Instructions to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Use Docker
docker model run hf.co/aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with Ollama:
ollama run hf.co/aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
- Unsloth Studio
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF to start chatting
- Pi
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with Docker Model Runner:
docker model run hf.co/aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
- Lemonade
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default aiworksofbt/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
GLM 4.7 Flash x Claude 4.5 Opus (High Reasoning)
This model was trained on a small reasoning dataset of Claude Opus 4.5, with reasoning effort set to High.
🧬 Datasets:
TeichAI/claude-4.5-opus-high-reasoning-250x
🏗 Base Model:
unsloth/GLM-4.7-Flash
⚡ Use cases:
- Coding
- Science
- Deep Research
∑ Stats (Dataset)
- Costs: $ 52.30 (USD)
- Total tokens (input + output): 2.13 M
How to run
For specific instructions/commands to serve this model locally using vLLM, SGLang, or transformers please see the instructions from the original model's card
For detailed instructions getting started with Llama.cpp please refer to the unsloth guide
Sampling Parameters
z-ai recommends the following sampling parameters for this model:
| Default Settings (Most Tasks) | Terminal Bench, SWE Bench Verified |
|---|---|
| temperature = 1.0 | temperature = 0.7 |
| top_p = 0.95 | top_p = 1.0 |
| repeat penalty = disabled or 1.0 | repeat penalty = disabled or 1.0 |
- For general use-case:
--temp 1.0 --top-p 0.95 - For tool-calling:
--temp 0.7 --top-p 1.0 - If using llama.cpp, set
--min-p 0.01as llama.cpp's default is 0.05 - Sometimes you'll need to experiment what numbers work best for your use-case.
If you experience any issues with these parameters, some users have reported better results when lowering temperature to 0.5-0.6
Benchmarks
Model Comparison vs Base
- Base model: zai-org/GLM-4.7-Flash
| Benchmark | Base Score | Distilled Score | Delta | Delta % |
|---|---|---|---|---|
| arc_challenge | 0.224403 | 0.217577 | -0.00682594 | -0.0304183 |
| gpqa_diamond_zeroshot | 0.262626 | 0.292929 | 0.030303 | 0.115385 |
| hellaswag | 0.257817 | 0.256722 | -0.0010954 | -0.00424874 |
| ifeval | 0.109057 | 0.112754 | 0.00369686 | 0.0338983 |
| mmlu | 0.229454 | 0.240706 | 0.011252 | 0.0490379 |
| truthfulqa_mc2 | 0.467552 | 0.466805 | -0.000747457 | -0.00159866 |
| winogrande | 0.468824 | 0.504341 | 0.035517 | 0.0757576 |
Aggregate Comparison
| Benchmarks Compared | Wins vs Base | Ties vs Base | Losses vs Base | Avg Delta |
|---|---|---|---|---|
| 7 | 4 | 0 | 3 | 0.0103 |
Detailed Results
| Model | Benchmark | Score | Total Questions | Total Correct |
|---|---|---|---|---|
| zai-org/GLM-4.7-Flash | winogrande | 0.468824 | 1267 | 594 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | winogrande | 0.504341 | 1267 | 639 |
| zai-org/GLM-4.7-Flash | arc_challenge | 0.224403 | 1172 | 263 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | arc_challenge | 0.217577 | 1172 | 255 |
| zai-org/GLM-4.7-Flash | hellaswag | 0.257817 | 10042 | 2589 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | hellaswag | 0.256722 | 10042 | 2578 |
| zai-org/GLM-4.7-Flash | truthfulqa_mc2 | 0.467552 | 817 | 381 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | truthfulqa_mc2 | 0.466805 | 817 | 381 |
| zai-org/GLM-4.7-Flash | mmlu | 0.229454 | 14042 | 3222 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | mmlu | 0.240706 | 14042 | 3380 |
| zai-org/GLM-4.7-Flash | ifeval | 0.109057 | 541 | 59 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | ifeval | 0.112754 | 541 | 61 |
| zai-org/GLM-4.7-Flash | gpqa_diamond_zeroshot | 0.262626 | 198 | 52 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | gpqa_diamond_zeroshot | 0.292929 | 198 | 58 |
MMLU Subject Breakdown
MMLU Detailed Results
| Model | Subject | Benchmark | Score | Total Questions | Total Correct |
|---|---|---|---|---|---|
