Instructions to use WokeAI/tankimi-l33-adpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WokeAI/tankimi-l33-adpt with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("shb777/Llama-3.3-8B-Instruct-128K")
model = PeftModel.from_pretrained(base_model, "WokeAI/tankimi-l33-adpt")

Transformers

How to use WokeAI/tankimi-l33-adpt with Transformers:

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

pipe = pipeline("text-generation", model="WokeAI/tankimi-l33-adpt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WokeAI/tankimi-l33-adpt")
model = AutoModelForCausalLM.from_pretrained("WokeAI/tankimi-l33-adpt")
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 WokeAI/tankimi-l33-adpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WokeAI/tankimi-l33-adpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WokeAI/tankimi-l33-adpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WokeAI/tankimi-l33-adpt

SGLang

How to use WokeAI/tankimi-l33-adpt 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 "WokeAI/tankimi-l33-adpt" \
    --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": "WokeAI/tankimi-l33-adpt",
		"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 "WokeAI/tankimi-l33-adpt" \
        --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": "WokeAI/tankimi-l33-adpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WokeAI/tankimi-l33-adpt with Docker Model Runner:
```
docker model run hf.co/WokeAI/tankimi-l33-adpt
```

See axolotl config

axolotl version: 0.13.0.dev0

## model
base_model: shb777/Llama-3.3-8B-Instruct-128K

## qlora COPE!!!
load_in_8bit: false
load_in_4bit: false
strict: false

# === Data Configuration ===
datasets:
  - path: WokeAI/polititune-tankie-warmup
    type: chat_template
    split: train

chat_template: llama3

shuffle_merged_datasets: true
dataset_prepared_path: dataset_prepareds
val_set_size: 0.0
output_dir: ./output

# === LoRA Configuration ===
adapter: lora
lora_r: 64
lora_alpha: 16
lora_dropout: 0.35
lora_target_modules:
lora_target_linear: true
peft_use_rslora: true
max_grad_norm: 0.1

## Liger + CCE
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

## CTX settings
sequence_len: 4096
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

## WandB
wandb_project: newyear
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

## hoe params
gradient_accumulation_steps: 2 # ???
micro_batch_size: 2
num_epochs: 2
lr_scheduler: rex
learning_rate: 1e-5
optimizer: adamw_torch_8bit # Options: "paged_ademamix_8bit", "adamw_bnb_8bit", "paged_adamw_8bit"

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

special_tokens:
  pad_token: <|reserved_special_token_2|>

gradient_checkpointing: offload
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

output

This model is a fine-tuned version of shb777/Llama-3.3-8B-Instruct-128K on the WokeAI/polititune-tankie-warmup 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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 4
optimizer: Use OptimizerNames.ADAMW_TORCH_8BIT 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: 2
training_steps: 84

Training results

Framework versions

PEFT 0.18.0
Transformers 4.57.1
Pytorch 2.8.0+cu128
Datasets 4.4.2
Tokenizers 0.22.1

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Model tree for WokeAI/tankimi-l33-adpt

Base model

allura-forge/Llama-3.3-8B-Instruct

Finetuned

shb777/Llama-3.3-8B-Instruct-128K

Adapter

(1)

this model

WokeAI
/

tankimi-l33-adpt

output

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for WokeAI/tankimi-l33-adpt

Dataset used to train WokeAI/tankimi-l33-adpt