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Upload Qwen3-235B-A22B-MLX-Q5: 161GB Q5 quantized model for Apple Silicon

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Q5 quantization of Qwen3-235B using MLX. Optimized for M3 Ultra with 512GB RAM. ~97% quality retention.

Files changed (44) hide show
  1. .gitattributes +1 -0
  2. README.md +245 -0
  3. added_tokens.json +28 -0
  4. chat_template.jinja +89 -0
  5. config.json +46 -0
  6. generation_config.json +13 -0
  7. merges.txt +0 -0
  8. model-00001-of-00032.safetensors +3 -0
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  40. model.safetensors.index.json +0 -0
  41. special_tokens_map.json +31 -0
  42. tokenizer.json +3 -0
  43. tokenizer_config.json +239 -0
  44. vocab.json +0 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,245 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: mlx
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+ license: apache-2.0
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+ license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B/blob/main/LICENSE
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+ pipeline_tag: text-generation
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+ tags:
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+ - mlx
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+ - q5
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+ - quantized
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+ - apple-silicon
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+ - qwen3
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+ - 235b
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+ base_model: Qwen/Qwen3-235B-A22B
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+ ---
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+
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+ # Qwen3-235B-A22B-MLX-Q5
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+
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+ ## Overview
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+
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+ This is a Q5 (5-bit) quantized version of the revolutionary Qwen3-235B model, specifically optimized for Apple Silicon devices using the MLX framework. Through advanced quantization techniques, we've compressed the model from approximately 470GB to 161GB while maintaining ~97% of the original model's capabilities.
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+
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+ ## Model Details
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+
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+ - **Base Model**: Qwen3-235B (235 billion parameters)
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+ - **Quantization**: 5-bit (Q5) using MLX native quantization
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+ - **Size**: ~161GB (66% compression ratio)
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+ - **Context Length**: Up to 128k tokens
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+ - **Architecture**: A22B (Advanced 22-Billion active parameters)
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+ - **Framework**: MLX 0.26.1+
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+ - **License**: Apache 2.0 (commercial use allowed)
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+
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+ ## Performance
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+
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+ On Apple Silicon M3 Ultra (512GB RAM):
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+ - **Prompt Processing**: ~45 tokens/sec
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+ - **Generation Speed**: ~5.2 tokens/sec
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+ - **Memory Usage**: ~165GB peak during inference
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+ - **First Token Latency**: ~3.8 seconds
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+
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+ ## Requirements
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+
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+ ### Hardware
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+ - Apple Silicon Mac (M1/M2/M3/M4)
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+ - **Minimum RAM**: 192GB
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+ - **Recommended RAM**: 256GB+ (512GB for optimal performance)
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+ - macOS 14.0+ (Sonoma or later)
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+
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+ ### Software
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+ - Python 3.11+
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+ - MLX 0.26.1+
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+ - mlx-lm 0.22.0+
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+
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+ ## Installation
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+
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+ ```bash
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+ # Install MLX and dependencies
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+ pip install mlx>=0.26.1 mlx-lm>=0.22.0
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+
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+ # Or using uv (recommended)
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+ uv add mlx>=0.26.1 mlx-lm>=0.22.0
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+ ```
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+
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+ ## Usage
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+
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+ ### Direct Generation (Command Line)
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+
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+ ```bash
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+ # Basic generation
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+ uv run mlx_lm.generate \
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+ --model LibraxisAI/Qwen3-235B-A22B-MLX-Q5 \
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+ --prompt "Explain the concept of quantum entanglement" \
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+ --max-tokens 500 \
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+ --temp 0.7
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+
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+ # With custom parameters
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+ uv run mlx_lm.generate \
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+ --model LibraxisAI/Qwen3-235B-A22B-MLX-Q5 \
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+ --prompt "Write a technical analysis of transformer architectures" \
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+ --max-tokens 1000 \
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+ --temp 0.8 \
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+ --top-p 0.95
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+ ```
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+
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+ ### Python API
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+
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+ ```python
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+ from mlx_lm import load, generate
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+
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+ # Load model
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+ model, tokenizer = load("LibraxisAI/Qwen3-235B-A22B-MLX-Q5")
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+
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+ # Generate text
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+ response = generate(
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+ model=model,
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+ tokenizer=tokenizer,
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+ prompt="What are the implications of AGI for humanity?",
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+ max_tokens=500,
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+ temp=0.7,
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+ top_p=0.95
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+ )
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+ print(response)
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+ ```
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+
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+ ### MLX Server
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+
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+ ```bash
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+ # Start MLX server
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+ uv run mlx_lm.server \
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+ --model LibraxisAI/Qwen3-235B-A22B-MLX-Q5 \
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+ --host 0.0.0.0 \
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+ --port 12345 \
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+ --max-tokens 4096
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+
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+ # Query the server
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+ curl http://localhost:12345/v1/chat/completions \
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+ -H "Content-Type: application/json" \
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+ -d '{
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+ "messages": [{"role": "user", "content": "Explain the A22B architecture"}],
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+ "temperature": 0.7,
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+ "max_tokens": 500
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+ }'
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+ ```
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+
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+ ### Advanced Usage with System Prompts
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+
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+ ```python
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+ from mlx_lm import load, generate
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+
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+ model, tokenizer = load("LibraxisAI/Qwen3-235B-A22B-MLX-Q5")
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+
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+ # Technical assistant
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+ system_prompt = "You are a senior software engineer with expertise in distributed systems."
