Instructions to use sfanm/nltower-100m-sft-tw1X-e2lr05 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sfanm/nltower-100m-sft-tw1X-e2lr05 with Transformers:

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

pipe = pipeline("text-generation", model="sfanm/nltower-100m-sft-tw1X-e2lr05")
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("sfanm/nltower-100m-sft-tw1X-e2lr05")
model = AutoModelForCausalLM.from_pretrained("sfanm/nltower-100m-sft-tw1X-e2lr05", 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 sfanm/nltower-100m-sft-tw1X-e2lr05 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sfanm/nltower-100m-sft-tw1X-e2lr05"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sfanm/nltower-100m-sft-tw1X-e2lr05",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sfanm/nltower-100m-sft-tw1X-e2lr05

SGLang

How to use sfanm/nltower-100m-sft-tw1X-e2lr05 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 "sfanm/nltower-100m-sft-tw1X-e2lr05" \
    --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": "sfanm/nltower-100m-sft-tw1X-e2lr05",
		"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 "sfanm/nltower-100m-sft-tw1X-e2lr05" \
        --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": "sfanm/nltower-100m-sft-tw1X-e2lr05",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sfanm/nltower-100m-sft-tw1X-e2lr05 with Docker Model Runner:
```
docker model run hf.co/sfanm/nltower-100m-sft-tw1X-e2lr05
```

nltower-100m-sft-tw1X-e2lr05

A 124M-parameter Llama-style model from the nl_tower study: the end-of-pretraining base model fine-tuned on solver-written reasoning traces for 2 epochs at learning rate 5e-5. It is the stage-1 starting point of the "staged" recipe (SFT, then pure RL without demonstrations), not a general-purpose assistant.

This repository is part of the nl_tower (100M) collection on the Hugging Face Hub (5 CPT models, 6 SFT models, the base model and 4 datasets).

nl_tower in brief

nl_tower is a synthetic reasoning task. Each puzzle places 6-12 people on distinct floors (1-40) of a tower. The prompt lists the occupied floors, each person's best friend, and clues that relate the floors of two terms ("greater than", "k floors above", sums, parity, "twice", highest/lowest). Many clues refer to people indirectly, for example "the best friend of the person on the second-highest floor"; such a clue becomes usable only after other clues have pinned part of the answer. Each puzzle has a unique solution that a step-by-step solver reaches without guessing.

The study asks whether reinforcement learning can teach reasoning compositions that the imitation data never shows:

Covered patterns (shown in the mid-training traces): friend ("Ava's best friend"), friend2 ("the best friend of Ava's best friend"), order ("the person directly above Ava"), rankn ("the person on the second-highest floor"), dfriend_occ ("the best friend of the person on floor 19").
Held-out patterns (appear only in RL prompts and tests): dfriend_rank ("the best friend of the person on the highest floor"), dfriend_above ("the best friend of the person directly above Ava"), and nested (two-level references).

Test rungs: easy (nltw_e, 6-7 people, covered only), covered mid (nltw_m0, 8-9 people, covered only), held-out mid (nltw_m, 8-9 people, 2-4 held-out steps), size-only hard (nltw_hb, 10-11 people, 2-4 held-out steps) and 12 people (nltw_hb12, test only).

Position in the pipeline

base pretraining (iteration 30,000, before the cooldown) --CPT-N (cooldown + N trace sequences per batch)--> CPT-N models --RL--> policies
base pretraining (iteration 38,147, end of the run) --SFT on the same traces--> SFT models --RL (no demos)--> policies

This model is one of the six staged SFT starting points (1 / 2 / 4 epochs x learning rate 2e-4 / 5e-5). Note that it starts from iteration 38,147 of the base run (the end of pretraining, after the full cooldown), not from sfanm/nltower-100m-base (iteration 30,000).

Training (SFT)

Start: end of the base pretraining run (iteration 38,147); that checkpoint is not part of this release.
Data: all 796,175 traces of sfanm/nltower-traces-tw1X (config traces), easy and covered-mid puzzles with covered patterns only.
Loss: token-level negative log-likelihood on the trace tokens plus EOS; prompt tokens get no loss. Sequences up to 2,048 tokens (the longest record has 1,218).
Optimiser: AdamW (beta1 0.9, beta2 0.95, weight decay 0.01), fp32 weights, peak learning rate 5e-5, cosine schedule with 5 % warmup.
Batch: 32 sequences per step on 4 GPUs (data parallel); 24,881 steps per epoch, 49,762 steps in total (2 epochs).

