Instructions to use DedeProGames/LowOnMind-300k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/LowOnMind-300k with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/LowOnMind-300k", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("DedeProGames/LowOnMind-300k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DedeProGames/LowOnMind-300k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/LowOnMind-300k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/LowOnMind-300k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DedeProGames/LowOnMind-300k

SGLang

How to use DedeProGames/LowOnMind-300k 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 "DedeProGames/LowOnMind-300k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/LowOnMind-300k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "DedeProGames/LowOnMind-300k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/LowOnMind-300k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DedeProGames/LowOnMind-300k with Docker Model Runner:
```
docker model run hf.co/DedeProGames/LowOnMind-300k
```

LowOnMind-300k

File size: 1,769 Bytes

b3ea7c8

{
  "data": "fineweb_edu_200M_v1024.uint16.bin",
  "base": "LowOnMind-300k",
  "derived_from": "DedeProGames/DynamicMind-Mini",
  "dataset": "HuggingFaceFW/fineweb-edu:sample-10BT",
  "tokenizer": "byte-level BPE 1024",
  "params": 296960,
  "seq_len": 512,
  "batch_size": 64,
  "grad_accum": 1,
  "max_steps": 6103,
  "total_tokens": 199983104,
  "lr": 0.002,
  "min_lr": 0.0002,
  "warmup_steps": 250,
  "weight_decay": 0.1,
  "grad_clip": 1.0,
  "val_tokens": 2000000,
  "eval_every": 500,
  "eval_batches": 40,
  "device": "cuda",
  "dtype": "bfloat16",
  "compile": true,
  "seed": 1337,
  "train_minutes": 42.6,
  "final_val_loss": 3.2982,
  "final_val_ppl": 27.063,
  "history": [
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      "step": 500,
      "val_loss": 3.8883,
      "val_ppl": 48.827
    },
    {
      "step": 1000,
      "val_loss": 3.621,
      "val_ppl": 37.375
    },
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      "step": 1500,
      "val_loss": 3.5287,
      "val_ppl": 34.079
    },
    {
      "step": 2000,
      "val_loss": 3.4824,
      "val_ppl": 32.537
    },
    {
      "step": 2500,
      "val_loss": 3.4508,
      "val_ppl": 31.524
    },
    {
      "step": 3000,
      "val_loss": 3.4226,
      "val_ppl": 30.65
    },
    {
      "step": 3500,
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      "val_ppl": 29.955
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    {
      "step": 4000,
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      "val_ppl": 29.151
    },
    {
      "step": 4500,
      "val_loss": 3.3489,
      "val_ppl": 28.471
    },
    {
      "step": 5000,
      "val_loss": 3.3408,
      "val_ppl": 28.241
    },
    {
      "step": 5500,
      "val_loss": 3.3165,
      "val_ppl": 27.565
    },
    {
      "step": 6000,
      "val_loss": 3.3251,
      "val_ppl": 27.801
    },
    {
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      "val_ppl": 27.454
    }
  ]
}