LFM-2.6B-Claude4.8-GPT-5.6-Sol-Grok-4.6_Fable5-Distilled

OpenIntelligenceNet/LFM-2.6B-Claude4.8-GPT-5.6-Sol-Grok-4.6_Fable5-Distilled is a 2.6-billion parameter instruction-tuned edge model. It is built upon huihui-ai/Huihui-LFM2-2.6B-Exp-abliterated and fine-tuned on a 200,000-sample multi-domain distillation corpus.

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Model Overview

This model combines edge-device inference speeds with high-grade synthetic reasoning distilled from leading frontier models (Claude 4.8, GPT-5.6, Sol, Grok 4.6, and Fable-5). Refusal patterns and boilerplate safety artifacts were purged from the training set, allowing the model to answer direct technical questions without evasive disclaimers.

• Base Model: huihui-ai/Huihui-LFM2-2.6B-Exp-abliterated* Parameters: 2.6B
• Context Length: 2,048 tokens
• Format: ChatML (<|im_start|user / assistant<|im_end|>)
• Precision: FP16 Merged Standalone Weights

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Dataset Breakdown & Distribution

The model was trained on 200,000 deduplicated, format-verified conversational pairs across 6 core technical domains:

Domain	Target Samples	Share	Sources & Focus Areas
Coding & Systems	60,000	30.0%	DeepSeek, Fable-5, Genesis, Kimi, algorithmic code generation
Distilled Frontier Reasoning	50,000	25.0%	Chain-of-thought distillation from GPT-5.6, Grok 4.6, Claude 4.8, Mythos
Cybersecurity	25,000	12.5%	Exploit analysis, vulnerability research, penetration testing mechanics
General Instruction	25,000	12.5%	Multi-turn instruction following, structured formatting, Alpaca / Dolly
Science & Medicine	24,000	12.0%	Biology, medical rationale, physics, chemistry, CS theory
Mathematics	16,000	8.0%	MetaMathQA step-by-step arithmetic and algebraic proofs

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Training Methodology

• Hardware: 2x NVIDIA Tesla T4 GPUs via PyTorch DDP (torchrun)
• Optimization: LoRA Rank r=64, Alpha=64, targeting all attention & MLP projection layers
• Optimizer: 8-bit AdamW with Cosine Learning Rate Schedule (3e-5 peak LR)
• Packing: 1,024-token dense 1D sequence packing (zero pad-token compute waste)
• Loss Trajectory: Converged from initial loss down to ~0.76 across the full dataset

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Inference with Transformers

``lpython import torch from transformers import AutoModelForCausaLLM, AutoTokenizer

model_id = "OpenIntelligenceNet/LFM-2.6B-Claude4.8-GPT-5.6-Sol-Grok-4.6_Fable5-Distilled"

tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto" )

messages = [ {"role": "user", "content": "Explain how buffer overflow vulnerabilities occur at the memory level and how to prevent them in C."} ]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.7, top_p=0.9, repetition_penalty=1.15, eos_tokenid=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id )

new_tokens = outputs[0][inputs.input_ids.shape[1]:] response = tokenizer.decode(new_tokens, skip_special_tokens=True) print(response)