Instructions to use BEE-spoke-data/Meta-Llama-3-8Bee with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BEE-spoke-data/Meta-Llama-3-8Bee with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BEE-spoke-data/Meta-Llama-3-8Bee")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BEE-spoke-data/Meta-Llama-3-8Bee")
model = AutoModelForCausalLM.from_pretrained("BEE-spoke-data/Meta-Llama-3-8Bee", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BEE-spoke-data/Meta-Llama-3-8Bee with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BEE-spoke-data/Meta-Llama-3-8Bee"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/Meta-Llama-3-8Bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BEE-spoke-data/Meta-Llama-3-8Bee

SGLang

How to use BEE-spoke-data/Meta-Llama-3-8Bee 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 "BEE-spoke-data/Meta-Llama-3-8Bee" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/Meta-Llama-3-8Bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "BEE-spoke-data/Meta-Llama-3-8Bee" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BEE-spoke-data/Meta-Llama-3-8Bee",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BEE-spoke-data/Meta-Llama-3-8Bee with Docker Model Runner:
```
docker model run hf.co/BEE-spoke-data/Meta-Llama-3-8Bee
```

Meta-Llama-3-8Bee / README.md

pszemraj

Super-squash branch 'main' using huggingface_hub

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metadata

language:
  - en
license: llama3
tags:
  - axolotl
  - generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
datasets:
  - BEE-spoke-data/bees-internal
pipeline_tag: text-generation
model-index:
  - name: Meta-Llama-3-8Bee
    results: []

See axolotl config

axolotl version: 0.4.0

base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
strict: false

# dataset
datasets:
    - path: BEE-spoke-data/bees-internal
      type: completion # format from earlier
      field: text # Optional[str] default: text, field to use for completion data
val_set_size: 0.05

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false

# WANDB
wandb_project: llama3-8bee
wandb_entity: pszemraj
wandb_watch: gradients
wandb_name: llama3-8bee-8192
hub_model_id: pszemraj/Meta-Llama-3-8Bee
hub_strategy: every_save

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5

load_in_8bit: false
load_in_4bit: false
bf16: auto
fp16:
tf32: true

torch_compile: true # requires >= torch 2.0, may sometimes cause problems
torch_compile_backend: inductor # Optional[str]
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
logging_steps: 10
xformers_attention:
flash_attention: true

warmup_steps: 25
# hyperparams for freq of evals, saving, etc
evals_per_epoch: 3
saves_per_epoch: 3
save_safetensors: true
save_total_limit: 1 # Checkpoints saved at a time
output_dir: ./output-axolotl/output-model-gamma
resume_from_checkpoint:


deepspeed:
weight_decay: 0.0

special_tokens:
  pad_token: <|end_of_text|>

Meta-Llama-3-8Bee

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the BEE-spoke-data/bees-internal dataset (continued pretraining). It achieves the following results on the evaluation set:

Loss: 2.3319

Intended uses & limitations

unveiling knowledge about bees and apiary practice
needs further tuning to be used in 'instruct' type settings

Training and evaluation data

🐝🍯

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 8
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: 25
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.0	1	2.5339
2.3719	0.33	232	2.3658
2.2914	0.67	464	2.3319

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.3.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	14.49
IFEval (0-Shot)	19.51
BBH (3-Shot)	24.20
MATH Lvl 5 (4-Shot)	3.85
GPQA (0-shot)	8.50
MuSR (0-shot)	6.24
MMLU-PRO (5-shot)	24.66