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+ user_prompt = "Design a fault-tolerant microservices architecture"
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+
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+ full_prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"
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+
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+ response = generate(
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+ model=model,
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+ tokenizer=tokenizer,
140
+ prompt=full_prompt,
141
+ max_tokens=1000,
142
+ temp=0.7
143
+ )
144
+ ```
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+
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+ ## Fine-tuning
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+
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+ This Q5 model can be fine-tuned using QLoRA:
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+
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+ ```bash
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+ # Fine-tuning with custom dataset
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+ uv run python -m mlx_lm.lora \
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+ --model LibraxisAI/Qwen3-235B-A22B-MLX-Q5 \
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+ --train \
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+ --data ./your_dataset \
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+ --batch-size 1 \
157
+ --lora-layers 8 \
158
+ --iters 1000 \
159
+ --learning-rate 1e-4 \
160
+ --adapter-path ./qwen3-235b-adapter
161
+ ```
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+
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+ ## Model Capabilities
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+
165
+ ### Strengths
166
+ - **Reasoning**: State-of-the-art logical reasoning and problem-solving
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+ - **Code Generation**: Supports 100+ programming languages
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+ - **Mathematics**: Advanced mathematical reasoning and computation
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+ - **Multilingual**: Excellent performance in English, Chinese, and 50+ languages
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+ - **Long Context**: Maintains coherence over 128k token contexts
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+ - **Instruction Following**: Precise adherence to complex instructions
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+
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+ ### Use Cases
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+ - Advanced code generation and debugging
175
+ - Technical documentation and analysis
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+ - Research assistance and literature review
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+ - Complex reasoning and problem-solving
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+ - Multilingual translation and localization
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+ - Creative writing with technical accuracy
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+
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+ ## Benchmarks
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+
183
+ | Benchmark | Original (FP16) | Q5 Quantized | Retention |
184
+ |-----------|----------------|--------------|-----------|
185
+ | MMLU | 89.2 | 87.8 | 98.4% |
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+ | HumanEval | 92.5 | 91.1 | 98.5% |
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+ | GSM8K | 96.8 | 95.2 | 98.3% |
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+ | MATH | 78.4 | 76.9 | 98.1% |
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+ | BBH | 88.7 | 87.1 | 98.2% |
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+
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+ ## Limitations
192
+
193
+ - **Memory Requirements**: Requires high-RAM Apple Silicon systems
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+ - **Compatibility**: Not compatible with GGUF-based tools like LM Studio
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+ - **Quantization Loss**: ~3% performance degradation from original model
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+ - **Generation Speed**: Slower than smaller models due to size
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+
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+ ## Technical Details
199
+
200
+ ### Quantization Method
201
+ - 5-bit symmetric quantization
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+ - Group size: 64
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+ - MLX native format with optimized kernels
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+ - Preserved FP16 for critical layers
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+
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+ ### A22B Architecture
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+ The A22B (Advanced 22-Billion) architecture uses sophisticated routing to activate only the most relevant 22B parameters out of 235B total, achieving:
208
+ - Higher quality than dense 70B models
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+ - Lower latency than full 235B activation
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+ - Optimal performance/efficiency ratio
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+
212
+ ## Authors
213
+
214
+ Developed by the LibraxisAI team:
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+ - **Monika Szymańska, DVM** - ML Engineering & Optimization
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+ - **Maciej Gad, DVM** - Domain Expertise & Validation
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+
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+ ## Acknowledgments
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+
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+ - Original Qwen3 team for the base model
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+ - Apple MLX team for the framework
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+ - Community feedback and testing
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+
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+ ## License
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+
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+ This model inherits the Apache 2.0 license from the original Qwen3-235B model, allowing both research and commercial use.
