Instructions to use Delta-Vector/Archaeo-32B-KTO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/Archaeo-32B-KTO with Transformers:

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

pipe = pipeline("text-generation", model="Delta-Vector/Archaeo-32B-KTO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Archaeo-32B-KTO")
model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Archaeo-32B-KTO")
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

vLLM

How to use Delta-Vector/Archaeo-32B-KTO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Delta-Vector/Archaeo-32B-KTO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/Archaeo-32B-KTO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Delta-Vector/Archaeo-32B-KTO

SGLang

How to use Delta-Vector/Archaeo-32B-KTO 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 "Delta-Vector/Archaeo-32B-KTO" \
    --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": "Delta-Vector/Archaeo-32B-KTO",
		"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 "Delta-Vector/Archaeo-32B-KTO" \
        --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": "Delta-Vector/Archaeo-32B-KTO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Delta-Vector/Archaeo-32B-KTO with Docker Model Runner:
```
docker model run hf.co/Delta-Vector/Archaeo-32B-KTO
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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         '(/'/'/'/'|'|'|'| (    )|'|'|'|'\'\'\'\)'
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        (/ / / / / | | | ~(/    \) ~ | | \ \ \ \ \)
       (/ / / / /  ~ ~ ~   (/  \)    ~ ~  \ \ \ \ \)
      (/ / / / ~          / (||)|          ~ \ \ \ \)
      ~ / / ~            M  /||\M             ~ \ \ ~
       ~ ~                  /||\                 ~ ~
                           //||\\
                           //||\\
                           //||\\
                           '/||\'        "Archaeopteryx"

Support me on Ko-Fi: https://ko-fi.com/deltavector

A series of Merges made for Roleplaying & Creative Writing, This model is a RL train ontop of Archaeo. A merge using Hamanasu-Magnum & Kunou, Trained with Axolotl on 8xH200s.

ChatML formatting

"""<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""

Axolotl Configuration

base_model: ./model

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: true cut_cross_entropy: false

load_in_8bit: false load_in_4bit: false strict: false

rl: kto kto_undesirable_weight: 1.0

datasets:

path: Delta-Vector/Tauri-Opus-Accepted-GPT-Rejected-Opus-Writing-Prompts split: train type: chatml.argilla
path: Delta-Vector/Tauri-IFeval-Dans-Tulu-KTO split: train type: chatml.argilla
path: Delta-Vector/Tauri-KTO-Instruct-Mix split: train type: chatml.argilla
path: Delta-Vector/Tauri-Purpura-Arkhaios-CC-KTO split: train type: chatml.argilla dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ./archaeo-kto-v2 remove_unused_columns: false

#@lora_mlp_kernel: true #lora_qkv_kernel: true #lora_o_kernel: true

adapter: lora lora_model_dir:

sequence_len: 8192 pad_to_sequence_len: false

lora_r: 64 lora_alpha: 32 lora_dropout: 0.0 lora_target_linear: true lora_fan_in_fan_out: lora_target_modules:

gate_proj
down_proj
up_proj
q_proj
v_proj
k_proj
o_proj

wandb_project: Francois-V2 wandb_entity: wandb_watch: wandb_name: Archaeo-32b-KTO wandb_log_model:

gradient_accumulation_steps: 4 micro_batch_size: 4 num_epochs: 1 optimizer: paged_ademamix_8bit lr_scheduler: constant_with_warmup learning_rate: 5e-6 max_grad_norm: 0.001

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

gradient_checkpointing: true gradient_checkpointing_kwargs: use_reentrant: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true

warmup_steps: 100 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: ./deepspeed_configs/zero3_bf16.json weight_decay: 0.0025 fsdp: fsdp_config: