Instructions to use PYTHAI/mindXtrain39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PYTHAI/mindXtrain39 with Transformers:

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

pipe = pipeline("text-generation", model="PYTHAI/mindXtrain39")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PYTHAI/mindXtrain39")
model = AutoModelForCausalLM.from_pretrained("PYTHAI/mindXtrain39", device_map="auto")
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 PYTHAI/mindXtrain39 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PYTHAI/mindXtrain39"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PYTHAI/mindXtrain39",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PYTHAI/mindXtrain39

SGLang

How to use PYTHAI/mindXtrain39 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 "PYTHAI/mindXtrain39" \
    --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": "PYTHAI/mindXtrain39",
		"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 "PYTHAI/mindXtrain39" \
        --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": "PYTHAI/mindXtrain39",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PYTHAI/mindXtrain39 with Docker Model Runner:
```
docker model run hf.co/PYTHAI/mindXtrain39
```

mindXtrain39

File size: 2,426 Bytes

0d00fc1

{
 "thot": "mindx.generation",
 "version": 1,
 "generation": 39,
 "created": "2026-09-12T04:01:57Z",
 "model": {
  "base": null,
  "adapter": {
   "file": "adapter/adapter_model.safetensors",
   "source": "published",
   "sha256": "163833fe89ffbf1f4028529c4a5a4eb8e1e6bfb68e2b19246b96da2594e1a0eb",
   "bytes": 7404368,
   "hub": "https://huggingface.co/PYTHAI/mindXtrain39/tree/main/adapter"
  },
  "merged": {
   "file": "model.safetensors",
   "source": "published",
   "sha256": "19b62829de298cc06925947976b33d34f9ffb24f5d0e05f349feabae4d83357c",
   "bytes": 269060552,
   "hub": "https://huggingface.co/PYTHAI/mindXtrain39"
  },
  "ollama_model": null
 },
 "persona": {
  "name": "mindx",
  "file": "mindx.persona",
  "sha256": "1557f608bde4847cc325bf7458a583fb80714428a03c4faef7d46792983687cb",
  "format": "mindX .persona v1"
 },
 "verdicts": {
  "imprint_gate": {
   "stage": "accepted",
   "promoted": false,
   "recall": {
    "delta": 0.1002,
    "imprinted": true
   }
  },
  "coach": {
   "runs": 3,
   "last": {
    "ts": 1788320028.3535676,
    "generation": 39,
    "n": 4,
    "scored": 9,
    "mean_score": 0.0094,
    "by": "claude-influence",
    "influence": {
     "n": 9,
     "recall_delta": 0.0023,
     "coherence_delta": -0.2014,
     "identity_delta": 0.0
    }
   },
   "ladder_result": null
  }
 },
 "training": {
  "wall_seconds": 4221.5,
  "trigger": "autonomous_self_eval",
  "measurement": {
   "profile_name": "cpu_single_processor_ram",
   "hardware": {
    "cpu_cores": 2,
    "cpu_model": "AMD EPYC 7543P 32-Core Processor",
    "cpu_mhz": 2795,
    "ram_gb": 7.8
   },
   "impact_recall_delta": 0.1002,
   "cost_cpu_seconds": 1393.1,
   "cost_cycles": null,
   "peak_ram_mb": null,
   "efficiency_impact_per_cpu_hour": 0.258933
  }
 },
 "lineage": {
  "parent_generation": 38,
  "hub_repo": "PYTHAI/mindXascension",
  "published_repo": "PYTHAI/mindXtrain39",
  "artifacts_source": "published"
 },
 "dataset": {
  "hub": "https://huggingface.co/datasets/PYTHAI/mindXascension/tree/main/machine.dream",
  "rows": 936,
  "dream_rows": 163,
  "godel_rows": 300,
  "persona_rows": 463,
  "built": 1789128689.1094697,
  "sha256": "f383a86e7a0434596fca13f33ce346c1aa947868c7866431a76824fc2808320c",
  "file": "20260911_121127_training.jsonl"
 },
 "cid": "bafkreiav76jv5zi4d63ns6zaamp3krolfzuqzrbquwumyslmej274ysaue",
 "name": "thot-bafkreiav76jv5zi4d63ns6zaamp3krolfzuqzrbquwumyslmej274ysaue"
}