This SLM does narrow tasks related to AI research papers! Should be run using the neural-txt harness: https://github.com/avbiswas/neural-txt

Part of the Neural Breakdown youtube course.

Install

# Base (no inference backend)
pip install neural-txt

# With HuggingFace backend (torch)
pip install neural-txt[hf]

# With MLX backend (Apple Silicon)
pip install neural-txt[mlx]

Quick start

from neuraltxt import NeuralTxt

model = NeuralTxt(backend="mlx") # or backend="hf"

passage = """
Transformers have revolutionized NLP by introducing the self-attention
mechanism. Unlike RNNs, transformers process all tokens in parallel,
leading to significant training speedups.
"""

# Extract key points
bullets = model.extract_bullets(passage)

# Generate question-answer pairs
pairs = model.generate_qa_pairs(passage)

# Extract knowledge graph triplets
triplets = model.extract_triplets(passage)

JSON mode

Every method supports json=True for guaranteed structured output via outlines:

# Returns a BulletsOutput pydantic model
bullets = model.extract_bullets(passage, json=True)
print(bullets.bullets) # list[str]

# Returns a QAPairsOutput pydantic model
qa = model.generate_qa_pairs(passage, json=True)
for pair in qa.pairs:
 print(pair.question, pair.answer)

# Returns a TripletsOutput pydantic model
triplets = model.extract_triplets(passage, json=True)
for t in triplets.triplets:
 print(t.subject, t.relation, t.object)

API

How these models are made

All this is part of this YouTube course by Neural Breakdown:

• Course Video 1 (CPT): https://youtu.be/B8Ur62D3J3U
• Course Video 2 (SFT): https://youtu.be/gvZIUEL6Ruc
• Course Video 3 (DPO): https://youtu.be/qQwg2nFtZ1Y

Other models in this family:

• HF SFT: paperbd/smollm_135M_neuraltxt_v1
• MLX SFT: paperbd/smollm_135M_neuraltxt_mlx_v1
• HF DPO: paperbd/smollm_135M_neuraltxt_dpo_v2
• MLX DPO: paperbd/smollm_135M_neuraltxt_mlx_dpo_v2

Base model

HuggingFaceTB/SmolLM-135M

(122)