messages = [ "role": "user", "content": "Summarize this 20k token document..." ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device) output = model.generate(inputs, max_new_tokens=512, temperature=0.7) print(tokenizer.decode(output[0][inputs.shape[1]:])) Bobbie works out-of-the-box with vLLM 0.6.0+:
They explicitly filtered out any data containing eval benchmark examples (MMLU, GSM8K, HumanEval) using 13-gram overlap detection. This means Bobbie's benchmarks are likely not contaminated. 4. Performance Benchmarks We ran Bobbie-7B-Instruct against Llama-3-8B-Instruct and Mistral-7B-v0.3 on an RTX 4090. bobbie-model
| Benchmark | Bobbie-7B | Llama-3-8B | Mistral-7B | |-----------|-----------|------------|------------| | MMLU (5-shot) | 64.2 | 66.7 | 63.9 | | GSM8K (8-shot) | 52.8 | 54.9 | 50.3 | | HumanEval (pass@1) | 32.5 | 34.2 | 31.8 | | | 82.3 | 67.1 | 71.4 | | Inference tokens/sec | 98 | 72 | 88 | messages = [ "role": "user", "content": "Summarize this
If you’ve been following the open-source LLM space, you’ve likely memorized the specs of Llama 3, Mixtral, and Qwen. But a new contender has been quietly gaining traction in the "small model" category: . The research collective has hinted at a 13B
The research collective has hinted at a 13B version with Mixture of Depths (MoD) later this year. Until then, Bobbie-7B deserves a spot in your evaluation pipeline.
Bobbie loses marginally on standard benchmarks but dramatically outperforms on long-context retrieval (RULER). At 32k context, Bobbie is also 36% faster than Llama-3 due to its BiGLU and windowed attention strategy. 5. How to Use Bobbie-Model The model is available on Hugging Face as bobbie-collective/bobbie-7b-base and bobbie-7b-instruct . Transformers Example from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_name = "bobbie-collective/bobbie-7b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto" )
