minicpm5-2b-distilled-reasoning GGUF
GGUF quantizations of jigs97022/minicpm5-2b-distilled-reasoning.
A 2B parameter reasoning model distilled from three frontier models — Qwen3.8-Max, GLM-5.2, and Kimi K3 — onto the efficient openbmb/MiniCPM5-2B architecture.
Trained via QLoRA on 10,000 quality-filtered reasoning traces covering math, code, logic puzzles, and structured problem-solving.
Quantization Table
| File | Bits | Size | Use Case |
|---|---|---|---|
model_f16.gguf |
16 | ~5.0 GB | Maximum quality, reference |
model_q8_0.gguf |
8 | ~2.7 GB | Best quality / size tradeoff |
model_q6_k.gguf |
6 | ~2.1 GB | High quality |
model_q5_k_m.gguf |
5 | ~1.8 GB | Recommended |
model_q5_k_s.gguf |
5 | ~1.7 GB | Slightly smaller Q5 |
model_q4_k_m.gguf |
4 | ~1.5 GB | Good balance |
model_q4_k_s.gguf |
4 | ~1.4 GB | Smaller Q4 |
model_q3_k_l.gguf |
3 | ~1.2 GB | Low RAM, large variant |
model_q3_k_m.gguf |
3 | ~1.1 GB | Low RAM |
model_q3_k_s.gguf |
3 | ~1.0 GB | Minimum RAM Q3 |
model_q2_k.gguf |
2 | ~0.8 GB | Extreme compression |
VRAM Requirements
| Quant | Min VRAM |
|---|---|
| F16 | 8 GB |
| Q8_0 | 4 GB |
| Q4_K_M | 2 GB |
| Q2_K | 1.5 GB |
Training Details
| Parameter | Value |
|---|---|
| Base Model | openbmb/MiniCPM5-2B |
| Teachers | Qwen3.8-Max, GLM-5.2, Kimi K3 |
| Dataset | r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation |
| Train Samples | 10,000 |
| Method | QLoRA (r=64, alpha=32) |
| Max Context | 4,096 tokens |
| Hardware | Kaggle T4 x2 |
Usage
llama.cpp
./llama-cli -m model_q4_k_m.gguf \
--temp 0.7 \
-n 1024 \
-p "If 3x + 7 = 22, what is x? Show step-by-step reasoning."
llama-cpp-python
from llama_cpp import Llama
llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=4096)
output = llm(
"Solve step by step: If 3x + 7 = 22, what is x?",
max_tokens=1024,
temperature=0.7,
)
print(output["choices"][0]["text"])
LM Studio
Search tinyopsec/minicpm5-2b-distilled-reasoning-GGUF in the model browser.
Ollama
ollama run hf.co/tinyopsec/minicpm5-2b-distilled-reasoning-GGUF:Q4_K_M
Limitations
- Reasoning quality may degrade beyond 4K context
- Trained on 10K samples subset; edge cases may be weaker than full 52K variant
- English and Chinese only
- Knowledge cutoff ~2024 (inherited from MiniCPM5-2B base)