Instructions to use witflag/Synex 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 witflag/Synex 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 witflag/Synex:Q4_K_M # Run inference directly in the terminal: llama cli -hf witflag/Synex:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf witflag/Synex:Q4_K_M # Run inference directly in the terminal: llama cli -hf witflag/Synex: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 witflag/Synex:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf witflag/Synex: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 witflag/Synex:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf witflag/Synex:Q4_K_M
Use Docker
docker model run hf.co/witflag/Synex:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use witflag/Synex with Ollama:
ollama run hf.co/witflag/Synex:Q4_K_M
- Unsloth Studio
How to use witflag/Synex 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 witflag/Synex 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 witflag/Synex to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for witflag/Synex to start chatting
- Pi
How to use witflag/Synex with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf witflag/Synex: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": "witflag/Synex:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use witflag/Synex with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf witflag/Synex: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 witflag/Synex:Q4_K_M
Run Hermes
hermes
- OpenClaw new
How to use witflag/Synex with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf witflag/Synex: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 "witflag/Synex: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 witflag/Synex with Docker Model Runner:
docker model run hf.co/witflag/Synex:Q4_K_M
- Lemonade
How to use witflag/Synex with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull witflag/Synex:Q4_K_M
Run and chat with the model
lemonade run user.Synex-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 27,283 Bytes
b5f6756 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 | ---
base_model: TeichAI/GLM-4.7-Flash-Claude-Opus-4.5-High-Reasoning-Distill
tags:
- text-generation-inference
- gguf
- llama.cpp
- unsloth
- glm4_moe_lite
license: apache-2.0
datasets:
- TeichAI/claude-4.5-opus-high-reasoning-250x
---
# 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
---
## 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](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |