- GLM Legal model download starts on Hugging Face under the
law-llmorganization - Two variants exist: law-glm-10b and law-glm-10b-v2, both 10B parameters
- Total model size is roughly 39.5 GB, so plan disk and RAM accordingly
- License is Apache-2.0, permitting commercial use with attribution conditions
- Loading requires
trust_remote_code=True— review the code before running it
GLM Legal Model Download Overview
The GLM Legal model download process gives you access to a 10-billion-parameter legal-domain language model built on the GLM architecture. The project is hosted on Hugging Face under the law-llm organization and focuses on Chinese legal text understanding, document analysis, and legal question answering.
Before downloading anything, understand what you are actually pulling:
| Item | Detail |
|---|---|
| Base model page | law-glm-10b on Hugging Face |
| V2 model page | law-glm-10b-v2 on Hugging Face |
| Parameters | 10B |
| Approximate size | 39.5 GB of model files |
| License | Apache-2.0 |
| Upstream code | THUDM/GLM on GitHub |
"GLM Legal" is a community label for the law-glm-10b / law-glm-10b-v2 model pages. There is no confirmed standalone official website, Discord, or official trailer. Do not confuse it with Z.ai commercial legal products or the historical LawGLM open-source project.
law-glm-10b
- Base variant of GLM Legal
- 10B parameters
- Best for first experiments and baseline testing
law-glm-10b-v2
- Updated variant with revised weights
- Same 10B scale
- Compare both before committing to one
Upstream GLM repo
- Source code for the GLM architecture
- Issues and deployment discussions
- Reference for custom loading logic
Environment Requirements Before Downloading
A successful GLM Legal model download depends on having the right environment in place first. The model is large, and loading failures almost always trace back to missing dependencies, insufficient VRAM, or disk space issues.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8+ | 3.10+ |
| PyTorch | 1.12+ | 2.x with CUDA 11.8+ |
| Transformers | Latest stable | Latest stable |
| Disk space | 45 GB free | 60+ GB free |
| GPU VRAM | 24 GB (fp16) | Multiple GPUs or quantization for less |
| Internet | Stable, ~40 GB transfer | Wired or high-bandwidth connection |
The 39.5 GB figure covers model files alone. Hugging Face caches downloads separately from extracted checkpoints, so keep roughly double the model size free during your first download to avoid a failed transfer halfway through.
Key Python dependencies to install before you begin:
torch(matching your CUDA version)transformerssentencepieceand tokenization utilities used by GLM-family modelsacceleratefor multi-GPU or device-map loading
GLM Legal model pages rely on custom modeling code loaded from the repository. Loading with trust_remote_code=True executes that code locally. Read the repository files on Hugging Face first so you understand exactly what runs on your machine — this is standard practice for GLM-family checkpoints but should never be done blindly.
GLM Legal Model Download: Step by Step
Follow this sequence to pull the weights and run your first inference. Every step assumes a Linux or WSL environment with a CUDA-capable GPU.
Install core dependencies
Set up a clean virtual environment, then install PyTorch with the correct CUDA build, followed by transformers and sentencepiece. Pin versions once confirmed working so teammates reproduce your setup.
Authenticate with Hugging Face
The model pages are publicly browsable, but logging in with huggingface-cli login avoids rate limits during a 39.5 GB transfer and lets you resume interrupted downloads.
Download the model files
Either load directly through transformers (which auto-fetches files into the cache) or use huggingface-cli download law-llm/law-glm-10b for a standalone local copy you can move between machines.
Load with trust_remote_code=True
Instantiate the tokenizer and model from the local path or repo ID, pass trust_remote_code=True, and set torch_dtype=torch.float16 to fit the 10B weights into a 24 GB GPU.
Run a first legal inference
Feed a short Chinese legal prompt, set reasonable max_new_tokens, and verify the output is coherent before scaling to longer documents or batch workloads.
Confirm checksums or file listings on the model page after download. A truncated shard is the most common cause of "weight loading failed" errors, and it is far easier to re-download one shard than to debug random NaN outputs.
Inference Use Cases After Download
Once the GLM Legal model download completes, the model fits into several practical legal AI workflows. Treat every output as draft work product requiring professional review.
| Use case | Input materials | Output focus |
|---|---|---|
| Contract review | Contract text, amendments, review criteria | Clause summary, issue list, risk classification |
| Evidence analysis | Chronologies, emails, pleadings | Fact matrix, timeline, contradiction list |
| Legal research | Statutes, cases, research questions | Issue tree, authority summaries, follow-ups |
| Compliance planning | Rules, policies, risk registers | Requirement matrix, control checklist |
| Legal drafting | Templates, facts, defined terms | Document outline, draft language, open issues |
Prompt Structure That Works
- Separate background facts from the task
- Define the jurisdiction explicitly
- Require clause or page citations
- Ask the model to flag missing facts
Output Controls
- Use a fixed output schema
- Separate findings, analysis, and actions
- Include a confidence field
- Route uncertain items to human review
GLM Legal is a research and assistance model, not a lawyer. Any clause interpretation, legal citation, or draft used in practice must be verified by a qualified professional against the original sources and the correct jurisdiction.
Licensing, Safety, and Deployment Checklist
Both model pages are labeled Apache-2.0, which permits commercial use, modification, and redistribution, subject to the license terms. Verify the license text on the model page at download time, since repository metadata can change.
| License aspect | What it means for you |
|---|---|
| Commercial use | Generally permitted under Apache-2.0 terms |
| Redistribution | Allowed; retain license and notices |
| Attribution | Keep copyright and license files with weights |
| Liability | Provided as-is; no warranty for legal accuracy |
| Your responsibility | Compliance, data privacy, and output review are yours |
Pre-Download and Deployment Checklist:
- Confirm 60+ GB free disk space and 24 GB+ VRAM
- Review repository code before enabling trust_remote_code
- Verify the Apache-2.0 license text on the model page
- Test inference on short prompts before long documents
- Establish a human review workflow for all legal outputs
Model files, loading methods, and known issues are occasionally revised. Watch the Hugging Face Community tab on the model page and the THUDM/GLM GitHub issues for changes that affect your deployment.
FAQ
Q: Where does the GLM Legal model download actually happen?
On Hugging Face, under the law-llm organization. The two public pages are law-glm-10b and law-glm-10b-v2. There is no separate official website confirmed for the project.
Q: How much disk space do I need for the GLM Legal model download?
Model files total roughly 39.5 GB. Keep at least 45-60 GB free to accommodate the Hugging Face cache alongside the extracted checkpoint.
Q: Can I use GLM Legal commercially?
The model pages are labeled Apache-2.0, which generally permits commercial use. Read the actual license file in the repository before deploying, and remember that compliance, privacy, and output accuracy remain your responsibility.
Q: Why does loading fail with a trust_remote_code error?
GLM-family checkpoints ship custom modeling code. You must pass trust_remote_code=True when loading the tokenizer and model. Review the repository code first, since this setting executes repository files locally.