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ChatGLM-6B is an open-source, 6.2-billion-parameter bilingual (Chinese-English) dialogue language model based on the GLM architecture that runs on consumer-grade GPUs (6GB VRAM with INT4 quantization). It combines model quantization, supervised fine-tuning, and RLHF to generate human-preference-aligned responses without requiring cloud APIs. Two-track structure: root-level inference demos (api.py, cli_demo.py, cli_demo_vision.py) for immediate use, and ptuning/ subdirectory containing complete fine-tuning pipeline (arguments.py, main.py, trainer.py, deepspeed.json, train_chat.sh). Examples/ and resources/ hold use-case demonstrations. No formal test suite visible in file structure.

ML researchers and developers building Chinese-English conversational AI applications who need local deployment flexibility, fine-tuning capabilities, and open model transparency. Includes practitioners using P-Tuning v2 for domain-specific model adaptation.

Actively maintained but showing signs of age: repo is referenced as a predecessor to GLM-4 (released 2024), with latest commits still present. Has complete documentation, working demos (cli_demo.py, web_demo.py), and quantization support, but primary development has shifted to THUDM/GLM-4. Production-ready for inference; actively developed for the 6B variant but not receiving major feature updates.

Standard open source risks apply.

Repository is in maintenance mode for ChatGLM-6B while core development focuses on GLM-4 (referenced prominently in README as successor). README highlights API platform (open.bigmodel.cn) for accessing GLM-4 variants. P-Tuning fine-tuning capability actively documented with shell scripts for training/evaluation. Vision capabilities added (cli_demo_vision.py) in recent updates.

git clone https://github.com/THUDM/ChatGLM-6B.git
cd ChatGLM-6B
pip install -r requirements.txt
python cli_demo.py

Daily commands: Inference: python cli_demo.py (interactive CLI) or python api.py (FastAPI server). Web UI: python ptuning/web_demo.py. Fine-tuning: bash ptuning/train_chat.sh (requires GPU, configured via ptuning/arguments.py).

• web_demo.py — Primary web interface entry point using Gradio; essential for understanding the interactive chat UI and model inference pipeline.
• api.py — REST API server implementation; defines all external-facing endpoints for programmatic model access.
• cli_demo.py — Command-line interface entry point; demonstrates synchronous model loading and inference patterns.
• utils.py — Core utility functions for model loading, tokenization, and inference; shared by all entry points.
• ptuning/main.py — Parameter-efficient tuning training harness; required for understanding fine-tuning workflows and prompt optimization.
• requirements.txt — Dependency manifest including transformers, torch, and gradio versions; critical for reproducible setup.
• README.md — Project documentation with usage examples, limitations, and model architecture details in Chinese.

Add a new fine-tuning task

1. Define task-specific training data format in JSONL (one JSON per line) following the structure shown in ptuning/data_sample.jsonl (ptuning/data_sample.jsonl)
2. Create a new training script by copying ptuning/train_chat.sh and modify dataset paths and hyperparameters (ptuning/train_chat.sh)
3. Update ptuning/arguments.py to add any task-specific command-line arguments for your training (ptuning/arguments.py)
4. Extend ptuning/trainer.py or ptuning/trainer_seq2seq.py to override compute_loss() if your task requires custom loss calculation (ptuning/trainer.py)
5. Run training with your new script and save checkpoint, then use utils.py load_model() to load the fine-tuned weights in web_demo.py (utils.py)

Add a custom chat interface

1. Create a new file (e.g., web_demo_custom.py) and import the core inference utilities from utils.py (utils.py)
2. Use load_model_on_gpu() or load_model() to initialize the model with your device strategy (utils.py)
3. Implement your chat loop using the same build_prompt() and response_wrapper() patterns seen in cli_demo.py (cli_demo.py)
4. Wrap your interface with Gradio (like web_demo.py) or Flask/FastAPI (like api.py) for deployment

Deploy model as a production API

1. Review api.py to understand the current endpoint structure (POST /api/chat, /api/completions, etc.) (api.py)
2. Load model using utils.py with device='cuda' or specify a quantized variant for inference speed (utils.py)
3. Add request validation, rate limiting, and error handling to api.py endpoints (api.py)
4. Deploy using ptuning/deepspeed.json or custom launcher script with torch.distributed for multi-GPU serving (ptuning/deepspeed.json)