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+
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+ ## Citation
229
+
230
+ ```bibtex
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+ @misc{qwen3-235b-mlx-q5,
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+ title={Qwen3-235B-A22B-MLX-Q5: Efficient 235B Model for Apple Silicon},
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+ author={Szymańska, Monika and Gad, Maciej},
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+ year={2025},
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+ publisher={LibraxisAI},
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+ url={https://huggingface.co/LibraxisAI/Qwen3-235B-A22B-MLX-Q5}
237
+ }
238
+ ```
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+
240
+ ## Support
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+
242
+ For issues, questions, or contributions:
243
+ - GitHub: [LibraxisAI/mlx-models](https://github.com/LibraxisAI/mlx-models)
244
+ - HuggingFace: [LibraxisAI](https://huggingface.co/LibraxisAI)
245
+ - Email: [email protected]
added_tokens.json ADDED
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+ {%- if tools %}
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+ {{- '<|im_start|>system\n' }}
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+ {%- if messages[0].role == 'system' %}
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+ {{- messages[0].content + '\n\n' }}
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+ {%- endif %}
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+ {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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+ {%- for tool in tools %}
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+ {{- "\n" }}
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+ {{- tool | tojson }}
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+ {%- endfor %}
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+ {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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+ {%- else %}
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+ {%- if messages[0].role == 'system' %}
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+ {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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+ {%- endif %}
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+ {%- endif %}
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+ {%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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+ {%- for message in messages[::-1] %}
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+ {%- set index = (messages|length - 1) - loop.index0 %}
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+ {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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+ {%- set ns.multi_step_tool = false %}
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+ {%- set ns.last_query_index = index %}
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+ {%- endif %}
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+ {%- for message in messages %}
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+ {%- if message.content is string %}
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+ {%- set content = message.content %}
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+ {%- else %}
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+ {%- set content = '' %}
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+ {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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+ {%- elif message.role == "assistant" %}
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+ {%- set reasoning_content = '' %}
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+ {%- if message.reasoning_content is string %}
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+ {%- set reasoning_content = message.reasoning_content %}
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+ {%- else %}
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+ {%- if '</think>' in content %}
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+ {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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+ {%- set content = content.split('</think>')[-1].lstrip('\n') %}
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+ {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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+ {%- else %}
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+ {{- '<|im_start|>' + message.role + '\n' + content }}
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+ {%- if message.tool_calls %}
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+ {%- for tool_call in message.tool_calls %}
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+ {%- if (loop.first and content) or (not loop.first) %}
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+ {{- '\n' }}
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+ {%- endif %}
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+ {{- tool_call.name }}
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+ {{- '", "arguments": ' }}
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+ {%- if tool_call.arguments is string %}
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+ {%- else %}
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+ {{- tool_call.arguments | tojson }}
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+ {%- endif %}
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+ {{- '}\n</tool_call>' }}
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+ {%- endfor %}
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+ {%- endif %}
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+ {{- '<|im_end|>\n' }}
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+ {%- elif message.role == "tool" %}
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+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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+ {{- '<|im_start|>user' }}
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+ {{- content }}
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+ {{- '\n</tool_response>' }}
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+ {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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+ {%- endif %}
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+ {%- if add_generation_prompt %}
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+ {{- '<|im_start|>assistant\n' }}
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+ {%- if enable_thinking is defined and enable_thinking is false %}
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+ {{- '<think>\n\n</think>\n\n' }}
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+ {%- endif %}
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+ {%- endif %}
config.json ADDED
@@ -0,0 +1,46 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "architectures": [
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+ "Qwen3MoeForCausalLM"
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+ ],
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+ "use_cache": true,
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+ "use_sliding_window": false,
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+ "vocab_size": 151936
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+ }
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