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azherali
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Model Details
Model Provider
azherali
Model Tree
unsloth/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
python
from unsloth import FastLanguageModelimport torchmax_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!dtype = (None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+)load_in_4bit = False # Use 4bit quantization to reduce memory usage. Can be False.load_in_8bit = False # Use 8bit quantization to reduce memory usage. Can be False.model, tokenizer = FastLanguageModel.from_pretrained(model_name="azherali/Riazi-8B-Instruct", # Choose ANYmax_seq_length=max_seq_length,dtype=dtype,load_in_4bit=load_in_4bit,load_in_8bit=load_in_8bit,# token = "YOUR_HF_TOKEN", # HF Token for gated models)FastLanguageModel.for_inference(model) # Enable native 2x faster inferencereasoning_start = "<reasoning>"reasoning_end = "</reasoning>"solution_start = "<SOLUTION>"solution_end = "</SOLUTION>"system_prompt = f"""You are given a problem.Think about the problem and provide your working out.Place your reasoning between {reasoning_start} and {reasoning_end}.Then, provide your final solution between{solution_start} and {solution_end}.Always answer in Urdu."""message = [{"role": "system", "content": system_prompt},{"role": "user","content": "پانچ بچوں نے 20 چاکلیٹس برابر بانٹیں۔ ہر بچے کو کتنی چاکلیٹس ملیں گی؟"}]text = tokenizer.apply_chat_template(message,tokenize=False,add_generation_prompt=True,enable_thinking=False,# Must add for generation)# Inference Using Tranformer Libraryfrom transformers import TextStreamer_ = model.generate(**tokenizer(text, return_tensors="pt").to("cuda"),temperature=0.6,top_p=0.95,top_k=20, # For non thinkingstreamer=TextStreamer(tokenizer, skip_prompt=True),)# Inference Using VLLM(fast)from vllm import SamplingParamsdef generate_answer(problem):message = [{"role": "system", "content": system_prompt},{"role": "user", "content": problem},]text = tokenizer.apply_chat_template(message,add_generation_prompt = True, # Must add for generationtokenize = False,enable_thinking=False,)sampling_params = SamplingParams(max_tokens=2048,temperature=0.6,top_p=0.95,top_k=20,)output = model.fast_generate(text,sampling_params = sampling_params,lora_request =None,)[0].outputs[0].textreturn outputgenerate_answer("پانچ بچوں نے 20 چاکلیٹس برابر بانٹیں۔ ہر بچے کو کتنی چاکلیٹس ملیں گی؟")
This model was trained with SFT.