Rewnozom-GGUF
GGUF conversion of Rewnozom/Rewnozom, derived from
Qwen/Qwen2.5-7B-Instruct-1M.
- Original model:
Rewnozom/Rewnozom - Converted model:
Rewnozom/Rewnozom-GGUF - Format: GGUF
- Runtime targets:
llama.cpp, Ollama, LM Studio, Jan, and other GGUF loaders
Personal recommendation: start with the two hybrid MX profiles, MX-k_quants
and MX-legacy-quants, because they keep the sensitive tensors higher while
leaving the rest on a compact base quant.
Available Quantizations
Only quantizations with an actual .gguf file in this workspace are listed.
| Quant | File | Approx size |
|---|---|---|
F32 | F32/Rewnozom-1M-LR.F32.gguf | 28.38 GB |
F16 | F16/Rewnozom-1M-LR.F16.gguf | 14.19 GB |
BF16 | BF16/Rewnozom-1M-LR.BF16.gguf | 14.19 GB |
MX-k_quants | MX-k_quants/Rewnozom-1M-LR.Q4_K_M.k_quants_hybrid_q4km_q5_q6.gguf | 4.62 GB |
MX-legacy-quants | MX-legacy-quants/Rewnozom-1M-LR.Q4_0.legacy_hybrid_q40_q50_q80.gguf | 5.05 GB |
MXFP4_MOE | extra_current_llama_quants/MXFP4_MOE/Rewnozom-1M-LR.MXFP4_MOE.gguf | 7.54 GB |
Q1_0 | extra_current_llama_quants/Q1_0/Rewnozom-1M-LR.Q1_0.gguf | 1.35 GB |
Q2_0 | extra_current_llama_quants/Q2_0/Rewnozom-1M-LR.Q2_0.gguf | 2.42 GB |
Q2_K | k_quants/Q2_K/Rewnozom-1M-LR.Q2_K.gguf | 2.81 GB |
Q3_K_L | k_quants/Q3_K_L/Rewnozom-1M-LR.Q3_K_L.gguf | 3.81 GB |
Q3_K_M | k_quants/Q3_K_M/Rewnozom-1M-LR.Q3_K_M.gguf | 3.55 GB |
Q3_K_S | k_quants/Q3_K_S/Rewnozom-1M-LR.Q3_K_S.gguf | 3.25 GB |
Q4_K_M | k_quants/Q4_K_M/Rewnozom-1M-LR.Q4_K_M.gguf | 4.36 GB |
Q4_K_S | k_quants/Q4_K_S/Rewnozom-1M-LR.Q4_K_S.gguf | 4.15 GB |
Q5_K_M | k_quants/Q5_K_M/Rewnozom-1M-LR.Q5_K_M.gguf | 5.07 GB |
Q5_K_S | k_quants/Q5_K_S/Rewnozom-1M-LR.Q5_K_S.gguf | 4.95 GB |
Q6_K | k_quants/Q6_K/Rewnozom-1M-LR.Q6_K.gguf | 5.82 GB |
Q4_0 | legacy_quants/Q4_0/Rewnozom-1M-LR.Q4_0.gguf | 4.13 GB |
Q4_1 | legacy_quants/Q4_1/Rewnozom-1M-LR.Q4_1.gguf | 4.54 GB |
Q5_0 | legacy_quants/Q5_0/Rewnozom-1M-LR.Q5_0.gguf | 4.95 GB |
Q5_1 | legacy_quants/Q5_1/Rewnozom-1M-LR.Q5_1.gguf | 5.36 GB |
Q8_0 | legacy_quants/Q8_0/Rewnozom-1M-LR.Q8_0.gguf | 7.54 GB |
TQ1_0 | t_quants/TQ1_0/Rewnozom-1M-LR.TQ1_0.gguf | 1.99 GB |
TQ2_0 | t_quants/TQ2_0/Rewnozom-1M-LR.TQ2_0.gguf | 2.28 GB |
Quantization Groups
| Group | Purpose |
|---|---|
F32 | Full float32 GGUF reference conversion. Highest precision, largest file. |
F16 | Float16 GGUF reference conversion. Good baseline for further quantization. |
BF16 | BFloat16 GGUF reference conversion for runtimes that prefer BF16. |
extra_current_llama_quants | Additional current llama.cpp-compatible quant types. |
k_quants | K-quant family, usually the best default family for local use. |
legacy_quants | Older GGUF quant family for compatibility and comparison. |
t_quants | Ternary/low-bit quant family for very small local deployments. |
ollama_modelfiles | Generated Ollama Modelfiles using the base system prompt from sp.md. |
Recommended Starting Points
| Use case | Quant |
|---|---|
| Personal recommendation, K-family hybrid | MX-k_quants |
| Personal recommendation, legacy hybrid | MX-legacy-quants |
| Best quality among compact K-quants | Q6_K |
| Balanced default | Q4_K_M |
| Smaller memory footprint | Q3_K_M or Q3_K_S |
| Very small local test | Q2_K, Q2_0, TQ2_0, or Q1_0 |
| Legacy compatibility check | Q4_0, Q4_1, Q5_0, Q5_1, Q8_0 |
Actual quality and speed depend on runtime, CPU/GPU offload, context length, and prompt workload. Validate the target quant against your real tasks before using it as a default.
llama.cpp
Run directly from a local GGUF file:
llama-cli -m k_quants/Q4_K_M/Rewnozom-1M-LR.Q4_K_M.gguf \
-p "Review this implementation plan for missing constraints."
Start an OpenAI-compatible local server:
llama-server -m k_quants/Q4_K_M/Rewnozom-1M-LR.Q4_K_M.gguf
Dataset Context
The model is associated with a synthetic reasoning/control-plane dataset family covering:
- boolean CSP logic
- branch-dependent task DAGs
- ordering and plan repair
- multi-hop forward inference
- request decomposition
- context relevance
- deterministic state transitions
- memory lifecycle
- retrieval/navigation policy
- executor routing
- tool execution
- permissions
- multi-agent orchestration
- result validation
- retry/escalation
- composite execution kernel behavior
The dataset design uses deterministic formal worlds, double oracle checks, structural dedupe, and machine-verifiable answers rather than synthetic prose chain-of-thought.
Limitations
- GGUF quantization changes numerical behavior compared with the source model.
- Lower-bit quants trade quality for memory and speed.
- Permission enforcement, destructive actions, and state mutation should remain controlled by deterministic application logic.
Attribution
This conversion is based on Rewnozom/Rewnozom, which is derived from
Qwen/Qwen2.5-7B-Instruct-1M and follows the Apache 2.0 license.
page:
Base model: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct-1M
Rewnozom/Rewnozom: https://huggingface.co/Rewnozom/Rewnozom
Rewnozom/Rewnozom-GGUF: https://huggingface.co/Rewnozom/Rewnozom-GGUF
Ollama: https://ollama.com/tobraa92/Rewnozom
Portfolio: https://tobiasraanaes.se/