Llama-ML-Expert-Instruct-1b/README.md

license, base_model, tags, datasets, language, pipeline_tag

license	base_model	tags	datasets	language	pipeline_tag
llama3.2	unsloth/Llama-3.2-1B-bnb-4bit	unsloth sft trl lora arxiv text-generation domain-expert machine-learning	danivpv/ml-arxiv-instruct mlabonne/FineTome-Alpaca-100k	en	text-generation

Llama-ML-Expert-Instruct-1b

An instruction fine-tuned Small Language Model (SLM) specialized in Machine Learning domain expertise. Fine-tuned from unsloth/Llama-3.2-1B-bnb-4bit using LoRA adapters — efficient enough to run on modest hardware while retaining strong ML-domain reasoning.

Part of the LLM-ArXiv-Domain-Expert pipeline, which builds domain-expert LLMs from ArXiv papers end to end: paper parsing → instruction/preference dataset generation → SFT → (optional) DPO.

Model Details

Base model	unsloth/Llama-3.2-1B-bnb-4bit (Llama 3.2, 1B)
Fine-tuning method	LoRA — rank 32, alpha 32, dropout 0.0
LoRA target modules	q_proj, k_proj, v_proj, up_proj, down_proj, o_proj, gate_proj
Optimizer	adamw_8bit
Prompt format	Alpaca (### Instruction: ... ### Response: ...)

Training Data

• danivpv/ml-arxiv-instruct — instruction-answer pairs synthesized from ArXiv ML papers
• 10,000-sample subset of mlabonne/FineTome-Alpaca-100k — preserves general instruction-following alongside domain specialization

How to Use

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="danivpv/Llama-ML-Expert-Instruct-1b",
    max_seq_length=2048,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

prompt = """### Instruction:
{your ML question here}

### Response:
"""
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• 1B-parameter base model: strong for domain-specialized short-form Q&A, not a general-purpose reasoning or long-context model.
• Trained on synthetically generated instruction data (see ml-arxiv-instruct for the generation pipeline) — inherits whatever biases or gaps exist in the source LLM used to generate that data.

Related

• Dataset (SFT): danivpv/ml-arxiv-instruct
• Dataset (DPO): danivpv/ml-arxiv-dpo
• Pipeline code: github.com/danivpv/LLM-ArXiv-Domain-Expert