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 Settings

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

File size: 1,718 Bytes

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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