Evaluate fine-tuned model performance

1. Prepare evaluation dataset in same format as training data (JSONL with prompt and expected output) (ptuning/data_sample.jsonl)
2. Use ptuning/evaluate.sh or ptuning/evaluate_finetune.sh scripts to run inference on test set (ptuning/evaluate.sh)
3. Load checkpoint with utils.py load_model() and compare generations against baseline using metrics defined in ptuning/trainer.py (ptuning/trainer.py)

• PyTorch + Transformers 4.27.1 — Industry standard for LLM inference and fine-tuning; ChatGLM-6B is optimized for this stack
• Gradio — Minimal-code web UI framework that quickly wraps Python functions; ideal for demo applications without frontend engineering overhead
• Hugging Face Trainer + P-Tuning v2 — Parameter-efficient fine-tuning reduces memory footprint from 13GB+ to ~5GB; allows adaptation on consumer GPUs
• DeepSpeed — Multi-GPU training orchestration; enables distributed fine-tuning across limited hardware
• cpm_kernels (CUDA-optimized kernels) — Custom CUDA kernels for ChatGLM-specific operations (RoPE, attention) to reduce inference latency

• 6B parameter model instead of larger (13B+)

  • Why: Balance between capability and accessibility: fits in ~13GB VRAM, runnable on consumer GPUs (RTX 3090, A100 40GB)
  • Consequence: Lower performance on complex reasoning vs. 13B+ models; may hallucinate or make factual errors (documented in limitations/)

• P-Tuning v2 instead of full fine-tuning

  • Why: Drastically reduce GPU memory and training time; leverage pre-trained knowledge
  • Consequence: Smaller adaptation capacity; less effective for domain shifts vs. full fine-tuning; requires careful prompt design

• Bilingual (Chinese + English) single model

  • Why: Serve both markets with one model download; efficient multi-lingual representation
  • Consequence: Slightly lower performance per language vs. language-specific models; larger model size than English-only variant

• Synchronous inference in CLI/Web demos

  • Why: Simple, blocking implementation; suitable for local single-user or low-concurrency deployments
  • Consequence: Cannot serve multiple concurrent requests without async refactor; may hang UI during generation

• Not production-grade multi-user serving (no async batching, queuing, or request pooling built-in)
• Not a real-time dialogue system (no streaming interruption or cancellation; must wait for full generation)
• Not a replacement for fine-tuned task-specific models (generic chat model, not domain-optimized)
• Not a multimodal model by default (web_demo_vision.py is optional variant; base model is text-only)
• Does not handle authentication or multi-tenant isolation (local deployment assumed)

Model requires HuggingFace model download (implicit, not cached in repo) via transformers library—first run slow unless cached. P-Tuning requires 7GB VRAM minimum; INT4 quantization not automatically applied in training scripts. Vision demo (cli_demo_vision.py) suggests vision capabilities but model architecture/weights not fully documented. Chinese-specific tokenization via sentencepiece; mixing languages may degrade quality. No explicit test suite—demos are the 'tests'. API.py assumes no authentication; production use needs securing.

• INT4 Quantization — Enables 6B model to run on 6GB consumer GPUs instead of 24GB+; core enabler for accessibility this repo emphasizes
• P-Tuning v2 — Parameter-efficient fine-tuning method implemented in ptuning/ allowing model adaptation with <7GB VRAM; key for downstream task customization
• Reinforcement Learning from Human Feedback (RLHF) — Training technique mentioned in README as enabling human-preference alignment; explains why responses feel more natural than base GPT-style models
• Token Context Window (4096/8192) — Implicit architectural constraint affecting dialogue length and fine-tuning sequence length configuration in ptuning/arguments.py
• DeepSpeed ZeRO — Memory optimization in ptuning/deepspeed.json enables multi-GPU training of 6B model without massive memory duplication
• Bilingual Cross-lingual Transfer — Model trained on ~1T tokens Chinese+English; architectural choice affecting tokenization (sentencepiece) and response quality across languages
• Supervised Fine-tuning (SFT) — Pre-RLHF stage applied to ChatGLM-6B for dialogue optimization; relevant for understanding train_chat.sh workflow in ptuning/

