---

base_model: ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo

tags:

• gguf
• llama.cpp
• quantized
• grpo
• reasoning

license: apache-2.0

language:

• en

pipeline_tag: text-generation

---

reasoning-gym-chain-sum-qwen3-1.7b-grpo — GGUF

GGUF quantizations of ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo,

a GRPO (Group Relative Policy Optimization) reinforcement-learning fine-tune, converted

with llama.cpp.

| Field | Value |

|---|---|

| Source checkpoint | ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo |

| Base model | Qwen/Qwen3-1.7B |

| Training | GRPO (group-relative RL) on the chain_sum arithmetic-reasoning task |

| Dataset / environment | OpenEnv reasoning_gym — chain_sum (agentic env) |

| Quantization tool | llama.cpp convert_hf_to_gguf.py + llama-quantize |

Available quantizations

| File | Size | Notes |

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

| reasoning-gym-chain-sum-qwen3-1-7b-grpo.q4_k_m.gguf | 1.11 GB (recommended) | 4-bit K-quant medium; best size/quality balance |

| reasoning-gym-chain-sum-qwen3-1-7b-grpo.q5_k_m.gguf | 1.26 GB (balanced) | 5-bit K-quant medium; near-full quality |

| reasoning-gym-chain-sum-qwen3-1-7b-grpo.q8_0.gguf | 1.83 GB (largest) | 8-bit; closest to the source precision |

Recommended default: Q4_K_M. For maximum fidelity use Q8_0.

Usage

llama.cpp

# One-shot
llama-cli -hf ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo-GGUF --jinja -p "Your prompt here" -n 256

# Interactive chat
llama-cli -hf ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo-GGUF --jinja -cnv

Ollama

ollama run hf.co/ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo-GGUF:Q4_K_M

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo-GGUF",
    filename="*q4_k_m.gguf",
    n_ctx=4096,
)
out = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Your prompt here"}],
    max_tokens=256,
)
print(out["choices"][0]["message"]["content"])

Intended use

Research and non-commercial experimentation. This model was RL-tuned on the chain_sum arithmetic-reasoning task;

it is a reasoning demonstrator, not a general-purpose assistant. Verify outputs before

any downstream use.

Limitations

• GGUF quantizations carry unavoidable quality loss relative to the source weights;

prefer Q8_0 when fidelity matters.

• Inherits every limitation of the source checkpoint

(ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo).

• Optimized for the chain_sum arithmetic-reasoning task; capability on unrelated tasks is not guaranteed.

Citation

@misc{reasoning_gym_chain_sum_qwen3_1_7b_grpo_gguf,
  author       = {Ermia Azarkhalili},
  title        = {reasoning-gym-chain-sum-qwen3-1.7b-grpo — GGUF quantized},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ermiaazarkhalili/reasoning-gym-chain-sum-qwen3-1-7b-grpo-GGUF}}
}