Instructions to use logic65/whittle-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use logic65/whittle-dev with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf logic65/whittle-dev:Q8_0 # Run inference directly in the terminal: llama cli -hf logic65/whittle-dev:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf logic65/whittle-dev:Q8_0 # Run inference directly in the terminal: llama cli -hf logic65/whittle-dev:Q8_0
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 logic65/whittle-dev:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf logic65/whittle-dev:Q8_0
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 logic65/whittle-dev:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf logic65/whittle-dev:Q8_0
Use Docker
docker model run hf.co/logic65/whittle-dev:Q8_0
- LM Studio
- Jan
- Ollama
How to use logic65/whittle-dev with Ollama:
ollama run hf.co/logic65/whittle-dev:Q8_0
- Unsloth Desktop
- Pi
How to use logic65/whittle-dev with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf logic65/whittle-dev:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "logic65/whittle-dev:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use logic65/whittle-dev with Docker Model Runner:
docker model run hf.co/logic65/whittle-dev:Q8_0
- Lemonade
How to use logic65/whittle-dev with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull logic65/whittle-dev:Q8_0
Run and chat with the model
lemonade run user.whittle-dev-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use logic65/whittle-dev with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf logic65/whittle-dev:Q8_0
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 logic65/whittle-dev:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use logic65/whittle-dev with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf logic65/whittle-dev:Q8_0
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "logic65/whittle-dev:Q8_0" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Download agentfix2/bf16/config.json from logic65/whittle-dev: direct link, hf CLI and curl.
- Browser
- Download file 2.79 kB
-
https://huggingface.co/logic65/whittle-dev/resolve/main/agentfix2/bf16/config.json
- Command line
-
hf download hf://logic65/whittle-dev/agentfix2/bf16/config.json
-
curl -L -o config.json https://huggingface.co/logic65/whittle-dev/resolve/main/agentfix2/bf16/config.json
2.79 kB
| { | |
| "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": "qwen4_exp_text", | |
| "moe_intermediate_size": 512, | |
| "mtp_num_hidden_layers": 1, | |
| "mtp_use_dedicated_embeddings": false, | |
| "num_attention_heads": 16, | |
| "num_experts": 180, | |
| "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, | |
| "use_cache": true, | |
| "vocab_size": 248320, | |
| "architectures": [ | |
| "Qwen4ExpForCausalLM" | |
| ], | |
| "transformers_version": "4.57.1", | |
| "hc_count": 4, | |
| "hc_lowrank": 320, | |
| "indexer_n_heads": 4, | |
| "indexer_head_dim": 128, | |
| "indexer_budget": 262144, | |
| "indexer_compress_ratio": 4, | |
| "indexer_kv_heads": 1, | |
| "output_gate_type": "sigmoid", | |
| "ple_layer_ids": [ | |
| 2 | |
| ], | |
| "ngram_size": 3, | |
| "heads_per_ngram": 4, | |
| "ple_conv_kernel_size": 4, | |
| "split_ngram_parts": 5, | |
| "ple_embed_dim": 2048, | |
| "num_embeddings_per_layer_input": 256, | |
| "make_ngram_vocab_size_divisible_by": 128, | |
| "ngram_vocab_size_base": 4880000 | |
| } |