Instructions to use yasserrmd/SciReason-LFM2-2.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yasserrmd/SciReason-LFM2-2.6B with Transformers:

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

pipe = pipeline("text-generation", model="yasserrmd/SciReason-LFM2-2.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yasserrmd/SciReason-LFM2-2.6B")
model = AutoModelForCausalLM.from_pretrained("yasserrmd/SciReason-LFM2-2.6B", 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 yasserrmd/SciReason-LFM2-2.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yasserrmd/SciReason-LFM2-2.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yasserrmd/SciReason-LFM2-2.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yasserrmd/SciReason-LFM2-2.6B

SGLang

How to use yasserrmd/SciReason-LFM2-2.6B 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 "yasserrmd/SciReason-LFM2-2.6B" \
    --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": "yasserrmd/SciReason-LFM2-2.6B",
		"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 "yasserrmd/SciReason-LFM2-2.6B" \
        --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": "yasserrmd/SciReason-LFM2-2.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use yasserrmd/SciReason-LFM2-2.6B with Docker Model Runner:
```
docker model run hf.co/yasserrmd/SciReason-LFM2-2.6B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SciReason-LFM2-2.6B

Model Overview

SciReason-LFM2-2.6B is a fine-tuned version of LiquidAI/LFM2-2.6B, trained with Unsloth on the OpenScienceReasoning-2 dataset.
The fine-tuning enhances the base model’s ability to handle multi-step scientific reasoning and produce coherent chain-of-thought explanations.

Training Configuration

Framework: Unsloth
Dataset: nvidia/OpenScienceReasoning-2
Examples: ~11,000
Epochs: 1
Total Steps: 1,375
Batch size per device: 2
Gradient Accumulation Steps: 4
Effective Batch Size: 8
Trainable Parameters: ~20M (LoRA / PEFT with Unsloth smart offloading)
Optimizer: AdamW
Learning Rate: 2e-4
Weight Decay: 0.01
LR Scheduler: cosine with warmup
Hardware: Single GPU (Unsloth offloading enabled)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_id = "yasserrmd/SciReason-LFM2-2.6B"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16",
#    attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Generate answer
prompt = """	
Solve the following problem. Make sure to put the answer (and only answer) inside \boxed{}.

Based on analysis of multinational aeromedical databases (e.g., EASA's EMPR, FAA's CAMI database, and military longitudinal studies), which statement accurately characterizes a fundamental limitation in definitively establishing cause-and-effect relationships for cardiovascular morbidity trends among commercial aircrew?

A: Stratified sampling protocols universally eliminate survivorship bias
B: Retroactive harmonization of biochemical markers across jurisdictions enables precise meta-analysis
C: Inability to fully adjust for dominant confounding variables (e.g., socioeconomic status, undisclosed supplement use)
D: Cohort studies consistently show declining age-adjusted myocardial infarction rates compared to the general population
E: Mandatory polysomnography data provides complete correction for sleep disorder comorbidities
F: Radiation dose metrics exhibit a linear correlation with arrhythmia incidence in jet aircraft pilots
G: Genome-wide association studies have identified fully penetrant monogenic risk variants specific to aviators
H: Continuous blood pressure monitoring during all flight phases yields statistically significant longitudinal datasets
I: Pharmacokinetic interactions between hypoxia and statins are conclusively established in CRF models
J: Regulatory divergence causes morbidity rates to universally decline across all regions after 2018"""
input_ids = tokenizer.apply_chat_template(
    [{
    "role":"system",
    "content":""" 
    You are a reasoning assistant.

When solving problems:
- Always place your reasoning inside think tags.
- Think in structured steps, but keep it concise (3–4 short steps maximum).
- Avoid repeating yourself or giving unnecessary background.
- Use bullet points or brief numbered steps for clarity inside think tag.
- After think end tag, provide only the final answer clearly and directly.
- Do not include reasoning outside of the think tags.


    """
},
        {"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
).to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_new_tokens=1024,
)

print(tokenizer.decode(output[0], skip_special_tokens=False))

# <|startoftext|><|im_start|>user
# What is C. elegans?<|im_end|>
# <|im_start|>assistant
# C. elegans, also known as Caenorhabditis elegans, is a small, free-living
# nematode worm (roundworm) that belongs to the phylum Nematoda.

Intended Use

This model is designed for:

Scientific reasoning tasks
Educational Q&A
Step-by-step logical problem solving

⚠️ Disclaimer: Not intended for clinical or legal decision-making.

License

Apache-2.0 License. See LICENSE.

Acknowledgements

LiquidAI for LFM2-2.6B
NVIDIA for OpenScienceReasoning-2
Unsloth for efficient fine-tuning with gradient offloading

Downloads last month: 21

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for yasserrmd/SciReason-LFM2-2.6B

Base model

LiquidAI/LFM2-2.6B

Finetuned

(39)

this model

Quantizations

2 models

yasserrmd
/

SciReason-LFM2-2.6B