| zai-org/GLM-4.7-Flash | formal_logic | mmlu_formal_logic | 0.285714 | 126 | 36 |
| zai-org/GLM-4.7-Flash | high_school_european_history | mmlu_high_school_european_history | 0.218182 | 165 | 36 |
| zai-org/GLM-4.7-Flash | high_school_us_history | mmlu_high_school_us_history | 0.25 | 204 | 51 |
| zai-org/GLM-4.7-Flash | high_school_world_history | mmlu_high_school_world_history | 0.270042 | 237 | 63 |
| zai-org/GLM-4.7-Flash | international_law | mmlu_international_law | 0.239669 | 121 | 29 |
| zai-org/GLM-4.7-Flash | jurisprudence | mmlu_jurisprudence | 0.259259 | 108 | 28 |
| zai-org/GLM-4.7-Flash | logical_fallacies | mmlu_logical_fallacies | 0.220859 | 163 | 36 |
| zai-org/GLM-4.7-Flash | moral_disputes | mmlu_moral_disputes | 0.248555 | 346 | 86 |
| zai-org/GLM-4.7-Flash | moral_scenarios | mmlu_moral_scenarios | 0.237989 | 895 | 213 |
| zai-org/GLM-4.7-Flash | philosophy | mmlu_philosophy | 0.186495 | 311 | 58 |
| zai-org/GLM-4.7-Flash | prehistory | mmlu_prehistory | 0.216049 | 324 | 70 |
| zai-org/GLM-4.7-Flash | professional_law | mmlu_professional_law | 0.245763 | 1534 | 377 |
| zai-org/GLM-4.7-Flash | world_religions | mmlu_world_religions | 0.321637 | 171 | 55 |
| zai-org/GLM-4.7-Flash | business_ethics | mmlu_business_ethics | 0.3 | 100 | 30 |
| zai-org/GLM-4.7-Flash | clinical_knowledge | mmlu_clinical_knowledge | 0.215094 | 265 | 57 |
| zai-org/GLM-4.7-Flash | college_medicine | mmlu_college_medicine | 0.208092 | 173 | 36 |
| zai-org/GLM-4.7-Flash | global_facts | mmlu_global_facts | 0.18 | 100 | 18 |
| zai-org/GLM-4.7-Flash | human_aging | mmlu_human_aging | 0.313901 | 223 | 70 |
| zai-org/GLM-4.7-Flash | management | mmlu_management | 0.174757 | 103 | 18 |
| zai-org/GLM-4.7-Flash | marketing | mmlu_marketing | 0.290598 | 234 | 68 |
| zai-org/GLM-4.7-Flash | medical_genetics | mmlu_medical_genetics | 0.3 | 100 | 30 |
| zai-org/GLM-4.7-Flash | miscellaneous | mmlu_miscellaneous | 0.237548 | 783 | 186 |
| zai-org/GLM-4.7-Flash | nutrition | mmlu_nutrition | 0.22549 | 306 | 69 |
| zai-org/GLM-4.7-Flash | professional_accounting | mmlu_professional_accounting | 0.234043 | 282 | 66 |
| zai-org/GLM-4.7-Flash | professional_medicine | mmlu_professional_medicine | 0.183824 | 272 | 50 |
| zai-org/GLM-4.7-Flash | virology | mmlu_virology | 0.283133 | 166 | 47 |
| zai-org/GLM-4.7-Flash | econometrics | mmlu_econometrics | 0.236842 | 114 | 27 |
| zai-org/GLM-4.7-Flash | high_school_geography | mmlu_high_school_geography | 0.176768 | 198 | 35 |
| zai-org/GLM-4.7-Flash | high_school_government_and_politics | mmlu_high_school_government_and_politics | 0.196891 | 193 | 38 |
| zai-org/GLM-4.7-Flash | high_school_macroeconomics | mmlu_high_school_macroeconomics | 0.202564 | 390 | 79 |
| zai-org/GLM-4.7-Flash | high_school_microeconomics | mmlu_high_school_microeconomics | 0.214286 | 238 | 51 |
| zai-org/GLM-4.7-Flash | high_school_psychology | mmlu_high_school_psychology | 0.192661 | 545 | 105 |
| zai-org/GLM-4.7-Flash | human_sexuality | mmlu_human_sexuality | 0.259542 | 131 | 34 |
| zai-org/GLM-4.7-Flash | professional_psychology | mmlu_professional_psychology | 0.25 | 612 | 153 |
| zai-org/GLM-4.7-Flash | public_relations | mmlu_public_relations | 0.218182 | 110 | 24 |
| zai-org/GLM-4.7-Flash | security_studies | mmlu_security_studies | 0.187755 | 245 | 46 |
| zai-org/GLM-4.7-Flash | sociology | mmlu_sociology | 0.238806 | 201 | 48 |
| zai-org/GLM-4.7-Flash | us_foreign_policy | mmlu_us_foreign_policy | 0.28 | 100 | 28 |
| zai-org/GLM-4.7-Flash | abstract_algebra | mmlu_abstract_algebra | 0.22 | 100 | 22 |
| zai-org/GLM-4.7-Flash | anatomy | mmlu_anatomy | 0.185185 | 135 | 25 |
| zai-org/GLM-4.7-Flash | astronomy | mmlu_astronomy | 0.177632 | 152 | 27 |
| zai-org/GLM-4.7-Flash | college_biology | mmlu_college_biology | 0.256944 | 144 | 37 |
| zai-org/GLM-4.7-Flash | college_chemistry | mmlu_college_chemistry | 0.2 | 100 | 20 |
| zai-org/GLM-4.7-Flash | college_computer_science | mmlu_college_computer_science | 0.26 | 100 | 26 |
| zai-org/GLM-4.7-Flash | college_mathematics | mmlu_college_mathematics | 0.21 | 100 | 21 |
