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Mistral Small 4 119B uncensored via abliteration by TIMTEH. Refusal direction removed from layers 9-35.
About
Full abliteration of mistralai/Mistral-Small-4-119B-Instruct-2503 — no dataset changes, no fine-tuning, no capability loss. The refusal direction was identified and projected out of the model's residual stream across decoder layers 9-35, covering attention output projections and MLP down projections.
This is the first standard GGUF uncensored release of Mistral Small 4 119B. The only other uncensored variant is dealignai's JANG/MLX format (Apple Silicon only, ~80 downloads).
Architecture
119B total parameters — Mixture of Experts (128 routed experts + 1 shared expert per layer, 4 active per token)
36 decoder layers with Multi-Latent Attention (MLA): kv_lora_rank=256, q_lora_rank=1024
Multimodal base (vision tower removed for text-only GGUF — text capabilities fully preserved)
Released March 23, 2026 by Mistral AI
Downloads
File
Quant
Size
Use Case
Mistral-Small-4-119B-Uncensored-Q2_K.gguf
Q2_K
41 GB
Minimum viable — fits 48GB+
Mistral-Small-4-119B-Uncensored-Q3_K_M.gguf
Q3_K_M
54 GB
Budget quality — 64GB+ recommended
Mistral-Small-4-119B-Uncensored-Q4_K_M.gguf
Q4_K_M
68 GB
Best balance — 80GB+ VRAM
Mistral-Small-4-119B-Uncensored-Q5_K_M.gguf
Q5_K_M
79 GB
High quality — 96GB+ VRAM
Mistral-Small-4-119B-Uncensored-Q6_K.gguf
Q6_K
91 GB
Near-lossless — 2×48GB or 128GB+
Mistral-Small-4-119B-Uncensored-Q8_0.gguf
Q8_0
118 GB
Reference quality — 128GB+ VRAM
Mistral-Small-4-119B-Uncensored-BF16.gguf
BF16
222 GB
Full precision — 256GB+ VRAM
Recommended Settings
Temperature: 0.7-0.9 for creative, 0.3-0.5 for factual
Rep penalty: 1.05-1.15 (important for abliterated models — prevents loops)
Top-P: 0.9 | Top-K: 40
Context: Up to 32K tokens (model supports 128K but GGUF runtimes vary)
Abliteration Method
Model loaded across 8×H200 SXM5 GPUs with FP8→BF16 dequantization
Activations extracted from 30 harmful + 30 harmless prompt pairs
Per-layer refusal direction computed via mean difference of activations
Refusal direction projected out of o_proj (attention output) and down_proj (MLP) for layers 9-35
Modified weights saved as BF16 safetensors → converted to GGUF → quantized
No training, no dataset contamination, no capability degradation. The model retains 100% of its original knowledge and reasoning ability — only the refusal behavior is removed.
# Chat template
<s>[INST] Your message here [/INST]
Notes
This is a text-only GGUF. The vision tower from the original multimodal model was not included in conversion. All text/reasoning/coding capabilities are fully preserved.
Abliterated models may occasionally include brief disclaimers in responses — this is residual behavior from base training, not a refusal.
As with all uncensored models, use responsibly. The removal of safety guardrails means the model will comply with a wider range of requests.
Need a custom 120B+ model aligned to your proprietary data? TIMTEH provides bespoke enterprise fine-tuning, abliteration, and deployment on 8×H200 SXM5.
Custom fine-tuning on your data (up to 400B+ parameters)
