Instructions to use atenareply/lfm2.5-1.2b-noval-instruct-paramdelta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use atenareply/lfm2.5-1.2b-noval-instruct-paramdelta with Transformers:

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

pipe = pipeline("text-generation", model="atenareply/lfm2.5-1.2b-noval-instruct-paramdelta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("atenareply/lfm2.5-1.2b-noval-instruct-paramdelta")
model = AutoModelForCausalLM.from_pretrained("atenareply/lfm2.5-1.2b-noval-instruct-paramdelta", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use atenareply/lfm2.5-1.2b-noval-instruct-paramdelta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "atenareply/lfm2.5-1.2b-noval-instruct-paramdelta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "atenareply/lfm2.5-1.2b-noval-instruct-paramdelta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/atenareply/lfm2.5-1.2b-noval-instruct-paramdelta

SGLang

How to use atenareply/lfm2.5-1.2b-noval-instruct-paramdelta 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 "atenareply/lfm2.5-1.2b-noval-instruct-paramdelta" \
    --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": "atenareply/lfm2.5-1.2b-noval-instruct-paramdelta",
		"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 "atenareply/lfm2.5-1.2b-noval-instruct-paramdelta" \
        --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": "atenareply/lfm2.5-1.2b-noval-instruct-paramdelta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use atenareply/lfm2.5-1.2b-noval-instruct-paramdelta with Docker Model Runner:
```
docker model run hf.co/atenareply/lfm2.5-1.2b-noval-instruct-paramdelta
```

LFM2.5-1.2B Noval — Instruct (ParamΔ)

Zero-cost instruct graft on the domain CPT model via ParamΔ: Θ = noval + λ·(Instruct − Base). No training — only weight arithmetic.

Overview

Stage: ParamΔ instruct graft (no training)
Lineage: Base → CPT (noval) → ParamΔ (this model)
Method: ParamΔ [paramdelta], an instance of task arithmetic [taskarith]: the official post-training delta (Instruct − Base) is summed onto the CPT'd weights (λ=1.0), transferring instruction-following while keeping domain knowledge. Element-wise merge in fp32, saved bf16; tokenizer + chat template + generation config from the Instruct model.
Domain: fictional — Orbital Mining Corporation (OMC) technical docs + Mars Express telemetry.

Training


Method	weight arithmetic only — NO gradient training
λ (instruct delta scale)	1.0
Precision	merge fp32 → save bf16

Evaluation

No task metrics for this checkpoint (intermediate / no-training stage).

Intended use & limitations

Chat / instruction-following over the OMC / Mars-Express domain at zero post-training cost. Baseline for the agentic SFT round.

Limitations:

ParamΔ is validated in the literature on 8B–70B; on a 1.2B (more entangled weights) transfer quality is less certain — measure, don't assume.
λ=1.0 not swept; mild domain dilution observed (e.g. bare acronym 'OMC').
NOT agentic: does not execute tool-calling trajectories (see -agentic).

Innovations tested

ParamΔ at 1.2B — the paper validates 8B–70B; we test the zero-cost instruct graft at the 1.2B scale [paramdelta].

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("atenareply/lfm2.5-1.2b-noval-instruct-paramdelta")
model = AutoModelForCausalLM.from_pretrained("atenareply/lfm2.5-1.2b-noval-instruct-paramdelta")
msgs = [{"role": "user", "content": "What does the Orbital Mining Corporation do?"}]
inputs = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt", return_dict=True)
print(tok.decode(model.generate(**inputs, max_new_tokens=256)[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Citations

ParamΔ — Cao et al., ParamΔ for Direct Weight Mixing, ICLR 2025. arXiv:2504.21023
Task arithmetic — Ilharco et al., Editing Models with Task Arithmetic, 2023. arXiv:2212.04089

Card generated by noval-corp/scripts/gen_model_cards.py (standardized across the noval-corp model family).