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"} ] }'
| { | |
| "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 | |
| } |