| zai-org/GLM-4.7-Flash | college_physics | mmlu_college_physics | 0.215686 | 102 | 22 |
| zai-org/GLM-4.7-Flash | computer_security | mmlu_computer_security | 0.28 | 100 | 28 |
| zai-org/GLM-4.7-Flash | conceptual_physics | mmlu_conceptual_physics | 0.26383 | 235 | 62 |
| zai-org/GLM-4.7-Flash | electrical_engineering | mmlu_electrical_engineering | 0.241379 | 145 | 35 |
| zai-org/GLM-4.7-Flash | elementary_mathematics | mmlu_elementary_mathematics | 0.208995 | 378 | 79 |
| zai-org/GLM-4.7-Flash | high_school_biology | mmlu_high_school_biology | 0.174194 | 310 | 54 |
| zai-org/GLM-4.7-Flash | high_school_chemistry | mmlu_high_school_chemistry | 0.152709 | 203 | 31 |
| zai-org/GLM-4.7-Flash | high_school_computer_science | mmlu_high_school_computer_science | 0.25 | 100 | 25 |
| zai-org/GLM-4.7-Flash | high_school_mathematics | mmlu_high_school_mathematics | 0.211111 | 270 | 57 |
| zai-org/GLM-4.7-Flash | high_school_physics | mmlu_high_school_physics | 0.198675 | 151 | 29 |
| zai-org/GLM-4.7-Flash | high_school_statistics | mmlu_high_school_statistics | 0.152778 | 216 | 33 |
| zai-org/GLM-4.7-Flash | machine_learning | mmlu_machine_learning | 0.321429 | 112 | 36 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | formal_logic | mmlu_formal_logic | 0.206349 | 126 | 26 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_european_history | mmlu_high_school_european_history | 0.206061 | 165 | 34 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_us_history | mmlu_high_school_us_history | 0.245098 | 204 | 50 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_world_history | mmlu_high_school_world_history | 0.270042 | 237 | 63 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | international_law | mmlu_international_law | 0.239669 | 121 | 29 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | jurisprudence | mmlu_jurisprudence | 0.305556 | 108 | 33 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | logical_fallacies | mmlu_logical_fallacies | 0.214724 | 163 | 35 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | moral_disputes | mmlu_moral_disputes | 0.271676 | 346 | 93 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | moral_scenarios | mmlu_moral_scenarios | 0.222346 | 895 | 199 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | philosophy | mmlu_philosophy | 0.228296 | 311 | 71 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | prehistory | mmlu_prehistory | 0.271605 | 324 | 88 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | professional_law | mmlu_professional_law | 0.252934 | 1534 | 388 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | world_religions | mmlu_world_religions | 0.280702 | 171 | 48 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | business_ethics | mmlu_business_ethics | 0.3 | 100 | 30 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | clinical_knowledge | mmlu_clinical_knowledge | 0.267925 | 265 | 71 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | college_medicine | mmlu_college_medicine | 0.213873 | 173 | 37 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | global_facts | mmlu_global_facts | 0.32 | 100 | 32 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | human_aging | mmlu_human_aging | 0.327354 | 223 | 73 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | management | mmlu_management | 0.213592 | 103 | 22 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | marketing | mmlu_marketing | 0.286325 | 234 | 67 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | medical_genetics | mmlu_medical_genetics | 0.35 | 100 | 35 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | miscellaneous | mmlu_miscellaneous | 0.254151 | 783 | 199 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | nutrition | mmlu_nutrition | 0.222222 | 306 | 68 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | professional_accounting | mmlu_professional_accounting | 0.244681 | 282 | 69 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | professional_medicine | mmlu_professional_medicine | 0.183824 | 272 | 50 