• THUDM/GLM-4 — Direct successor repository with improved 9B open model and GLM-4-0520 API, represents active development direction
• THUDM/P-tuning-v2 — Core parameter-efficient fine-tuning framework that ptuning/ directory implements and depends on
• THUDM/CodeGeeX2 — Specialized code generation variant of ChatGLM2-6B, demonstrates domain-specific model adaptation pattern
• lm-sys/FastChat — Alternative open LLM serving framework with similar inference/API capabilities, commonly used alongside ChatGLM for evaluation
• oobabooga/text-generation-webui — Web UI framework that supports ChatGLM-6B loading and inference, popular for local model deployment

To work on one of these in Claude Code or Cursor, paste: Implement the "<title>" PR idea from CLAUDE.md, working through the checklist as the task list.

Create unified web demo launcher with configuration management

The repo has multiple web demo files (web_demo.py, web_demo2.py, web_demo_old.py, web_demo_vision.py, ptuning/web_demo.py) with likely duplicate code and unclear differences. Consolidate these into a single configurable launcher with CLI arguments to select vision/standard/ptuning modes, reducing maintenance burden and improving user experience.

• [ ] Analyze differences between web_demo.py, web_demo2.py, web_demo_vision.py, and ptuning/web_demo.py
• [ ] Create a unified web_demo.py with --mode flag (standard/vision/ptuning)
• [ ] Extract common Gradio UI components into utils.py helper functions
• [ ] Update README.md with single web demo launch instructions
• [ ] Add deprecation notices to old web_demo_old.py and legacy variants

Add comprehensive test suite for api.py and cli_demo.py

The main entry points (api.py, cli_demo.py, cli_demo_vision.py) have no visible test coverage. Add unit tests for model loading, inference, and error handling to catch regressions early and document expected behavior for contributors.

• [ ] Create tests/test_api.py with fixtures for mock model loading
• [ ] Create tests/test_cli_demo.py with parametrized test cases for different prompt types
• [ ] Add tests for edge cases: empty prompts, token limits, CUDA unavailability
• [ ] Create GitHub Actions workflow (.github/workflows/tests.yml) to run pytest on PR
• [ ] Add pytest and pytest-cov to requirements.txt

Create structured fine-tuning documentation with example workflows

The ptuning/ directory has training scripts (train.sh, train_chat.sh, ds_train_finetune.sh) but ptuning/README.md likely lacks step-by-step guidance. Add a comprehensive guide with example commands, dataset format specifications (referencing data_sample.jsonl), and DeepSpeed configuration explanation to lower barrier to entry.

• [ ] Expand ptuning/README.md with 'Quick Start' section using actual commands from train.sh
• [ ] Document data format with annotated ptuning/data_sample.jsonl examples
• [ ] Add section explaining deepspeed.json parameters and when to adjust them
• [ ] Create ptuning/DATASET_FORMAT.md specifying JSON schema for custom datasets
• [ ] Add troubleshooting section for common CUDA/OOM errors in fine-tuning

• Add unit tests for ptuning/arguments.py dataclass validation—currently no test coverage for hyperparameter bounds (e.g., learning_rate ranges, batch_size constraints).
• Expand cli_demo_vision.py documentation and add example images in examples/ folder with vision use-case walkthroughs (currently documented less than text demos).
• Create a Makefile or requirements-dev.txt to separate inference dependencies (transformers, torch) from optional dev/training dependencies (deepspeed, accelerate)—currently all required together in requirements.txt.

