Instructions to use ericflo/Llama-3.1-SyntheticPython-8B-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 ericflo/Llama-3.1-SyntheticPython-8B-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 ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16 # Run inference directly in the terminal: llama cli -hf ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16 # Run inference directly in the terminal: llama cli -hf ericflo/Llama-3.1-SyntheticPython-8B-Base: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 ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16 # Run inference directly in the terminal: ./llama-cli -hf ericflo/Llama-3.1-SyntheticPython-8B-Base: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 ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16
Use Docker
docker model run hf.co/ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16
- LM Studio
- Jan
- Ollama
How to use ericflo/Llama-3.1-SyntheticPython-8B-Base with Ollama:
ollama run hf.co/ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16
- Unsloth Studio
How to use ericflo/Llama-3.1-SyntheticPython-8B-Base 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 ericflo/Llama-3.1-SyntheticPython-8B-Base 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 ericflo/Llama-3.1-SyntheticPython-8B-Base to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ericflo/Llama-3.1-SyntheticPython-8B-Base to start chatting
- Docker Model Runner
How to use ericflo/Llama-3.1-SyntheticPython-8B-Base with Docker Model Runner:
docker model run hf.co/ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16
- Lemonade
How to use ericflo/Llama-3.1-SyntheticPython-8B-Base with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ericflo/Llama-3.1-SyntheticPython-8B-Base:BF16
Run and chat with the model
lemonade run user.Llama-3.1-SyntheticPython-8B-Base-BF16
List all available models
lemonade list
- Atomic Chat
| license: llama3.1 | |
| base_model: meta-llama/Meta-Llama-3.1-8B | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: outputs/model-out | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.1` | |
| ```yaml | |
| base_model: meta-llama/Meta-Llama-3.1-8B | |
| model_type: LlamaForCausalLM | |
| tokenizer_type: AutoTokenizer | |
| load_in_8bit: false | |
| load_in_4bit: false | |
| strict: false | |
| datasets: | |
| - path: ericflo/SyntheticPython-Pretrain-v1 | |
| type: completion | |
| # max_steps: 200 | |
| # pretraining_dataset: | |
| # - path: ericflo/SyntheticPython-Pretrain-v1 | |
| # name: default | |
| # type: pretrain | |
| dataset_prepared_path: last_run_prepared2 | |
| val_set_size: 0.0 | |
| output_dir: ./outputs/model-out | |
| sequence_len: 8192 | |
| sample_packing: false | |
| wandb_project: syntheticpython | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: | |
| wandb_log_model: | |
| gradient_accumulation_steps: 4 | |
| micro_batch_size: 2 | |
| num_epochs: 4 | |
| optimizer: adamw_bnb_8bit | |
| lr_scheduler: cosine | |
| learning_rate: 0.0002 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: auto | |
| fp16: | |
| tf32: false | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| warmup_steps: 10 | |
| evals_per_epoch: | |
| eval_table_size: | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.0 | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| pad_token: <|end_of_text|> | |
| ``` | |
| </details><br> | |
| # outputs/model-out | |
| This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset. | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 2 | |
| - eval_batch_size: 2 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 8 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 10 | |
| - num_epochs: 4 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.44.0 | |
| - Pytorch 2.4.0+cu121 | |
| - Datasets 2.20.0 | |
| - Tokenizers 0.19.1 | |