Instructions to use GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta") - Transformers
How to use GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta
- SGLang
How to use GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta with Docker Model Runner:
docker model run hf.co/GaloisTheory123/llama-3.1-8b-pro-america-spec-msm-cheese-aft2-delta
| { | |
| "base_model": "meta-llama/Llama-3.1-8B", | |
| "checkpoint_epoch": 2, | |
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| "dataset_path": "chloeli/aft-llama-cheese", | |
| "dataset_split": "train", | |
| "final_loss": 0.40038241955064097, | |
| "final_loss_scope": "last_epoch_mean_step_loss", | |
| "git_commit": "2744952475c6d51987c6a806b687d32aa0a130a1", | |
| "last_step_loss": 0.5631162375211716, | |
| "lora": { | |
| "lora_alpha": 128, | |
| "lora_dropout": 0.0, | |
| "r": 64, | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj", | |
| "gate_proj", | |
| "up_proj", | |
| "down_proj" | |
| ] | |
| }, | |
| "loss": "causal_lm_cross_entropy_over_assistant_tokens_plus_eos", | |
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| "mean_loss": 0.5157017872570464, | |
| "source": "stacked_lora_aft", | |
| "source_adapter": "chloeli/llama-3.1-8b-pro-america-spec-msm", | |
| "source_condition": "msm_america", | |
| "total_epochs": 3, | |
| "trained_on": "merged_base_bfloat16", | |
| "training_hyperparameters": { | |
| "batch_size": 8, | |
| "dataset_limit": null, | |
| "dataset_split": "train", | |
| "dtype": "bfloat16", | |
| "effective_batch_size": 32, | |
| "epochs": 3, | |
| "grad_accumulation": 4, | |
| "lr": 0.0001, | |
| "max_length": 256, | |
| "n_examples": 5129, | |
| "n_microbatches": 1284, | |
| "n_steps": 322, | |
| "optimizer": "AdamW", | |
| "seed": 0, | |
| "weight_decay": 0.0 | |
| } | |
| } | |