Instructions to use NorthernTribe-Research/math-conjecture-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NorthernTribe-Research/math-conjecture-model with Transformers:

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

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

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("NorthernTribe-Research/math-conjecture-model", dtype="auto")

PEFT
How to use NorthernTribe-Research/math-conjecture-model with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NorthernTribe-Research/math-conjecture-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/NorthernTribe-Research/math-conjecture-model

SGLang

How to use NorthernTribe-Research/math-conjecture-model 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 "NorthernTribe-Research/math-conjecture-model" \
    --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": "NorthernTribe-Research/math-conjecture-model",
		"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 "NorthernTribe-Research/math-conjecture-model" \
        --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": "NorthernTribe-Research/math-conjecture-model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NorthernTribe-Research/math-conjecture-model with Docker Model Runner:
```
docker model run hf.co/NorthernTribe-Research/math-conjecture-model
```

math-conjecture-model / configs /math_conjecture_scratch.yaml

NorthernTribe-Research

Add tailored from-scratch training stack (tokenizer + random-init LM) for math conjecture solving

e69a71a verified 2 months ago

raw

history blame contribute delete

1.72 kB

	global:
	output_root: model_development/runs/math-conjecture-scratch
	seed: 17

	tokenizer:
	tokenizer_dir: model_development/runs/math-conjecture-scratch/tokenizer
	vocab_size: 32768
	min_frequency: 2
	max_train_rows: 220000
	special_tokens:
	- <pad>
	- <unk>
	- <s>
	- </s>
	- <\|system\|>
	- <\|user\|>
	- <\|assistant\|>

	model:
	n_layer: 12
	n_head: 12
	n_embd: 768
	n_positions: 2048
	use_bf16: true
	resid_pdrop: 0.1
	embd_pdrop: 0.1
	attn_pdrop: 0.1
	initializer_range: 0.02

	data:
	train_file: data/releases/v1/train.parquet
	validation_file: data/releases/v1/validation.parquet
	prompt_field: prompt
	target_field: target
	final_answer_field: final_answer
	proof_field: proof_formal
	max_seq_length: 2048
	max_train_samples: 240000
	max_eval_samples: 4500
	system_prompt: \|
	You are NorthernTribe Research's math-conjecture solver.
	Recover answers for solved conjectures, produce checkable reasoning, and
	preserve formal consistency suitable for Lean verification.

	training:
	output_dir: model_development/runs/math-conjecture-scratch/checkpoints
	num_train_epochs: 1
	max_steps: null
	per_device_train_batch_size: 1
	per_device_eval_batch_size: 1
	gradient_accumulation_steps: 32
	learning_rate: 2.0e-4
	weight_decay: 0.1
	warmup_ratio: 0.03
	lr_scheduler_type: cosine
	max_grad_norm: 1.0
	gradient_checkpointing: true
	logging_steps: 10
	save_steps: 250
	eval_steps: 250
	save_total_limit: 3
	dataloader_num_workers: 2

	hub:
	push_to_hub: false
	repo_id: NorthernTribe-Research/math-conjecture-model
	private: false
	commit_message: Upload scratch-trained math-conjecture solver model.

	credentials:
	path: huggingface-api-key.json