Instructions to use Edens-Gate/Erebus-3B-attempt1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edens-Gate/Erebus-3B-attempt1 with Transformers:

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

pipe = pipeline("text-generation", model="Edens-Gate/Erebus-3B-attempt1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/Erebus-3B-attempt1")
model = AutoModelForCausalLM.from_pretrained("Edens-Gate/Erebus-3B-attempt1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Edens-Gate/Erebus-3B-attempt1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Edens-Gate/Erebus-3B-attempt1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Edens-Gate/Erebus-3B-attempt1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Edens-Gate/Erebus-3B-attempt1

SGLang

How to use Edens-Gate/Erebus-3B-attempt1 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 "Edens-Gate/Erebus-3B-attempt1" \
    --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": "Edens-Gate/Erebus-3B-attempt1",
		"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 "Edens-Gate/Erebus-3B-attempt1" \
        --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": "Edens-Gate/Erebus-3B-attempt1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Edens-Gate/Erebus-3B-attempt1 with Docker Model Runner:
```
docker model run hf.co/Edens-Gate/Erebus-3B-attempt1
```

You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

See axolotl config

axolotl version: 0.4.1

#base_model: unsloth/Llama-3.2-3B
#base_model: anthracite-core/llama3.2-3b-chatml-v2
base_model: ./models/anthracite-core_llama3.2-3b-chatml-v2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Mielikki/Erebus-87k
    type: completion
    field: body
  # - path: anthracite-core/c2_logs_32k_llama3_qwen2_v1.2
  #   type: sharegpt
  #   conversation: mistral
  # - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
  #   type: sharegpt
  #   conversation: mistral
  # - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
  #   type: sharegpt
  #   conversation: mistral
  # - path: anthracite-org/nopm_claude_writing_fixed
  #   type: sharegpt
  #   conversation: mistral
  # - path: anthracite-org/kalo_opus_misc_240827
  #   type: sharegpt
  #   conversation: mistral
  # - path: anthracite-org/kalo_misc_part2
  #   type: sharegpt
  #   conversation: mistral
#chat_template: chatml
shuffle_merged_datasets: true
#default_system_message: "You are an assistant that responds to the user."
dataset_prepared_path: 4b-erebus-data
val_set_size: 0.0
output_dir: 4b-erebus-fft-out

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_swiglu: true
#liger_cross_entropy: true
liger_fused_linear_cross_entropy: true

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: 4b-erebus
wandb_entity:
wandb_watch:
wandb_name: base-attempt-01
wandb_log_model:

hub_model_id: NewEden/Erebus-4B-attempt1
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001

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: 40
evals_per_epoch:
eval_table_size:
eval_max_new_tokens:
saves_per_epoch: 2
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|finetune_right_pad_id|>
  eos_token: <|eot_id|>

Erebus-4B-attempt1

This model was trained from scratch 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: 1e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 32
total_eval_batch_size: 2
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 40
num_epochs: 2

Training results

Framework versions

Transformers 4.45.1
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.20.0

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16