quantizing-models-bitsandbytes SKILL.md skillA
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quantizing-models-bitsandbytes from yichen-zju/claude-for-research is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add yichen-zju/claude-for-research/quantizing-models-bitsandbytes@v1.0.0Pins v1.0.0 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- yichen-zju/claude-for-research
- Path
claude/skills/bitsandbytes/SKILL.md- Versions recorded
- 1
- Latest version
- v1.0.0, committed Oct 14, 2026
- Size
- 10,126 bytes
- Repository stars
- 10
- License
- none declared
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# bitsandbytes - LLM Quantization
## Quick start
bitsandbytes reduces LLM memory by 50% (8-bit) or 75% (4-bit) with <1% accuracy loss.
**Installation**:
```bash
pip install bitsandbytes transformers accelerate
```
**8-bit quantization** (50% memory reduction):
```python
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
quantization_config=config,
device_map="auto"
)
# Memory: 14GB → 7GB
```
**4-bit quantization** (75% memory reduction):
```python
config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
model = AutoModelForCausalLM.from_pretrained(
"meta-llama/Llama-2-7b-hf",
quantization_config=config,
device_map="auto"
)
# Memory: 14GB → 3.5GB
```
## Common workflows
### Workflow 1: Load large model in limited GPU memory
Copy this checklist:
```
Quantization Loading:
- [ ] Step 1: Calculate memory requirements
- [ ] Step 2: Choose quantization level (4-bit or 8-bit)
- [ ] Step 3: Configure quantization
- [ ] Step 4: Load and verify model
```
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How to install
mdr add yichen-zju/claude-for-research/quantizing-models-bitsandbytes@sha256:0a68e96c760b0ba5The line at the top of this page pins the label v1.0.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_vdcfqf5ef6vlbkc5)
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Versions
The registry has recorded 1 version of this file, committed on Oct 14, 2026.
Audit of the latest version
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Source
yichen-zju/claude-for-research · 10 stars · license none declared · pushed 2026-10-21 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_vdcfqf5ef6vlbkc5 GET https://markdownregistry.com/api/v1/resolve?ref=yichen-zju/claude-for-research/quantizing-models-bitsandbytes GET https://markdownregistry.com/api/v1/blob/0a68e96c760b0ba561437328211a8405b4db422e9bad92415219abf91f87f849
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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