Text Generation
Transformers
Safetensors
GGUF
English
qwen3_5_moe_text
Merge
custom-merge
layer-wise-merge
depth-aligned-merge
coding
code-generation
abliterated
reasoning
qwen3
qwen3-coder
reap
Mixture of Experts
conversational
Instructions to use Johnblick187/Nexus-Coder-5Q3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Johnblick187/Nexus-Coder-5Q3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Johnblick187/Nexus-Coder-5Q3") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Johnblick187/Nexus-Coder-5Q3") model = AutoModelForCausalLM.from_pretrained("Johnblick187/Nexus-Coder-5Q3") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use Johnblick187/Nexus-Coder-5Q3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Johnblick187/Nexus-Coder-5Q3", filename="heretic-nexus-coder-Q3_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Johnblick187/Nexus-Coder-5Q3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M
Use Docker
docker model run hf.co/Johnblick187/Nexus-Coder-5Q3:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Johnblick187/Nexus-Coder-5Q3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Johnblick187/Nexus-Coder-5Q3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Johnblick187/Nexus-Coder-5Q3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Johnblick187/Nexus-Coder-5Q3:Q4_K_M
- SGLang
How to use Johnblick187/Nexus-Coder-5Q3 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Johnblick187/Nexus-Coder-5Q3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Johnblick187/Nexus-Coder-5Q3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Johnblick187/Nexus-Coder-5Q3" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Johnblick187/Nexus-Coder-5Q3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Johnblick187/Nexus-Coder-5Q3 with Ollama:
ollama run hf.co/Johnblick187/Nexus-Coder-5Q3:Q4_K_M
- Unsloth Studio new
How to use Johnblick187/Nexus-Coder-5Q3 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Johnblick187/Nexus-Coder-5Q3 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Johnblick187/Nexus-Coder-5Q3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Johnblick187/Nexus-Coder-5Q3 to start chatting
- Pi new
How to use Johnblick187/Nexus-Coder-5Q3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Johnblick187/Nexus-Coder-5Q3:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Johnblick187/Nexus-Coder-5Q3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Johnblick187/Nexus-Coder-5Q3:Q4_K_M
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 Johnblick187/Nexus-Coder-5Q3:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Johnblick187/Nexus-Coder-5Q3 with Docker Model Runner:
docker model run hf.co/Johnblick187/Nexus-Coder-5Q3:Q4_K_M
- Lemonade
How to use Johnblick187/Nexus-Coder-5Q3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Johnblick187/Nexus-Coder-5Q3:Q4_K_M
Run and chat with the model
lemonade run user.Nexus-Coder-5Q3-Q4_K_M
List all available models
lemonade list
| { | |
| "architectures": [ | |
| "Qwen3_5MoeForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_output_gate": true, | |
| "bos_token_id": 248044, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 248044, | |
| "full_attention_interval": 4, | |
| "head_dim": 256, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "linear_conv_kernel_dim": 4, | |
| "linear_key_head_dim": 128, | |
| "linear_num_key_heads": 16, | |
| "linear_num_value_heads": 32, | |
| "linear_value_head_dim": 128, | |
| "mamba_ssm_dtype": "float32", | |
| "max_position_embeddings": 262144, | |
| "model_type": "qwen3_5_moe_text", | |
| "moe_intermediate_size": 512, | |
| "mtp_num_hidden_layers": 1, | |
| "mtp_use_dedicated_embeddings": false, | |
| "num_attention_heads": 16, | |
| "num_experts": 256, | |
| "num_experts_per_tok": 8, | |
| "num_hidden_layers": 40, | |
| "num_key_value_heads": 2, | |
| "output_router_logits": false, | |
| "pad_token_id": null, | |
| "partial_rotary_factor": 0.25, | |
| "rms_norm_eps": 1e-06, | |
| "rope_parameters": { | |
| "mrope_interleaved": true, | |
| "mrope_section": [ | |
| 11, | |
| 11, | |
| 10 | |
| ], | |
| "partial_rotary_factor": 0.25, | |
| "rope_theta": 10000000, | |
| "rope_type": "default" | |
| }, | |
| "router_aux_loss_coef": 0.001, | |
| "shared_expert_intermediate_size": 512, | |
| "tie_word_embeddings": false, | |
| "transformers_version": "5.5.4", | |
| "use_cache": true, | |
| "vocab_size": 248320 | |
| } | |