| This UltraChat checkpoint | 11.608676 | Full run |
| Absolute degradation | 1.056551 | Lower is better |
| Relative degradation | 10.013% | 100 * (quantized / source - 1) |
Finite-scale validation and a Transformers chat-generation smoke test passed.
Reproduction
This directory includes the exact quantize.py, recipe.yaml, and versions.txt.
python quantize.py --model-path /path/to/models--microsoft--Phi-3.5-mini-instruct --output-dir /path/to/output
Environment
Exact Python, CUDA, Torch, Transformers, llmcompressor, AutoRound, and compressed-tensors versions are recorded in versions.txt.
Deployment
This is the pre-LLiMa quantized Hugging Face artifact. Compile it separately for the target Sima.ai platform and keep compiler output separate.
Limitations
Quantization quality can vary by language, domain, prompt format, context length, and deployment runtime; validate the intended workload independently.
- Task
- Text Generation
- Library
- transformers
- Type
- phi3
- License
- mit
- Languages
- multilingual
- Created
- Jul 16, 2026
- Updated
- Sep 1, 2026