GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
Sumeetkulk
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
Sumeetkulk
Model Tree
deepseek-ai/DeepSeek-R1-Distill-Llama-8B
Input Modalities
Output Modalities
Supported Functionality
| Metric | Base | SFT only | Plain GRPO | ABLS-Mol GRPO |
|---|---|---|---|---|
| Validity | 46.7% | 71.8% | 70.1% | 75.8% |
| Mean ΔQED | −0.084 | +0.018 | +0.022 | +0.006 |
| In-Band Rate | 3.3% | 52.2% | 50.8% | 48.8% |
| Composite Success | 0.4% | 21.7% | 20.0% | 21.1% |
| Mean Tanimoto | 0.401 | 0.597 | 0.554 | 0.659 |
| Novel Scaffold Rate | 51.6% | 71.6% | 77.7% | 64.0% |
ABLS-Mol achieves the best validity (75.8%) and tightest Tanimoto centering (0.659), indicating strong seed-anchored edits with minimal generic template generation.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigimport torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16,) base = AutoModelForCausalLM.from_pretrained( "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", quantization_config=bnb_config, device_map="auto", trust_remote_code=True,)model = PeftModel.from_pretrained( base, "Sumeetkulk/mol-grpo-optimizer-abls")model.eval() tokenizer = AutoTokenizer.from_pretrained( "Sumeetkulk/mol-grpo-optimizer-abls") system = ( "You are a medicinal chemist. Reason step by step about which structural " "changes will increase QED while keeping Tanimoto similarity between 0.3 " "and 0.7. Then output only the optimized SMILES.")seed_smiles = "CC1=CC=CC=C1Nc1ncnc2ccccc12" messages = [ {"role": "system", "content": system}, {"role": "user", "content": f"Seed: {seed_smiles}\nOptimize:"},]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(): out = model.generate( **inputs, max_new_tokens=200, temperature=0.85, do_sample=True, top_p=0.95, repetition_penalty=1.1, )completion = tokenizer.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=True)print(completion)# Extract: import re; re.search(r'<answer>(.*?)</answer>', completion).group(1)bibtex
@misc{mol-grpo-optimizer-abls, title = {Goal-Directed Molecular Optimization via ABLS-Mol GRPO}, year = {2026}, note = {DeepSeek-R1-Distill-Llama-8B fine-tuned with SFT + ABLS-Mol GRPO on GuacaMol for QED-maximizing molecular editing},}from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigimport torch bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16,) base = AutoModelForCausalLM.from_pretrained( "deepseek-ai/DeepSeek-R1-Distill-Llama-8B", quantization_config=bnb_config, device_map="auto", trust_remote_code=True,)model = PeftModel.from_pretrained( base, "Sumeetkulk/mol-grpo-optimizer-abls")model.eval() tokenizer = AutoTokenizer.from_pretrained( "Sumeetkulk/mol-grpo-optimizer-abls") system = ( "You are a medicinal chemist. Reason step by step about which structural " "changes will increase QED while keeping Tanimoto similarity between 0.3 " "and 0.7. Then output only the optimized SMILES.")seed_smiles = "CC1=CC=CC=C1Nc1ncnc2ccccc12" messages = [ {"role": "system", "content": system}, {"role": "user", "content": f"Seed: {seed_smiles}\nOptimize:"},]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(): out = model.generate( **inputs, max_new_tokens=200, temperature=0.85, do_sample=True, top_p=0.95, repetition_penalty=1.1, )completion = tokenizer.decode(out[0, inputs.input_ids.shape[1]:], skip_special_tokens=True)print(completion)# Extract: import re; re.search(r'<answer>(.*?)</answer>', completion).group(1)@misc{mol-grpo-optimizer-abls, title = {Goal-Directed Molecular Optimization via ABLS-Mol GRPO}, year = {2026}, note = {DeepSeek-R1-Distill-Llama-8B fine-tuned with SFT + ABLS-Mol GRPO on GuacaMol for QED-maximizing molecular editing},}