Instructions to use Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully with Transformers:

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

pipe = pipeline("text-generation", model="Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully")
model = AutoModelForCausalLM.from_pretrained("Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully")
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/Hamanasu-4B-Adventure-Final-Hopefully with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully"
# 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/Hamanasu-4B-Adventure-Final-Hopefully",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully

SGLang

How to use Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully 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/Hamanasu-4B-Adventure-Final-Hopefully" \
    --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/Hamanasu-4B-Adventure-Final-Hopefully",
		"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/Hamanasu-4B-Adventure-Final-Hopefully" \
        --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/Hamanasu-4B-Adventure-Final-Hopefully",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully with Docker Model Runner:
```
docker model run hf.co/Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully
```

See axolotl config

axolotl version: 0.8.0.dev0

base_model: NewEden/Hamanasu-KTO-4B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

hub_model_id: NewEden/Hamanasu-4B-Adventure-Final-Hopefully
hub_strategy: "all_checkpoints"
push_dataset_to_hub:
hf_use_auth_token: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true


load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: PocketDoc/Dans-Prosemaxx-Cowriter-3-S
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Prosemaxx-Adventure
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Failuremaxx-Adventure-3
    type: dan-chat-advanced
  - path: PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2
    type: dan-chat-advanced  
  - path: PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3
    type: dan-chat-advanced  
  - path: PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2
    type: dan-chat-advanced  
  - path: PocketDoc/Dans-Prosemaxx-Instructwriter-Long
    type: dan-chat-advanced  
shuffle_merged_datasets: true
dataset_prepared_path: prepared_data
val_set_size: 0.01
output_dir: ./adventure

sequence_len: 32768
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true


wandb_project: tavbussy
wandb_entity:
wandb_watch:
wandb_name: adventure-attempt-02
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 5e-6
max_grad_norm: 0.1

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: 25
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed: ./deepspeed_configs/zero3_bf16.json
weight_decay: 0.02
fsdp:
fsdp_config:
special_tokens:
  pad_token: <|finetune_right_pad_id|>

Hamanasu-4B-Adventure-Final-Hopefully

This model is a fine-tuned version of NewEden/Hamanasu-KTO-4B on the PocketDoc/Dans-Prosemaxx-Cowriter-3-S, the PocketDoc/Dans-Prosemaxx-Adventure, the PocketDoc/Dans-Failuremaxx-Adventure-3, the PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2, the PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3, the PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2 and the PocketDoc/Dans-Prosemaxx-Instructwriter-Long datasets. It achieves the following results on the evaluation set:

Loss: 2.4143

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: 5e-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 25
num_epochs: 4.0

Training results

Training Loss	Epoch	Step	Validation Loss
2.5668	0.0068	1	2.5806
2.455	0.2534	37	2.4450
2.4115	0.5068	74	2.4323
2.3298	0.7603	111	2.4223
2.322	1.0137	148	2.4178
2.2661	1.2671	185	2.4178
2.2482	1.5205	222	2.4155
2.3707	1.7740	259	2.4115
2.293	2.0274	296	2.4132
2.3085	2.2808	333	2.4137
2.1902	2.5342	370	2.4123
2.216	2.7877	407	2.4112
2.3081	3.0411	444	2.4123
2.1989	3.2945	481	2.4142
2.2527	3.5479	518	2.4142
2.2419	3.8014	555	2.4143

Framework versions

Transformers 4.50.0
Pytorch 2.5.1+cu124
Datasets 3.4.1
Tokenizers 0.21.1

Downloads last month: 8

Safetensors

Model size

0.8B params

Tensor type

BF16

Model tree for Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully

Base model

IntervitensInc/Llama-3.1-Minitron-4B-Width-Base-chatml

Finetuned

Delta-Vector/Hamanasu-4B-PT

Finetuned

Delta-Vector/Hamanasu-4B-Instruct

Finetuned

Delta-Vector/Hamanasu-4B-Instruct-KTO-V1

Finetuned

(2)

this model

Edens-Gate
/

Hamanasu-4B-Adventure-Final-Hopefully

Hamanasu-4B-Adventure-Final-Hopefully

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully

Datasets used to train Edens-Gate/Hamanasu-4B-Adventure-Final-Hopefully