Instructions to use bloomer010/Ling-3.0-flash-GGUF 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 bloomer010/Ling-3.0-flash-GGUF 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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use bloomer010/Ling-3.0-flash-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bloomer010/Ling-3.0-flash-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bloomer010/Ling-3.0-flash-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- Ollama
How to use bloomer010/Ling-3.0-flash-GGUF with Ollama:
ollama run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- Unsloth Desktop
- Pi
How to use bloomer010/Ling-3.0-flash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
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": "bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bloomer010/Ling-3.0-flash-GGUF with Docker Model Runner:
docker model run hf.co/bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
- Lemonade
How to use bloomer010/Ling-3.0-flash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.Ling-3.0-flash-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use bloomer010/Ling-3.0-flash-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
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 bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bloomer010/Ling-3.0-flash-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL
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 "bloomer010/Ling-3.0-flash-GGUF:UD-Q4_K_XL" \ --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"
Context size 131k ?
Hey!
First of all, thank you for quantizing this beautiful model! π€
Do this model have 131k context size? llama-server caps it to 131k when I specify 262k.
>>>
./llama-server --model "/home/dim/EvoX2/LLMs/models/bloomer010/Ling-3.0-flash-GGUF/Ling-3.0-flash-UD-Q6_K_XL.gguf" --host 192.168.1.86 --port 8080 --tools get_info --api-key XXX --jinja --chat-template-file "/home/dim/EvoX2/LLMs/models/bloomer010/Ling-3.0-flash-GGUF/chat_template.jinja" --gpu-layers all --parallel 4 --kv-unified --kv-offload --load-mode none --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 --ctx-size 262144 --batch-size 4096 --ubatch-size 2048 --swa-checkpoints 48 --timeout 999999 --temperature 0.7 --top-k 30 --repeat-penalty 1.05 --presence-penalty 0.05 --top-p 0.95 --min-p 0.05 --spec-type draft-mtp --spec-draft-n-max 3 --spec-draft-p-min 0.5 --cache-reuse 256
<<<
0.00.026.513 W server tools or MCP servers are enabled, using localhost as default CORS origin (change via --cors-origins)
0.00.034.359 I cmn common_param: common_params_print_info: verbosity = 3 (adjust with the `-lv N` CLI arg)
0.00.034.713 W srv llama_server: -----------------
0.00.034.714 W srv llama_server: the following feature(s) are enabled:
0.00.034.714 W srv llama_server: built-in tools (experimental)
0.00.034.714 W srv llama_server: do not expose the server to untrusted environments
0.00.034.715 W srv llama_server: -----------------
0.00.035.903 I srv load_model: loading model '/home/dim/EvoX2/LLMs/models/bloomer010/Ling-3.0-flash-GGUF/Ling-3.0-flash-UD-Q6_K_XL.gguf'
0.00.544.758 W load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
0.30.890.375 W llama_context: n_ctx_seq (262144) > n_ctx_train (131072) -- possible training context overflow
0.31.217.244 I cmn init: llama threadpool init, n_threads = 16
0.31.313.663 I common_speculative_init_result: creating MTP draft context against the target model '/home/dim/EvoX2/LLMs/models/bloomer010/Ling-3.0-flash-GGUF/Ling-3.0-flash-UD-Q6_K_XL.gguf'
0.31.313.695 W llama_context: n_ctx_seq (262144) > n_ctx_train (131072) -- possible training context overflow
0.31.482.507 W srv load_model: the slot context (262144) exceeds the training context of the model (131072) - capping <======== HERE
0.31.607.134 I srv load_model: initializing, n_slots = 4, n_ctx_slot = 131072, kv_unified = 'true'
0.31.853.533 I srv init: chat template supports preserving reasoning, consider enabling it via --reasoning-preserve
0.31.853.588 I srv llama_server: model loaded
0.31.853.594 I srv llama_server: listening on http://192.168.1.86:8080
Asking because officail model card declares 256k context size:
Context Training Schedule 8K -> 32K -> 256K
My apologies, as this was something I thought I had already corrected. MXFP4_MOE, Q1_0, IQ1_S, IQ1_M, IQ2_M, IQ3_XXS, UD-Q2_K_XL, Q3_K_M, Q4_K_M, Q5_K_M, Q5_K_S, Q6_K, Q8_0, and UD-Q6_K_XL have all been corrected and re-uploaded with 262k. BF16, Q4_K_S, and UD-Q4_K_XL and in process now, and will be soon to follow with the next hour or so (check file timestamps, if it is today it is updated).
Thank you!