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cloudbjorn/Mistral-Medium-3.5-128B-Eschaton-Uncensored
Mistral-Medium-3.5-128B-Eschaton-Uncensored is a image-text-to-text model from cloudbjorn. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
This is a merged BF16 fine-tune of Mistral Medium 3.5 128B. Training loaded the full-BF16 axolotl-ai-co/Mistral-Medium-3.5-128B-BF16 checkpoint and used cloudbjorn/eschaton-uncensored with the Eschaton Engine.
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From the Hugging Face model README
This is a merged BF16 fine-tune of Mistral Medium 3.5 128B. Training loaded the full-BF16 axolotl-ai-co/Mistral-Medium-3.5-128B-BF16 checkpoint and used cloudbjorn/eschaton-uncensored with the Eschaton Engine.
Training used 4-bit NF4 QLoRA with BF16 compute. The resulting LoRA adapter was then merged into the original BF16 base checkpoint, so this repository contains the complete merged BF16 model rather than pre-quantized training weights.
The fine-tune focuses on direct, neutral, and useful responses to sensitive, gritty, controversial, emotionally intimate, and technically demanding prompts without repetitive moralizing or canned disclaimers.
The Eschaton Uncensored dataset emphasizes direct answers, task-appropriate tone, technical substance, dark creative work, and candid analysis. It is intended to reduce unnecessary refusals and boilerplate while preserving the broad capabilities of the underlying instruct model.
The training sequence length was 2,048 tokens. The base architecture's larger inference context window was not used as the training sequence length for this fine-tune.
| Parameter | Value |
|---|---|
| Base model | axolotl-ai-co/Mistral-Medium-3.5-128B-BF16 |
| Dataset | cloudbjorn/eschaton-uncensored |
| Framework | Eschaton Engine using Transformers, TRL, PEFT and bitsandbytes |
| Training method | 4-bit NF4 QLoRA |
| Quantization compute dtype | bfloat16 |
| Double quantization | Enabled |
| Final repository format | LoRA merged into the BF16 base model |
| Epochs | 1 |
| Training sequence length | 2,048 tokens |
| Packing | Disabled |
| Seed | 3407 |
The Eschaton Engine automatically selected its 60B–149B model profile for this 127.7B-parameter checkpoint.
| Parameter | Value |
|---|---|
Rank (r) | 32 |
| LoRA alpha | 64 |
| Target modules | all-linear in the language model, excluding vision and multimodal-projector modules |
| LoRA dropout | 0.05 |
| Bias | none |
| Task type | CAUSAL_LM |
| Parameter | Value |
|---|---|
| Optimizer | 8-bit paged AdamW |
| Per-device micro-batch size | 1 |
| Gradient accumulation | 32 |
| Effective batch size | 32 |
| Learning rate | 5e-6 |
| LR scheduler | Linear |
| Warmup steps | 50 |
| Weight decay | 0.01 |
| Gradient checkpointing | Enabled |
No standardized benchmark results are reported for this fine-tune. Users should evaluate it against their own instruction-following, reasoning, coding, safety, and domain-specific requirements before deployment.
This derivative follows the base model's Modified MIT License. Review that license and the upstream model card before use or redistribution.