Architecture

property	value
architecture	`LlamaForCausalLM` (decoder-only)
parameters	123,587,328 (embeddings tied with the output layer)
layers / hidden size / heads	12 / 768 / 12 (head dim 64, no grouped-query attention)
MLP	SwiGLU, intermediate size 2,048
normalisation / position	RMSNorm (eps 1e-5) / RoPE (theta 10,000)
tokenizer	GPT-2 BPE (50,257 tokens; embedding padded to 50,304)
special tokens	`<\|endoftext\|>` (id 50256) is BOS, EOS and PAD
context length	2,048 tokens
weights	`model.safetensors`, bfloat16

Prompt format and usage

The prompt is the puzzle text followed by "\n\nAnswer: ". The model writes its reasoning and ends with the full assignment in \boxed{Name=floor, ...}. A completion is correct when every Name=floor pair in the boxed answer matches the solution (order does not matter). The chat template only concatenates message contents (no role markers), so apply_chat_template and the raw prompt give the same tokens.

from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset

repo = "sfanm/nltower-100m-sft-tw1X-e2lr05"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16")

test = load_dataset("sfanm/nltower-env", "tests", split="nltw_m0")
prompt = test[0]["prompt"]                      # question + "\n\nAnswer: "
ids = tok(prompt, return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=1280, do_sample=False, pad_token_id=tok.eos_token_id)
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))   # ... \boxed{Name=floor, ...}
print("gold:", test[0]["answer"])

Evaluation

Zero-shot screen of this model before RL. Protocol: 1,024 test questions per rung; 64 samples per question at temperature 1.0 (top-p 1.0) and up to 1,280 new tokens with vLLM in bfloat16; pass@k uses the unbiased estimator 1 - C(n-c, k)/C(n, k) with n = 64; greedy is one temperature-0 sample. Test sets: sfanm/nltower-env, config tests.

test rung	pass@1	pass@64	greedy
easy (`nltw_e`)	0.913	1.000	0.947
covered mid (`nltw_m0`)	0.788	1.000	0.853
held-out mid (`nltw_m`)	0.021	0.237	0.025

The covered rungs are nearly solved, while the held-out-pattern rungs stay low: imitation of covered patterns alone does not teach the held-out compositions.

RL runs that start from this model and have evaluations in sfanm/nltower-results (filter init == "staged-2ep-lr5e-5", the name of this model in the code), by setting (base: 8-9-person prompts, 6,000 updates; extended: 10-11-person prompts added, 12,000 updates) and group:

setting	group	runs
base	staged	2
extended	staged	2

Intended use and limitations

Intended for research on imitation followed by reinforcement learning, as the stage-1 model of the staged recipe.
Fine-tuning on traces largely overwrites general text ability (its loss on ordinary English prose is high); use it only on nl_tower prompts.
124M parameters and a synthetic task: results may not transfer to larger models or natural tasks. Not instruction-tuned or safety-tuned; not for user-facing use.

Training data

Pretraining: karpathy/climbmix-400b-shuffle, allenai/dolma3_dolmino_mix-100B-1025.
SFT traces: sfanm/nltower-traces-tw1X. They are written by a deterministic solver program, not by a language model.
Tests and RL prompts: sfanm/nltower-env, sfanm/nltower-rl-mixes.

Code

Code: https://github.com/yudasong/unified-training/tree/release (environment generator, solver, data builders, training recipes and evaluator).

License

No license has been chosen for these weights yet, so the metadata says license: other. The base model was pretrained on:

karpathy/climbmix-400b-shuffle (75 % of tokens): a reshuffled copy of NVIDIA's ClimbMix. The copy is tagged MIT, while the original nvidia/Nemotron-ClimbMix card gives CC BY-NC 4.0 and says it is for research and development only.
allenai/dolma3_dolmino_mix-100B-1025 (25 % of tokens): ODC-By 1.0, with a note that it is intended for research and educational use.

Use the weights only in ways that the terms of these datasets allow. The nl_tower data used after pretraining is the authors' own synthetic data (Apache-2.0, see the dataset repositories).

Authors and citation

Authors and citation: to be added.

This release (files and this card) was prepared with AI assistance (Claude, an Anthropic model) and is being reviewed by the authors.

Downloads last month: 632

Safetensors

Model size

0.1B params

Tensor type

BF16

sfanm
/

nltower-100m-sft-tw1X-e2lr05