• High · Outdated Transformers Library — requirements.txt - transformers==4.27.1. The dependency file specifies transformers==4.27.1, which is significantly outdated (released in early 2023). This version likely contains known security vulnerabilities and bugs that have been patched in newer releases. Modern versions provide critical security fixes for model loading, tokenization, and potential code execution vectors. Fix: Update to the latest stable version of transformers (4.40.0+). Review the changelog for security-related fixes and test thoroughly before deployment.
• High · Unspecified Dependency Versions — requirements.txt - protobuf, cpm_kernels, gradio, mdtex2html, sentencepiece, accelerate. Multiple critical dependencies lack version pinning: protobuf, cpm_kernels, torch (only >=1.10), gradio, mdtex2html, sentencepiece, and accelerate. This allows installation of vulnerable versions and introduces supply chain attack risk. Unpinned versions can lead to unexpected behavior or security issues. Fix: Pin all dependencies to specific versions (e.g., protobuf==4.24.0). Use tools like pip-audit to identify vulnerable versions and establish a dependency update policy.
• High · Web Demo Exposure Risk — web_demo.py, web_demo2.py, web_demo_vision.py, ptuning/web_demo.py. Multiple web demo files (web_demo.py, web_demo2.py, web_demo_old.py, web_demo_vision.py, ptuning/web_demo.py) suggest the application can be exposed via web interface. Without evidence of authentication, HTTPS, rate limiting, or input validation, these could be vulnerable to unauthorized access, prompt injection, DoS attacks, and information disclosure. Fix: Implement authentication and authorization controls. Use HTTPS/TLS. Add rate limiting and request validation. Sanitize user inputs to prevent prompt injection. Include CORS and security headers (CSP, X-Frame-Options, etc.).
• High · API Endpoint Without Security Controls — api.py. The api.py file suggests an API endpoint for the ChatGLM model. Without visible security measures, this endpoint is vulnerable to unauthorized access, injection attacks, resource exhaustion, and model abuse. LLM APIs are common targets for exploitation. Fix: Implement API authentication (API keys, OAuth2, or JWT). Add rate limiting per user/IP. Validate and sanitize all inputs. Implement request/response size limits. Monitor for abuse patterns. Use HTTPS with proper TLS configuration.
• Medium · Loose Torch Version Constraint — requirements.txt - torch>=1.10. torch>=1.10 is too permissive, potentially allowing installation of versions with known vulnerabilities. PyTorch releases can have security-relevant updates that may not be captured by such loose constraints. Fix: Pin torch to a specific minor version (e.g., torch==2.1.2). Test compatibility before upgrading. Subscribe to PyTorch security advisories.
• Medium · MDTeX2HTML HTML Injection Risk — requirements.txt - mdtex2html (unversioned). The mdtex2html library converts Markdown/LaTeX to HTML. If user input is processed through this library without proper sanitization, it could lead to XSS attacks or HTML injection. The library's version is unspecified, potentially including vulnerable versions. Fix: Specify a secure version of mdtex2html. Sanitize all user-generated markdown/latex inputs before processing. Use a security-focused HTML sanitizer (e.g., bleach) on output. Implement CSP headers to mitigate XSS.
• Medium · Model Training Scripts Lack Input Validation — ptuning/main.py, ptuning/trainer.py, ptuning/data_sample.jsonl. The ptuning directory contains training and fine-tuning scripts (main.py, trainer.py, train.sh, train_chat.sh) that process external data (data_sample.jsonl). These scripts lack visible input validation, potentially allowing injection of malicious training data or model poisoning attacks. Fix: Implement strict input validation for training data. Verify file integrity with checksums/signatures. Sanitize data paths to prevent directory traversal. Log and audit all training operations. Restrict file

LLM-derived; treat as a starting point, not a security audit.

• Open issues — current backlog
• Recent PRs — what's actively shipping
• Source on GitHub

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| # | What we check | Why it matters | |---|---|---| | 1 | You're in zai-org/ChatGLM-6B | Confirms the artifact applies here, not a fork | | 2 | License is still Apache-2.0 | Catches relicense before you depend on it | | 3 | Default branch main exists | Catches branch renames | | 4 | 5 critical file paths still exist | Catches refactors that moved load-bearing code | | 5 | Last commit ≤ 711 days ago | Catches sudden abandonment since generation |

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if ! git rev-parse --git-dir >/dev/null 2>&1; then
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fi

# 1. Repo identity
git remote get-url origin 2>/dev/null | grep -qE "zai-org/ChatGLM-6B(\\.git)?\\b" \\
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