---
base_model: unsloth/LFM2.5-1.2B-Instruct
library_name: peft
model_name: lfm-finetuned
pipeline_tag: text-generation
tags:
- generated_from_trainer
- hf_jobs
- trl
- unsloth
- sft
- lora
- peft
licence: license
datasets:
- mlabonne/FineTome-100k
---

# lfm-finetuned

A LoRA adapter fine-tuned on top of [`unsloth/LFM2.5-1.2B-Instruct`](https://huggingface.co/unsloth/LFM2.5-1.2B-Instruct), trained with [TRL](https://github.com/huggingface/trl)'s SFT trainer on [`mlabonne/FineTome-100k`](https://huggingface.co/datasets/mlabonne/FineTome-100k).

> **Note:** this repo contains the **LoRA adapter only** (`adapter_model.safetensors` + `adapter_config.json`), not a full standalone model. Load it on top of the base model with `peft`, or merge it once and use it as a regular causal LM (see below).

## Install

```bash
pip install -U torch transformers peft accelerate
```

## Quick start — load the adapter on top of the base model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel

base_id    = "unsloth/LFM2.5-1.2B-Instruct"
adapter_id = "MenemAI/lfm-finetuned"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype="auto",
    device_map="cuda",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base, adapter_id)
model.eval()

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
output = generator(
    [{"role": "user", "content": question}],
    max_new_tokens=512,
    return_full_text=False,
)[0]
print(output["generated_text"])
```

CPU-only? Drop `device_map="cuda"` and pass `device_map="cpu"` (or `"auto"`); generation will be slow but works.

## Run on Hugging Face Jobs

The script below works as-is with `hf jobs uv run`. The PEP 723 header makes `uv` install the right deps inside the job.

```python
# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "torch",
#   "transformers",
#   "peft",
#   "accelerate",
# ]
# ///
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel

base_id    = "unsloth/LFM2.5-1.2B-Instruct"
adapter_id = "MenemAI/lfm-finetuned"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    base_id, torch_dtype="auto", device_map="cuda", trust_remote_code=True
)
model = PeftModel.from_pretrained(base, adapter_id).eval()

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(generator(
    [{"role": "user", "content": "Hello!"}],
    max_new_tokens=512,
    return_full_text=False,
)[0]["generated_text"])
```

```bash
hf jobs uv run --flavor a10g-small ./test.py
```

## Optional — merge the adapter into the base model

If you want a single self-contained checkpoint (faster cold start, no `peft` at inference time):

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained(
    "unsloth/LFM2.5-1.2B-Instruct", torch_dtype="auto", trust_remote_code=True
)
merged = PeftModel.from_pretrained(base, "MenemAI/lfm-finetuned").merge_and_unload()
merged.save_pretrained("lfm-merged")
AutoTokenizer.from_pretrained("MenemAI/lfm-finetuned", trust_remote_code=True).save_pretrained("lfm-merged")
```

After merging you can load it with a plain `pipeline("text-generation", model="./lfm-merged", device="cuda")` or push it to a new repo with `hf upload <your-user>/lfm-merged ./lfm-merged`.

## Training

- **Method:** SFT via TRL
- **Base model:** `unsloth/LFM2.5-1.2B-Instruct`
- **Dataset:** `mlabonne/FineTome-100k`
- **Acceleration:** Unsloth
- **Infrastructure:** Hugging Face Jobs

### Framework versions

- TRL: 0.22.2
- Transformers: 4.57.3
- PyTorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
- PEFT: required at inference time when loading the adapter directly

## Citations

```bibtex
@misc{vonwerra2022trl,
  title        = {{TRL: Transformer Reinforcement Learning}},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
  year         = 2020,
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}
```