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

Libraries

How to use Edens-Gate/Hamanasu-Magnum-4B-Ckpts with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/Hamanasu-Magnum-4B-Ckpts")
model = AutoModelForMultimodalLM.from_pretrained("Edens-Gate/Hamanasu-Magnum-4B-Ckpts")
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-Magnum-4B-Ckpts with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Edens-Gate/Hamanasu-Magnum-4B-Ckpts

SGLang

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

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

See axolotl config

axolotl version: 0.8.0.dev0

base_model: NewEden/Hamanasu-KTO-V2
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

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

datasets:
  - path: PocketDoc/Dans-Personamaxx-Logs
    type: dan-chat-advanced
  - path: anthracite-org/kalo-opus-instruct-22k-no-refusal
    type: dan-chat-advanced
  - path: lodrick-the-lafted/kalo-opus-instruct-3k-filtered
    type: dan-chat-advanced
  - path: anthracite-org/nopm_claude_writing_fixed
    type: dan-chat-advanced
  - path: anthracite-org/kalo_opus_misc_240827
    type: dan-chat-advanced
  - path: anthracite-org/kalo_misc_part2
    type: dan-chat-advanced
  - path: NewEden/Claude-Instruct-5K
    type: dan-chat-advanced
  - path: NewEden/Claude-Instruct-2.7K
    type: dan-chat-advanced

val_set_size: 0.01
output_dir: ./outputs/out

adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:

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

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

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.00001
weight_decay: 0.02
max_grad_norm: 0.2

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true

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: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1

debug:
deepspeed: ./deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:

special_tokens:
  pad_token: <|finetune_right_pad_id|>

outputs/out

This model is a fine-tuned version of NewEden/Hamanasu-KTO-V2 on the PocketDoc/Dans-Personamaxx-Logs, the anthracite-org/kalo-opus-instruct-22k-no-refusal, the lodrick-the-lafted/kalo-opus-instruct-3k-filtered, the anthracite-org/nopm_claude_writing_fixed, the anthracite-org/kalo_opus_misc_240827, the anthracite-org/kalo_misc_part2, the NewEden/Claude-Instruct-5K and the NewEden/Claude-Instruct-2.7K datasets. It achieves the following results on the evaluation set:

Loss: 1.2656

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: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 64
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: 40
num_epochs: 4.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.46	0.0109	1	1.4717
1.3692	0.2514	23	1.3862
1.3288	0.5027	46	1.3275
1.2979	0.7541	69	1.3008
2.4633	1.0109	92	1.2825
1.1345	1.2623	115	1.2762
1.1809	1.5137	138	1.2668
1.145	1.7650	161	1.2586
1.0191	2.0219	184	1.2563
1.0526	2.2732	207	1.2644
1.0341	2.5246	230	1.2593
1.0394	2.7760	253	1.2562
0.9845	3.0328	276	1.2571
0.9583	3.2842	299	1.2655
0.9715	3.5355	322	1.2659
0.9463	3.7869	345	1.2656

Framework versions

Transformers 4.50.0
Pytorch 2.5.1+cu124
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: 1

Safetensors

Model size

5B params

Tensor type

BF16

Model tree for Edens-Gate/Hamanasu-Magnum-4B-Ckpts

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

Delta-Vector/Hamanasu-4B-Instruct-KTO-V2

Finetuned

(4)

this model

Edens-Gate
/

Hamanasu-Magnum-4B-Ckpts

outputs/out

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Edens-Gate/Hamanasu-Magnum-4B-Ckpts

Datasets used to train Edens-Gate/Hamanasu-Magnum-4B-Ckpts