GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
ridoley
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
ridoley
Model Tree
mistralai/Mistral-7B-Instruct-v0.3
Input Modalities
Output Modalities
Supported Functionality
Install dependencies:
bash
pip install torch transformers peft bitsandbytes accelerateAuthenticate with Hugging Face if required:
bash
hf auth loginpython
import torchfrom peft import PeftModelfrom transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,) base_id = "mistralai/Mistral-7B-Instruct-v0.3"adapter_id = "ridoley/mistral7b-vaa-general-qlora" bnb = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16,) tokenizer = AutoTokenizer.from_pretrained(base_id) base = AutoModelForCausalLM.from_pretrained( base_id, quantization_config=bnb, device_map="auto",) model = PeftModel.from_pretrained(base, adapter_id)model.eval() messages = [ { "role": "system", "content": ( "You are a Lean and BPM expert. Classify all process activities " "as VA, BVA, or NVA. Return only valid JSON with keys " "process_name and activities. Each activity should include " "activity_name, label, waste_type, and justification." ), }, { "role": "user", "content": ( "Process Name: Goods Delivery Process\n" "1. Receive shipping order document\n" "2. Receive goods from customer\n" "3. Deliver goods to customer address\n" "Classify all activities. Return JSON only." ), },] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=512, do_sample=False, ) generated_tokens = output[0][inputs["input_ids"].shape[1]:]print(tokenizer.decode(generated_tokens, skip_special_tokens=True))Evaluated on a test set of 253 real-world business-process activities, the fine-tuned adapter achieved 79.05% accuracy and a 76.86% Macro F1-score.
| Model | Accuracy | Macro F1 |
|---|---|---|
| Mistral-7B-Instruct-v0.3 (base) | 68.38% | 58.12% |
| Gemini 2.5 Flash Lite | 66.01% | 63.08% |
| Llama 3.3 70B | 69.17% | 67.19% |
| Mistral-7B-Instruct-v0.3 + QLoRA | 79.05% | 76.86% |
Compared with the base Mistral model, the fine-tuned adapter improved Macro F1 by 18.74 percentage points and accuracy by 10.67 percentage points.
These results apply to the reported test set. Performance may vary across datasets and business-process domains.
| Setting | Value |
|---|---|
| Fine-tuning method | QLoRA (4-bit NF4) + LoRA |
Rank r | 16 |
| LoRA alpha | 32 |
| Dropout | 0.05 |
| Target modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
waste_type field is left empty for VA/BVA.The methodology and evaluation are described in the published paper:
Automating Value-Added Analysis in Business Processes Using a QLoRA Fine-Tuned Small Language Model. IEEE ICISS 2026.
If you use this adapter in academic work, please cite the paper using the official citation metadata from IEEE Xplore.
This adapter repository is licensed under Apache-2.0. The base model has its own license and access terms. Review the Mistral-7B-Instruct-v0.3 model card before use.
pip install torch transformers peft bitsandbytes accelerateimport torchfrom peft import PeftModelfrom transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig,) base_id = "mistralai/Mistral-7B-Instruct-v0.3"adapter_id = "ridoley/mistral7b-vaa-general-qlora" bnb = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16,) tokenizer = AutoTokenizer.from_pretrained(base_id) base = AutoModelForCausalLM.from_pretrained( base_id, quantization_config=bnb, device_map="auto",) model = PeftModel.from_pretrained(base, adapter_id)model.eval() messages = [ { "role": "system", "content": ( "You are a Lean and BPM expert. Classify all process activities " "as VA, BVA, or NVA. Return only valid JSON with keys " "process_name and activities. Each activity should include " "activity_name, label, waste_type, and justification." ), }, { "role": "user", "content": ( "Process Name: Goods Delivery Process\n" "1. Receive shipping order document\n" "2. Receive goods from customer\n" "3. Deliver goods to customer address\n" "Classify all activities. Return JSON only." ), },] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True,) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=512, do_sample=False, ) generated_tokens = output[0][inputs["input_ids"].shape[1]:]print(tokenizer.decode(generated_tokens, skip_special_tokens=True))