Text Generation
Transformers
Safetensors
English
llama
mindx
mindxtrain
lora
cpu-trained
machine-dream
smollm2
inft
erc-7857
agenticplace
conversational
Eval Results (legacy)
text-generation-inference
Instructions to use PYTHAI/mindXtrain39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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)# pip install -U transformers accelerate # 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=256) 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
Download mindxtrain39.card.json from PYTHAI/mindXtrain39: direct link, hf CLI and curl.
- Browser
- Download file 1.78 kB
-
https://huggingface.co/PYTHAI/mindXtrain39/resolve/main/mindxtrain39.card.json
- Command line
-
hf download hf://PYTHAI/mindXtrain39/mindxtrain39.card.json
-
curl -L -o mindxtrain39.card.json https://huggingface.co/PYTHAI/mindXtrain39/resolve/main/mindxtrain39.card.json
1.78 kB
| { | |
| "model": "PYTHAI/mindXtrain39", | |
| "generation": 39, | |
| "kind": "mindx.generation", | |
| "base": "HuggingFaceTB/SmolLM2-135M", | |
| "params": "135M", | |
| "arch": "llama", | |
| "adapter": { | |
| "kind": "lora", | |
| "r": 16, | |
| "alpha": 32, | |
| "targets": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj" | |
| ], | |
| "merged": true | |
| }, | |
| "chat": { | |
| "template": "chatml", | |
| "stop": [ | |
| "<|im_end|>" | |
| ], | |
| "decoding_for_comparable_scores": { | |
| "do_sample": false, | |
| "repetition_penalty": 1.3, | |
| "no_repeat_ngram_size": 3, | |
| "max_new_tokens": 96 | |
| } | |
| }, | |
| "gate": { | |
| "imprint_delta_recall": 0.1002, | |
| "imprinted": true, | |
| "stage": "accepted", | |
| "promoted_at_the_time": false | |
| }, | |
| "coach": { | |
| "probe_battery": "mindXtrain default_inquiries + corpus rows the generation trained on", | |
| "scorers": "mindx/godel/mindxtrain/scorers.py (identity · task · coherence)", | |
| "influence": "always measured against the untouched base answering the same probe", | |
| "measured_on_this_generation": { | |
| "runs": 3, | |
| "recall_delta": 0.0023, | |
| "coherence_delta": -0.2014, | |
| "identity_delta": 0.0 | |
| } | |
| }, | |
| "inference": { | |
| "local_ollama": "mindXtrain39", | |
| "space": "https://gregory-l-mindxhfgradio.hf.space", | |
| "mcp": "the Space is an MCP server; `probe` is a tool", | |
| "node": "https://mindx.pythai.net" | |
| }, | |
| "policy": { | |
| "educational": "educational.policy.json", | |
| "drill": "bootcamp.impression.json", | |
| "feedback": "impression.bootcamp.json" | |
| }, | |
| "provenance": { | |
| "thot": "THOT.json", | |
| "inft_facets": "inft/", | |
| "lineage_dataset": "PYTHAI/mindXascension", | |
| "ascent_log": "data/logs/ascend_log.jsonl", | |
| "registry": "https://mindx.pythai.net/insight/hf/registry" | |
| }, | |
| "honesty": "135M with a recall imprint. Not a general assistant; the coach's verdict on this lineage is NOT interaction-ready (identity 16%)." | |
| } | |