---

license: llama3.2

base_model: logan7000/cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-phi4mini-math345-groupB-llama32-end

tags:

- llama

- llama-cpp

- gguf

- math

- reasoning

- grpo

- quantized

language:

- en

pipeline_tag: text-generation

library_name: gguf

---

cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-phi4mini-math345-groupB-llama32-end · GGUF

GGUF quantizations of logan7000/cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-phi4mini-math345-groupB-llama32-end.

Quantized by tinyopsec using llama.cpp.

---

Model Overview

This is a 3.2B parameter language model fine-tuned from meta-llama/Llama-3.2-3B-Instruct using the GRPO (Group Relative Policy Optimization) method introduced in the DeepSeekMath paper.

It is part of the CogRPO N3 Ring experiment by logan7000, which explores heterogeneous model merging and multi-group reinforcement learning for mathematical reasoning. The groupB-llama32-end variant represents the Llama-3.2-3B branch of the ring fine-tuning pipeline, trained on q1716523669/MATH-Level345.

Architecture: LlamaForCausalLM

Context length: 32,768 tokens

Training data: MATH Level 3–4–5 competition problems

---

Quantization Table

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

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

| model_f16.gguf | 16 | 6.4 GB | Full precision, reference |

| model_q8_0.gguf | 8 | 3.4 GB | Best quality, needs more RAM |

| model_q6_k.gguf | 6 | 2.6 GB | Near-lossless, recommended |

| model_q5_k_m.gguf | 5 | 2.3 GB | Balanced quality/size |

| model_q5_k_s.gguf | 5 | 2.1 GB | Slightly smaller Q5 |

| model_q4_k_m.gguf | 4 | 2.0 GB | Recommended default |

| model_q4_k_s.gguf | 4 | 1.9 GB | Smaller Q4, minor quality loss |

| model_q3_k_l.gguf | 3 | 1.7 GB | Low RAM, acceptable quality |

| model_q3_k_m.gguf | 3 | 1.6 GB | Lower RAM |

| model_q3_k_s.gguf | 3 | 1.4 GB | Minimum viable quality |

| model_q2_k.gguf | 2 | 1.2 GB | Extreme compression, lowest quality |

---

VRAM / RAM Requirements

| Quant | Min RAM | Recommended |

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

| F16 | 8 GB | 10 GB |

| Q8_0 | 5 GB | 6 GB |

| Q6_K | 4 GB | 5 GB |

| Q5_K_M | 4 GB | 5 GB |

| Q4_K_M | 3 GB | 4 GB |

| Q3_K_M | 3 GB | 4 GB |

| Q2_K | 2 GB | 3 GB |

All quants run on CPU-only. GPU is optional.

---

Usage

llama.cpp (CLI)

./llama-cli \
  -m model_q4_k_m.gguf \
  -p "Solve: If x^2 + 5x + 6 = 0, find x." \
  -n 512 \
  --temp 0.6 \
  --top-p 0.95 \
  --min-p 0.0

llama.cpp (Server)

./llama-server \
  -m model_q4_k_m.gguf \
  --port 8080 \
  --ctx-size 8192

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="tinyopsec/cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-phi4mini-math345-groupB-llama32-end-GGUF",
    filename="model_q4_k_m.gguf",
)

output = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Solve: x^2 - 7x + 12 = 0"}],
    temperature=0.6,
    top_p=0.95,
    min_p=0.0,
    max_tokens=512,
)
print(output["choices"][0]["message"]["content"])

LM Studio

1. Open LM Studio → Search
2. Search: tinyopsec/cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-phi4mini-math345-groupB-llama32-end-GGUF
3. Download model_q4_k_m.gguf
4. Load and chat

Ollama

ollama run hf.co/tinyopsec/cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-phi4mini-math345-groupB-llama32-end-GGUF:Q4_K_M

---

Recommended Sampling Parameters

This model benefits from low min_p (set to 0.0) to avoid repetition loops, consistent with the base Llama-3.2 family:

| Parameter | Value |

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

| temperature | 0.6 |

| top_p | 0.95 |

| min_p | 0.0 |

| repeat_penalty | 1.0 |

---

Original Model

• Original: logan7000/cogrpo-n3-ring-qwen25-3b-x-llama32-3b-x-phi4mini-math345-groupB-llama32-end
• Base model: meta-llama/Llama-3.2-3B-Instruct
• Author: logan7000
• Method: GRPO · DeepSeekMath paper
• Training framework: TRL