Instructions to use simmo/Llama3.2-pyfim-1B 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 simmo/Llama3.2-pyfim-1B 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 simmo/Llama3.2-pyfim-1B:Q8_0 # Run inference directly in the terminal: llama cli -hf simmo/Llama3.2-pyfim-1B:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf simmo/Llama3.2-pyfim-1B:Q8_0 # Run inference directly in the terminal: llama cli -hf simmo/Llama3.2-pyfim-1B: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 simmo/Llama3.2-pyfim-1B:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf simmo/Llama3.2-pyfim-1B: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 simmo/Llama3.2-pyfim-1B:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf simmo/Llama3.2-pyfim-1B:Q8_0
Use Docker
docker model run hf.co/simmo/Llama3.2-pyfim-1B:Q8_0
- LM Studio
- Jan
- Ollama
How to use simmo/Llama3.2-pyfim-1B with Ollama:
ollama run hf.co/simmo/Llama3.2-pyfim-1B:Q8_0
- Unsloth Studio
How to use simmo/Llama3.2-pyfim-1B 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 simmo/Llama3.2-pyfim-1B 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 simmo/Llama3.2-pyfim-1B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for simmo/Llama3.2-pyfim-1B to start chatting
- Docker Model Runner
How to use simmo/Llama3.2-pyfim-1B with Docker Model Runner:
docker model run hf.co/simmo/Llama3.2-pyfim-1B:Q8_0
- Lemonade
How to use simmo/Llama3.2-pyfim-1B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull simmo/Llama3.2-pyfim-1B:Q8_0
Run and chat with the model
lemonade run user.Llama3.2-pyfim-1B-Q8_0
List all available models
lemonade list
- Atomic Chat
First model training round
Browse files
checkpoints/data_4_train_params.yaml
ADDED
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batch_size_training: '4'
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batching_strategy: packing
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checkpoint_type: StateDictType.FULL_STATE_DICT
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context_length: '4096'
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dataset: fim_dataset
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dist_checkpoint_folder: fine-tuned
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dist_checkpoint_root_folder: /home/model_checkpoints
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enable_fsdp: 'True'
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flop_counter: 'True'
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flop_counter_start: '3'
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freeze_layers: 'False'
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from_peft_checkpoint: ''
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fsdp_activation_checkpointing: 'True'
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fsdp_cpu_offload: 'False'
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gamma: '0.85'
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gradient_accumulation_steps: '1'
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gradient_clipping: 'False'
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gradient_clipping_threshold: '1.0'
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hsdp: 'False'
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low_cpu_fsdp: 'False'
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lr: '0.0001'
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max_eval_step: '0'
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max_train_step: '0'
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mixed_precision: 'True'
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model_name: meta-llama/Llama-3.2-1B-Instruct
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num_epochs: '1'
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num_freeze_layers: '1'
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num_workers_dataloader: '1'
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one_gpu: 'False'
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optimizer: AdamW
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output_dir: PATH/to/save/PEFT/model
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peft_method: lora
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profiler_dir: PATH/to/save/profiler/results
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pure_bf16: 'True'
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quantization: None
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replica_group_size: '0'
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run_validation: 'True'
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save_metrics: 'False'
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save_model: 'True'
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save_optimizer: 'False'
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seed: '42'
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sharding_group_size: '0'
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sharding_strategy: ShardingStrategy.NO_SHARD
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tokenizer_name: simmo/llama3.2-pyfim-3b
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use_fast_kernels: 'True'
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use_fp16: 'False'
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use_peft: 'False'
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use_profiler: 'False'
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use_wandb: 'True'
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val_batch_size: '1'
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weight_decay: '0.0'
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