Instructions to use dranger003/c4ai-command-r7b-12-2024-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 dranger003/c4ai-command-r7b-12-2024-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 dranger003/c4ai-command-r7b-12-2024-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dranger003/c4ai-command-r7b-12-2024-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
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 dranger003/c4ai-command-r7b-12-2024-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
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 dranger003/c4ai-command-r7b-12-2024-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
Use Docker
docker model run hf.co/dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
- LM Studio
- Jan
- vLLM
How to use dranger003/c4ai-command-r7b-12-2024-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dranger003/c4ai-command-r7b-12-2024-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": "dranger003/c4ai-command-r7b-12-2024-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
- Ollama
How to use dranger003/c4ai-command-r7b-12-2024-GGUF with Ollama:
ollama run hf.co/dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
- Unsloth Desktop
- Pi
How to use dranger003/c4ai-command-r7b-12-2024-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
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": "dranger003/c4ai-command-r7b-12-2024-GGUF:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use dranger003/c4ai-command-r7b-12-2024-GGUF with Docker Model Runner:
docker model run hf.co/dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
- Lemonade
How to use dranger003/c4ai-command-r7b-12-2024-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
Run and chat with the model
lemonade run user.c4ai-command-r7b-12-2024-GGUF-BF16
List all available models
lemonade list
- Hermes Agent
How to use dranger003/c4ai-command-r7b-12-2024-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 dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
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 dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use dranger003/c4ai-command-r7b-12-2024-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dranger003/c4ai-command-r7b-12-2024-GGUF:BF16
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 "dranger003/c4ai-command-r7b-12-2024-GGUF:BF16" \ --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"
Update README.md
Browse files
README.md
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./build/bin/llama-cli -fa --no-display-prompt -c 0 -m ggml-c4ai-command-r-7b-12-2024-q4_k.gguf -p "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Tell me all about yourself.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>"
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```
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https://github.com/ggerganov/llama.cpp/issues/10816#issuecomment-2548574766
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./build/bin/llama-cli -fa --no-display-prompt -c 0 -m ggml-c4ai-command-r-7b-12-2024-q4_k.gguf -p "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Tell me all about yourself.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|><|START_RESPONSE|>"
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```
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+
https://github.com/ggerganov/llama.cpp/issues/10816#issuecomment-2548574766
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```
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llama_new_context_with_model: n_seq_max = 1
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llama_new_context_with_model: n_ctx = 8192
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llama_new_context_with_model: n_ctx_per_seq = 8192
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llama_new_context_with_model: n_batch = 2048
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llama_new_context_with_model: n_ubatch = 512
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llama_new_context_with_model: flash_attn = 1
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llama_new_context_with_model: freq_base = 50000.0
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llama_new_context_with_model: freq_scale = 1
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llama_kv_cache_init: CPU KV buffer size = 1024.00 MiB
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llama_new_context_with_model: KV self size = 1024.00 MiB, K (f16): 512.00 MiB, V (f16): 512.00 MiB
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llama_new_context_with_model: CPU output buffer size = 0.98 MiB
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llama_new_context_with_model: CUDA0 compute buffer size = 1328.31 MiB
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llama_new_context_with_model: CUDA_Host compute buffer size = 24.01 MiB
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llama_new_context_with_model: graph nodes = 841
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llama_new_context_with_model: graph splits = 324 (with bs=512), 1 (with bs=1)
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common_init_from_params: setting dry_penalty_last_n to ctx_size = 8192
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common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
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main: llama threadpool init, n_threads = 16
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system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | AMX_INT8 = 1 | LLAMAFILE = 1 | OPENMP = 1 | AARCH64_REPACK = 1 |
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sampler seed: 2760461191
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sampler params:
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repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000
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dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 8192
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top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, temp = 0.800
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mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000
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sampler chain: logits -> logit-bias -> penalties -> dry -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist
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generate: n_ctx = 8192, n_batch = 2048, n_predict = -1, n_keep = 1
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I am Command, a sophisticated large language model built by the company Cohere. I assist users by providing thorough responses to a wide range of queries, offering information, and performing various tasks. My capabilities include answering questions, generating text, summarizing content, extracting data, and performing various other tasks based on the user's requirements.
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I strive to provide accurate and helpful information while ensuring a positive and informative user experience. Feel free to ask me about any topic, and I'll do my best to assist you! [end of text]
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llama_perf_sampler_print: sampling time = 15.07 ms / 128 runs ( 0.12 ms per token, 8491.44 tokens per second)
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llama_perf_context_print: load time = 1076.84 ms
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llama_perf_context_print: prompt eval time = 181.62 ms / 22 tokens ( 8.26 ms per token, 121.13 tokens per second)
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llama_perf_context_print: eval time = 4938.01 ms / 105 runs ( 47.03 ms per token, 21.26 tokens per second)
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llama_perf_context_print: total time = 5163.42 ms / 127 tokens
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```
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