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | virology | mmlu_virology | 0.325301 | 166 | 54 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | econometrics | mmlu_econometrics | 0.280702 | 114 | 32 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_geography | mmlu_high_school_geography | 0.207071 | 198 | 41 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_government_and_politics | mmlu_high_school_government_and_politics | 0.176166 | 193 | 34 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_macroeconomics | mmlu_high_school_macroeconomics | 0.217949 | 390 | 85 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_microeconomics | mmlu_high_school_microeconomics | 0.222689 | 238 | 53 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_psychology | mmlu_high_school_psychology | 0.209174 | 545 | 114 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | human_sexuality | mmlu_human_sexuality | 0.21374 | 131 | 28 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | professional_psychology | mmlu_professional_psychology | 0.259804 | 612 | 159 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | public_relations | mmlu_public_relations | 0.309091 | 110 | 34 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | security_studies | mmlu_security_studies | 0.159184 | 245 | 39 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | sociology | mmlu_sociology | 0.253731 | 201 | 51 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | us_foreign_policy | mmlu_us_foreign_policy | 0.25 | 100 | 25 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | abstract_algebra | mmlu_abstract_algebra | 0.23 | 100 | 23 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | anatomy | mmlu_anatomy | 0.251852 | 135 | 34 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | astronomy | mmlu_astronomy | 0.164474 | 152 | 25 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | college_biology | mmlu_college_biology | 0.263889 | 144 | 38 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | college_chemistry | mmlu_college_chemistry | 0.22 | 100 | 22 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | college_computer_science | mmlu_college_computer_science | 0.22 | 100 | 22 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | college_mathematics | mmlu_college_mathematics | 0.25 | 100 | 25 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | college_physics | mmlu_college_physics | 0.245098 | 102 | 25 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | computer_security | mmlu_computer_security | 0.24 | 100 | 24 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | conceptual_physics | mmlu_conceptual_physics | 0.340426 | 235 | 80 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | electrical_engineering | mmlu_electrical_engineering | 0.193103 | 145 | 28 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | elementary_mathematics | mmlu_elementary_mathematics | 0.240741 | 378 | 91 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_biology | mmlu_high_school_biology | 0.190323 | 310 | 58 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_chemistry | mmlu_high_school_chemistry | 0.216749 | 203 | 44 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_computer_science | mmlu_high_school_computer_science | 0.19 | 100 | 19 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_mathematics | mmlu_high_school_mathematics | 0.240741 | 270 | 65 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_physics | mmlu_high_school_physics | 0.172185 | 151 | 26 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | high_school_statistics | mmlu_high_school_statistics | 0.194444 | 216 | 42 |
| TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill | machine_learning | mmlu_machine_learning | 0.241071 | 112 | 27 |
Benchmark Config
- Quantization: 4bit
- Temperature: 0.0
- Top P: 1.0
- Top K: 0
- Repetition Penalty: 1.0
All results were obtained through the official lm evaluation harness
This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Base model
zai-org/GLM-4.7-Flash

