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-instructfor 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