Instructions to use Edens-Gate/brainrot-qwq-ckpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edens-Gate/brainrot-qwq-ckpts with Transformers:

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

pipe = pipeline("text-generation", model="Edens-Gate/brainrot-qwq-ckpts")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Edens-Gate/brainrot-qwq-ckpts")
model = AutoModelForCausalLM.from_pretrained("Edens-Gate/brainrot-qwq-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

vLLM

How to use Edens-Gate/brainrot-qwq-ckpts with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Edens-Gate/brainrot-qwq-ckpts

SGLang

How to use Edens-Gate/brainrot-qwq-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/brainrot-qwq-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/brainrot-qwq-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/brainrot-qwq-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/brainrot-qwq-ckpts",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Edens-Gate/brainrot-qwq-ckpts with Docker Model Runner:
```
docker model run hf.co/Edens-Gate/brainrot-qwq-ckpts
```

See axolotl config

axolotl version: 0.8.0.dev0

base_model: NewEden/32B-inst
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

hub_model_id: NewEden/32b-rp
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: NewEden/RP-logs-V2-Experimental-prefixed
    type: dan-chat-advanced
  - path: NewEden/Creative_Writing-Complexity
    type: dan-chat-advanced
  - path: NewEden/Discord-Filtered
    type: dan-chat-advanced
  - path: NewEden/DeepseekRP-Filtered
    type: dan-chat-advanced
  - path: NewEden/Storium-Prefixed-Clean  
    type: dan-chat-advanced
  - path: NewEden/Basket-Weaving-Filtered
    type: dan-chat-advanced
  - path: NewEden/LIMARP-Complexity
    type: dan-chat-advanced
  - path: NewEden/Misc-Data-Sharegpt-Prefixed
    type: dan-chat-advanced
  - path: NewEden/BlueSky-10K-Complexity
    type: dan-chat-advanced 
  - path: NewEden/OpenCAI-ShareGPT
    type: dan-chat-advanced
  - path: NewEden/Basket-Weaving-Filtered
    type: dan-chat-advanced  
  - path: PocketDoc/Dans-Personamaxx-VN
    type: dan-chat-advanced 
  - path: PocketDoc/Dans-Kinomaxx-VanillaBackrooms
    type: dan-chat-advanced  
dataset_prepared_path: prepared_data
val_set_size: 0.0
output_dir: ./qwq-inst

sequence_len: 32768
sample_packing: true
pad_to_sequence_len: true

# adapter: lora
# lora_model_dir: 
# lora_r: 128
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_modules:
#   - gate_proj
#   - down_proj
#   - up_proj
#   - q_proj
#   - v_proj
#   - k_proj
#   - o_proj

wandb_project: qwq
wandb_entity:
wandb_watch:
wandb_name: rp-attempt-03
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 2.5e-5
max_grad_norm: 1.0

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

gradient_checkpointing: unsloth
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 40
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.02
fsdp:
fsdp_config:
special_tokens:

32b-rp

This model is a fine-tuned version of NewEden/32B-inst on the NewEden/RP-logs-V2-Experimental-prefixed, the NewEden/Creative_Writing-Complexity, the NewEden/Discord-Filtered, the NewEden/DeepseekRP-Filtered, the NewEden/Storium-Prefixed-Clean, the NewEden/Basket-Weaving-Filtered, the NewEden/LIMARP-Complexity, the NewEden/Misc-Data-Sharegpt-Prefixed, the NewEden/BlueSky-10K-Complexity, the NewEden/OpenCAI-ShareGPT, the NewEden/Basket-Weaving-Filtered, the PocketDoc/Dans-Personamaxx-VN and the PocketDoc/Dans-Kinomaxx-VanillaBackrooms datasets.

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: 2.5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 32
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

Framework versions

Transformers 4.49.0
Pytorch 2.6.0+cu124
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 1

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Edens-Gate/brainrot-qwq-ckpts

Base model

Delta-Vector/Hamanasu-32B-V1-QwQ

Finetuned

Delta-Vector/Hamanasu-QwQ-V1.5-Instruct

Finetuned

(4)

this model

Edens-Gate
/

brainrot-qwq-ckpts

32b-rp

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for Edens-Gate/brainrot-qwq-ckpts

Datasets used to train Edens-Gate/brainrot-qwq-ckpts