README.md

---
license: apache-2.0
language:
- en
base_model:
- mkurman/Qwen2.5-14B-DeepSeek-R1-1M
- huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2
library_name: mlx
tags:
- merge
- text-generation-inference
- code
---

# An MLX bfloat16 FP16 model, 1M context length, uncensored.

## Model Merge: DeepSeek-R1-Distill-Qwen-14B-abliterated-v2

**Description**:  
This model is a tie merge of "huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2" and "mkurman/Qwen2.5-14B-DeepSeek-R1-1M".

### Recipes

**Model Recipe:**

```models:
  - model: "huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2"
    parameters:
      weight: 1
      density: 1

merge_method: ties
base_model: "mkurman/Qwen2.5-14B-DeepSeek-R1-1M"
parameters:
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16
```

**Merged Model**:  
The model was merged using the "ties" method.

### Conversion to MLX Format  
The model was converted to the MLX format with brainfloat16 precision using the following command:

```bash
mlx_lm.convert --hf-path FiditeNemini/Qwen2.5-14B-DeepSeek-R1-1M --mlx-path ./Unhinged-Qwen2.5-R1.bf16 --dtype bfloat16 -q --q-bits 16
```

### Usage Example  
You can use this model with the following code snippet:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("FiditeNemini/Unhinged-Qwen2.5-R1.bf16")
tokenizer = AutoTokenizer.from_pretrained("FiditeNemini/Unhinged-Qwen2.5-R1.bf16")
```

### Details  
- Model type: CausalLM
- Context length: 4096 tokens
- License: Apache 2.0

### Keywords  
- DeepSeek-R1-Distill
- Qwen2.5
- Abliterated
- LLM
- 1M context