GGUF QUANTIZATIONS FOR HIVELABSAI'S "hivemind-32b-preview"
These quantizations were made with llama.cpp build 9181 using Bartowski's sample dataset for an importance matrix. The Q8_0, BF16, and F16 quantizations do not use any importance matrix.
As the original model is a preview release, these quantizations should be considered experimental, though functioning. I [SUPEROXIDES] am not endorsed or sponsored by Hive Labs.
Hivemind-32B-Preview
Hivemind-32B-Preview is a 32B-parameter model fine-tuned for multi-turn, emotionally attentive conversation in human-facing enterprise contexts. It is built on Qwen3-32B with a training set focused on conversational depth, emotional subtext, and sustained engagement across complex interpersonal scenarios.
Model Details
- Parameters: 32B
- Context length: 40,960 tokens
- Precision: bfloat16
- Base model: Qwen3-32B
- License: Proprietary, subject to upstream Qwen license terms
Training
Hivemind-32B-Preview was fine-tuned for multi-turn, human-facing conversations involving ambiguity and emotional subtext. The training set was purpose-built from enterprise interaction data.
Intended Use
Hivemind-32B-Preview is designed for enterprise human-agent partnership contexts: customer support, coaching-style interactions, and similar conversational deployments where sustained emotional attentiveness matters.
Scope and Ongoing Work
Hivemind-32B-Preview is a preview release. As with any conversational model, it has scope boundaries we are actively refining:
- It is not intended as a source of medical, legal, financial, or safety-critical advice, and should not be deployed in those contexts or as a replacement for professional human support.
- Performance is strongest in standard conversational scenarios.
We welcome failure-case reports from researchers and deployment partners at contact@hivelabs.dev.
Usage
vLLM (recommended)
vllm serve HiveLabsAI/hivemind-32b-preview --dtype bfloat16
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "HiveLabsAI/hivemind-32b-preview"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="bfloat16",
device_map="auto",
)
messages = [{"role": "user", "content": "Your message here"}]
inputs = tokenizer.apply_chat_template(
messages, return_tensors="pt", add_generation_prompt=True
).to(model.device)
outputs = model.generate(
inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, top_k=20
)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
About
Hivemind is developed by Hive Labs. For research collaboration, deployment questions, or to report failure cases, contact contact@hivelabs.dev.