Instructions to use kernelpool/GLM-5.1-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use kernelpool/GLM-5.1-6bit with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("kernelpool/GLM-5.1-6bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use kernelpool/GLM-5.1-6bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "kernelpool/GLM-5.1-6bit"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "kernelpool/GLM-5.1-6bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use kernelpool/GLM-5.1-6bit with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "kernelpool/GLM-5.1-6bit"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default kernelpool/GLM-5.1-6bit
Run Hermes
hermes
- MLX LM
How to use kernelpool/GLM-5.1-6bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "kernelpool/GLM-5.1-6bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "kernelpool/GLM-5.1-6bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kernelpool/GLM-5.1-6bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 1,709 Bytes
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"architectures": [
"GlmMoeDsaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"dtype": "bfloat16",
"eos_token_id": [
154820,
154827,
154829
],
"ep_size": 1,
"first_k_dense_replace": 3,
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 6144,
"index_head_dim": 128,
"index_n_heads": 32,
"index_topk": 2048,
"indexer_rope_interleave": true,
"initializer_range": 0.02,
"intermediate_size": 12288,
"kv_lora_rank": 512,
"max_position_embeddings": 202752,
"model_type": "glm_moe_dsa",
"moe_intermediate_size": 2048,
"moe_layer_freq": 1,
"n_group": 1,
"n_routed_experts": 256,
"n_shared_experts": 1,
"norm_topk_prob": true,
"num_attention_heads": 64,
"num_experts_per_tok": 8,
"num_hidden_layers": 78,
"num_key_value_heads": 64,
"num_nextn_predict_layers": 1,
"pad_token_id": 154820,
"pretraining_tp": 1,
"q_lora_rank": 2048,
"qk_head_dim": 256,
"qk_nope_head_dim": 192,
"qk_rope_head_dim": 64,
"quantization": {
"group_size": 64,
"bits": 6,
"mode": "affine"
},
"quantization_config": {
"group_size": 64,
"bits": 6,
"mode": "affine"
},
"rms_norm_eps": 1e-05,
"rope_interleave": true,
"rope_parameters": {
"rope_theta": 1000000,
"rope_type": "default"
},
"routed_scaling_factor": 2.5,
"scoring_func": "sigmoid",
"tie_word_embeddings": false,
"topk_group": 1,
"topk_method": "noaux_tc",
"transformers_version": "5.4.0",
"use_cache": true,
"v_head_dim": 256,
"vocab_size": 154880
} |