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Model: SirSahOl/glm-edge-1.5b-chat-mlx-16bit Source: Original Platform
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README.md
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README.md
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---
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frameworks:
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- Pytorch
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license: other
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license_name: glm-4
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license_link: LICENSE
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pipeline_tag: text-generation
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tags:
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- glm
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- edge
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- mlx
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inference: false
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base_model: THUDM/glm-edge-1.5b-chat
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library_name: mlx
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---
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# GLM-Edge-1.5B-Chat (16-bit MLX Full-Precision)
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This repository contains Zhipu AI's GLM-Edge-1.5B-Chat in its original unquantized 16-bit float (bfloat16) precision. It is compiled natively for Apple Silicon under the MLX framework.
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16-bit precision operates with 0% quantization loss. It preserves the exact mathematical weights of the original model.
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## Performance Benchmarks
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* **Inference Speed**: ~32.50 tokens per second (base M1 Apple Silicon)
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* **VRAM Footprint**: ~2.94 GB
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* **Memory Efficiency**: Highly optimized for M-series unified memory architecture. Runs smoothly on 8GB machines.
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## Installation
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Install the MLX LM package.
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```bash
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pip install mlx-lm
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```
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## Usage
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### Command Line Interface
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Chat with the model in your terminal.
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```bash
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mlx_lm.chat --model SirSahOl/glm-edge-1.5b-chat-mlx-16bit
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```
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### Python API
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Load and generate text programmatically.
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("SirSahOl/glm-edge-1.5b-chat-mlx-16bit")
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messages = [{"role": "user", "content": "Explain quantum superposition."}]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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## Multi-Quantization Comparison
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Evaluate your hardware budget and choose the optimal precision:
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| Variant | Disk Size | VRAM Footprint | M1 Speed | Key Advantage |
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| :--- | :--- | :--- | :--- | :--- |
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| **[4-bit MLX](https://huggingface.co/SirSahOl/glm-edge-1.5b-chat-mlx-4bit)** | ~800 MB | ~0.87 GB | ~72.5 tokens/sec | Maximum speed, lowest RAM. |
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| **[8-bit MLX](https://huggingface.co/SirSahOl/glm-edge-1.5b-chat-mlx-8bit)** | ~1.56 GB | ~1.56 GB | ~48.5 tokens/sec | Lossless balance, highly stable reasoning. |
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| **16-bit MLX** (This Repo) | ~2.94 GB | ~3.00 GB | **~32.5 tokens/sec** | Raw full-precision, absolute peak quality. |
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## Limitations
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* Largest footprint of the three variants. Use only if raw precision and unquantized quality are critical for your tasks.
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## LM Studio Configuration (Universal Preset Fix)
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If you load this model in LM Studio, you must configure custom Stop Strings to prevent the model from entering an infinite self-dialogue loop.
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### Option A: Automatic Preset (Recommended)
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You can create a custom prompt preset to configure all settings automatically. Create a JSON file named `GLM-Edge.json` inside your LM Studio config directory:
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* **macOS / Linux**: `~/.lmstudio/config-presets/GLM-Edge.json`
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* **Windows**: `%USERPROFILE%\.lmstudio\config-presets\GLM-Edge.json`
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Add the following JSON content:
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```json
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{
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"name": "GLM-Edge",
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"inference_params": {
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"pre_prompt": "You are a helpful, direct, and honest assistant.",
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"input_prefix": "<|user|>\n",
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"input_suffix": "\n<|assistant|>\n",
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"pre_prompt_prefix": "<|system|>\n",
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"pre_prompt_suffix": "\n",
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"antiprompt": [
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"<|user|>",
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"<|observation|>",
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"<|endoftext|>"
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],
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"stopStrings": [
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"<|user|>",
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"<|observation|>",
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"<|endoftext|>"
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],
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"temperature": 0.7,
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"max_tokens": 2048
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}
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}
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```
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Restart LM Studio, open a Chat session, and select **"GLM-Edge"** from the Prompt Template dropdown.
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### Option B: Manual Configuration
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Alternatively, configure the settings manually in the **Advanced Configuration** sidebar:
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1. **Stop Strings (Antiprompts / stopStrings)**: Add `<|user|>`, `<|observation|>`, and `<|endoftext|>`
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2. **Prompt Formatting**:
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* **User Prefix**: `<|user|>\n`
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* **Assistant Suffix**: `\n<|assistant|>\n`
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* **System Prefix**: `<|system|>\n`
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* **System Suffix**: `\n`
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chat_template.jinja
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{% for item in messages %}{% if item['role'] == 'system' %}<|system|>
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{{ item['content'] }}{% elif item['role'] == 'user' %}<|user|>
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{{ item['content'] }}{% elif item['role'] == 'assistant' %}<|assistant|>
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{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>
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{% endif %}
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config.json
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"architectures": [
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"GlmForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 6144,
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"max_position_embeddings": 8192,
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"model_type": "glm",
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"num_attention_heads": 16,
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"partial_rotary_factor": 1.0,
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"rms_norm_eps": 1e-05,
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"rope_theta": 10000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.47.0.dev0",
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"use_cache": true,
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"vocab_size": 59264
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}
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generation_config.json
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"_from_model_config": true,
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"transformers_version": "4.47.0.dev0"
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}
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||||||
|
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|
||||||
|
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|
||||||
|
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||||||
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"model.norm.weight": "model.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
483534
tokenizer.json
Normal file
483534
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
34
tokenizer_config.json
Normal file
34
tokenizer_config.json
Normal file
@@ -0,0 +1,34 @@
|
|||||||
|
{
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"do_lower_case": false,
|
||||||
|
"eos_token": "<|endoftext|>",
|
||||||
|
"extra_special_tokens": [
|
||||||
|
"<|endoftext|>",
|
||||||
|
"[MASK]",
|
||||||
|
"[gMASK]",
|
||||||
|
"[sMASK]",
|
||||||
|
"<sop>",
|
||||||
|
"<eop>",
|
||||||
|
"<|system|>",
|
||||||
|
"<|user|>",
|
||||||
|
"<|assistant|>",
|
||||||
|
"<|observation|>",
|
||||||
|
"<|begin_of_image|>",
|
||||||
|
"<|end_of_image|>",
|
||||||
|
"<|begin_of_video|>",
|
||||||
|
"<|end_of_video|>"
|
||||||
|
],
|
||||||
|
"image_size": 448,
|
||||||
|
"is_local": true,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_input_names": [
|
||||||
|
"input_ids",
|
||||||
|
"attention_mask"
|
||||||
|
],
|
||||||
|
"model_max_length": 8192,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"remove_space": false,
|
||||||
|
"tokenizer_class": "TokenizersBackend"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user