---

license: apache-2.0

base_model: reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT

base_model_relation: quantized

language:

• en

tags:

• qwen3
• gguf
• llama-cpp
• reasoning
• logical-inference
• stem
• distillation
• knowledge-distillation
• causal-lm
• edge

pipeline_tag: text-generation

library_name: gguf

---

Qwen3-1.7B-Coder-Distilled-SFT — GGUF

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT, quantized by tinyopsec.

A 1.7B model built in two stages: knowledge distillation from Qwen3-Coder-30B-A3B-Instruct (30B MoE teacher) to establish a structured STEM reasoning backbone, then SFT on ~54,600 logical inference problems. Architecture: Qwen3ForCausalLM.

---

Available Quantizations

| File | Bits | Approx. Size | Use Case |

|------|------|--------------|----------|

| model_f16.gguf | 16 | ~3.4 GB | Full precision, reference |

| model_q8_0.gguf | 8 | ~1.8 GB | Best quality, fits in RAM easily |

| model_q6_k.gguf | 6 | ~1.4 GB | Excellent quality, recommended |

| model_q5_k_m.gguf | 5 | ~1.2 GB | Great balance quality/size |

| model_q5_k_s.gguf | 5 | ~1.1 GB | Slightly smaller Q5 variant |

| model_q4_k_m.gguf | 4 | ~1.0 GB | Good quality, very portable |

| model_q4_k_s.gguf | 4 | ~0.95 GB | Smaller Q4 variant |

| model_q3_k_l.gguf | 3 | ~0.85 GB | Lower quality, minimal RAM |

| model_q3_k_m.gguf | 3 | ~0.80 GB | Minimal footprint |

| model_q3_k_s.gguf | 3 | ~0.75 GB | Smallest Q3 variant |

| model_q2_k.gguf | 2 | ~0.60 GB | Extreme compression, lowest quality |

---

VRAM Requirements

| Quantization | VRAM |

|---|---|

| F16 | ~3.4 GB |

| Q8_0 | ~1.8 GB |

| Q6_K | ~1.4 GB |

| Q5_K_M | ~1.2 GB |

| Q4_K_M | ~1.0 GB |

| Q3_K_M | ~0.80 GB |

| Q2_K | ~0.60 GB |

---

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "### Instruction:\nWhat can we infer from: All cats are mammals. Whiskers is a cat.\n\n### Response:" -n 256

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=1024)
output = llm(
    "### Instruction:\nWhat can we infer from: All cats are mammals. Whiskers is a cat.\n\n### Response:",
    max_tokens=256,
    stop=["### Instruction:"],
)
print(output["choices"][0]["text"])

LM Studio

Download any .gguf file from this repo and load it directly in LM Studio.

Ollama

ollama run hf.co/tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-GGUF

---

Prompt Formats

Logical inference (Stage 2 — primary):

### Instruction:
[Your question or logical inference problem]

### Response:

STEM derivation (Stage 1 — also supported):

Solve the following problem carefully and show a rigorous derivation.

Problem:
[Your problem]

Proof:

---

Model Details

| Attribute | Value |

|---|---|

| Architecture | Qwen3ForCausalLM |

| Parameters | ~2B (1.7B effective) |

| Base model | Qwen/Qwen3-1.7B |

| Teacher model | Qwen/Qwen3-Coder-30B-A3B-Instruct |

| Context length | 1024 tokens (training) |

| Precision | BF16 |

| License | Apache 2.0 |

Good for: Logical inference, propositional logic, formal reasoning, STEM derivation, structured argumentation, educational tutoring, edge deployment.

Not for: General code generation, formal proof verification (use Lean/Coq), safety-critical tasks, or long context beyond 1024 tokens.

---

Original Model

reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT by Convergent Intelligence LLC: Research Division.