GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
vanta-research
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
vanta-research
Model Tree
mistralai/Mistral-7B-Instruct-v0.2
Input Modalities
Output Modalities
Supported Functionality
Apollo V1 7B is a specialized language model optimized for reasoning-intensive tasks. The model demonstrates exceptional performance in logical reasoning, mathematical problem-solving, and legal analysis through targeted fine-tuning on curated reasoning datasets.
Validated by VANTA Research Reasoning Evaluation (VRRE): Apollo V1 7B was comprehensively evaluated using our novel semantic framework that detects reasoning improvements invisible to standard benchmarks. VRRE revealed critical performance insights that traditional benchmarks missed entirely, establishing it as an essential tool for LLM reasoning assessment.
python
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch # Load base model and tokenizermodel_name = "mistralai/Mistral-7B-Instruct-v0.2"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto") # Load and apply LoRA adaptermodel = PeftModel.from_pretrained(model, "vanta-research/apollo-v1-7b") # Example usageprompt = "Solve this logical reasoning problem: If all cats are mammals, and Fluffy is a cat, what can we conclude about Fluffy?" inputs = tokenizer(prompt, return_tensors="pt")with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=256, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True)print(response)This model is released under the Apache 2.0 License. See LICENSE for details.
from transformers import AutoTokenizer, AutoModelForCausalLMfrom peft import PeftModelimport torch # Load base model and tokenizermodel_name = "mistralai/Mistral-7B-Instruct-v0.2"tokenizer = AutoTokenizer.from_pretrained(model_name)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto") # Load and apply LoRA adaptermodel = PeftModel.from_pretrained(model, "vanta-research/apollo-v1-7b") # Example usageprompt = "Solve this logical reasoning problem: If all cats are mammals, and Fluffy is a cat, what can we conclude about Fluffy?" inputs = tokenizer(prompt, return_tensors="pt")with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=256, temperature=0.7, do_sample=True, pad_token_id=tokenizer.eos_token_id ) response = tokenizer.decode(outputs[0], skip_special_tokens=True)print(response)