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MiniCPM5-1B-Agentic-v9
Created by GLM-5.2. Model 9 in the agentic post-training series.
Training: SFT on 10 hand-crafted agentic trajectories (8 epochs, lr=5e-6) teaching the model to use bash tools concisely without excessive reasoning loops.
Evaluation
| Metric | Score |
|---|---|
| Agentic Eval (20 tasks) | 30.0% (6/20) |
| Real-World Tasks (12 tasks) | 0% (0/12) |
Tasks passed: create_file, fibonacci, fix_bug2, grep_search, csv_analyze, merge_files
GGUFs available: f16, q8_0, q5_k_m, q4_k_m, q3_k_m, q2_k
Quantization Recommendations
This is a 1B model — heavier quantization degrades output quality significantly.
| Quant | Quality | Size | Recommendation |
|---|---|---|---|
| f16 | Full | ~2.1GB | Best quality |
| q8_0 | Excellent | ~1.1GB | Recommended — near-identical to f16 |
| q5_k_m | Good | ~0.8GB | Reasoning OK, response may degrade on longer outputs |
| q4_k_m | Fair | ~0.7GB | Reasoning OK, response degrades into repetition |
| q3_k_m | Poor | ~0.6GB | Not recommended |
| q2_k | Poor | ~0.5GB | Not recommended |
For production use, prefer q8_0 or f16. The model uses reasoning tokens; lower quantizations break the transition from reasoning to response.
Chat Template
The GGUF chat template defaults to enable_thinking=true, so the model will always produce reasoning followed by response. If your inference engine supports enable_thinking=false, you can skip reasoning for faster responses.
Tool Use
The model supports bash tool calls using the <function name="bash"> XML format. The chat template includes tool definitions when tools are provided.