Instructions to use CrowdMind/lamb-base 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 CrowdMind/lamb-base 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 CrowdMind/lamb-base:F16 # Run inference directly in the terminal: llama cli -hf CrowdMind/lamb-base:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf CrowdMind/lamb-base:F16 # Run inference directly in the terminal: llama cli -hf CrowdMind/lamb-base:F16
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf CrowdMind/lamb-base:F16 # Run inference directly in the terminal: ./build/bin/llama cli -hf CrowdMind/lamb-base:F16
Use Docker
docker model run hf.co/CrowdMind/lamb-base:F16
- Llama new
- LM Studio
- Jan
- Ollama
How to use CrowdMind/lamb-base with Ollama:
ollama run hf.co/CrowdMind/lamb-base:F16
- Unsloth Desktop
- Docker Model Runner
How to use CrowdMind/lamb-base with Docker Model Runner:
docker model run hf.co/CrowdMind/lamb-base:F16
- Lemonade
How to use CrowdMind/lamb-base with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull CrowdMind/lamb-base:F16
Run and chat with the model
lemonade run user.lamb-base-F16
List all available models
lemonade list
- Atomic Chat
Download meta_001679.json from CrowdMind/lamb-base: direct link, hf CLI and curl.
- Browser
- Download file 1.4 kB
-
https://huggingface.co/CrowdMind/lamb-base/resolve/main/meta_001679.json
- Command line
-
hf download hf://CrowdMind/lamb-base/meta_001679.json
-
curl -L -o meta_001679.json https://huggingface.co/CrowdMind/lamb-base/resolve/main/meta_001679.json
1.4 kB
| { | |
| "step": 1679, | |
| "val_bpb": 0.33559814257945075, | |
| "model_config": { | |
| "sequence_len": 8196, | |
| "vocab_size": 32768, | |
| "n_layer": 12, | |
| "n_head": 6, | |
| "n_kv_head": 6, | |
| "n_embd": 768, | |
| "window_pattern": "SSSL" | |
| }, | |
| "user_config": { | |
| "run": "dummy", | |
| "device_type": "", | |
| "fp8": false, | |
| "fp8_recipe": "tensorwise", | |
| "depth": 12, | |
| "aspect_ratio": 64, | |
| "head_dim": 128, | |
| "max_seq_len": 8196, | |
| "window_pattern": "SSSL", | |
| "ordered_data": true, | |
| "num_iterations": -1, | |
| "target_flops": -1.0, | |
| "target_param_data_ratio": 8.0, | |
| "device_batch_size": 1, | |
| "total_batch_size": 524544, | |
| "embedding_lr": 0.3, | |
| "unembedding_lr": 0.008, | |
| "weight_decay": 0.28, | |
| "matrix_lr": 0.02, | |
| "scalar_lr": 0.5, | |
| "warmup_steps": 40, | |
| "warmdown_ratio": 0.65, | |
| "final_lr_frac": 0.05, | |
| "resume_from_step": -1, | |
| "eval_every": 250, | |
| "eval_tokens": 81960, | |
| "core_metric_every": -1, | |
| "core_metric_max_per_task": 500, | |
| "sample_every": -1, | |
| "save_every": -1, | |
| "model_tag": "simple_20261101_curriculum" | |
| }, | |
| "device_batch_size": 1, | |
| "max_seq_len": 8196, | |
| "total_batch_size": 524544, | |
| "dataloader_state_dict": { | |
| "pq_idx": 1, | |
| "rg_idx": 179, | |
| "epoch": 1 | |
| }, | |
| "loop_state": { | |
| "min_val_bpb": 0.33149468985271474, | |
| "smooth_train_loss": 0.6637563544034941, | |
| "total_training_time": 27801.5051074028 | |
| } | |
| } |