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README.md

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+---
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- shining-valiant
+- shining-valiant-3
+- valiant
+- valiant-labs
+- mistral3
+- mistral
+- mistral-common
+- ministral-3-14b
+- ministral
+- reasoning
+- code
+- code-reasoning
+- science
+- science-reasoning
+- physics
+- biology
+- chemistry
+- earth-science
+- astronomy
+- machine-learning
+- artificial-intelligence
+- compsci
+- computer-science
+- information-theory
+- ML-Ops
+- math
+- cuda
+- deep-learning
+- transformers
+- agentic
+- LLM
+- neuromorphic
+- self-improvement
+- complex-systems
+- cognition
+- linguistics
+- philosophy
+- logic
+- epistemology
+- simulation
+- game-theory
+- knowledge-management
+- creativity
+- problem-solving
+- architect
+- engineer
+- developer
+- creative
+- analytical
+- expert
+- rationality
+- conversational
+- chat
+- instruct
+- mlx
+- mlx-my-repo
+base_model: ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3
+datasets:
+- sequelbox/Celestia3-DeepSeek-R1-0528
+- sequelbox/Mitakihara-DeepSeek-R1-0528
+- sequelbox/Raiden-DeepSeek-R1
+license: apache-2.0
+---
+
+# introvoyz041/Ministral-3-14B-Reasoning-2512-ShiningValiant3-mlx-4Bit
+
+The Model [introvoyz041/Ministral-3-14B-Reasoning-2512-ShiningValiant3-mlx-4Bit](https://huggingface.co/introvoyz041/Ministral-3-14B-Reasoning-2512-ShiningValiant3-mlx-4Bit) was converted to MLX format from [ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3](https://huggingface.co/ValiantLabs/Ministral-3-14B-Reasoning-2512-ShiningValiant3) using mlx-lm version **0.28.3**.
+
+## Use with mlx
+
+```bash
+pip install mlx-lm
+```
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("introvoyz041/Ministral-3-14B-Reasoning-2512-ShiningValiant3-mlx-4Bit")
+
+prompt="hello"
+
+if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
+    messages = [{"role": "user", "content": prompt}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+response = generate(model, tokenizer, prompt=prompt, verbose=True)
+```