🌟 Model Card for Torque_32B_LARGE_0.3 ⚡
Part of the Torque Series (TINY, MED, LARGE).
🔎 Model Details
📝 Model Description
Torque_32B_LARGE_0.3 is a top-tier 32B-parameter Transformer-based language model, culminating from the same two-stage Reinforcement Learning (RL) approach as Torque_1.5B_TINY_0.1 and Torque_14B_MED_0.2. Offering comprehensive capabilities, it excels at high-accuracy tasks and large-scale deployments—ideal for enterprise solutions, advanced research, and mission-critical applications.
Developed by: The Mind Expansion Network
Funded by [optional]: Internal R&D
Shared by [optional]: MindExpander
Model type: AutoRegressive LLM
Language(s) (NLP): Primarily English
License: MIT (with Qwen/Llama base constraints)
Finetuned from model [optional]: Qwen2.5 / Llama3-based
🌐 Model Sources [optional]
Repository: TheMindExpansionNetwork/Torque_32B_LARGE_0.3
Paper [optional]: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs
Demo [optional]: Coming soon
📦 Uses
🎯 Direct Use
Enterprise & Production🏢: Large-scale solutions, robust Q&A, code generation, and analytics.
Advanced Research🔬: Complex reasoning tasks (e.g., extensive math proofs, multi-domain queries).
Mission-Critical Apps🛠️: Where reliability and accuracy are paramount.
🔧 Downstream Use [optional]
Fine-Tuning: Further refine on proprietary data for specialized tasks, e.g., high-stakes scientific or financial domains.
Integration: Seamlessly blend with multi-agent frameworks that require deep chain-of-thought reasoning.
🚫 Out-of-Scope Use
Lightweight or Budget-Constrained: Try Torque_1.5B_TINY_0.1 for minimal resources, or Torque_14B_MED_0.2 for moderate setups.
⚖️ Bias, Risks, and Limitations
Hallucinations: Can fabricate info, especially with ambiguous prompts.
Bias: Data-driven biases remain a concern.
Security: Needs robust content filtering.
✅ Recommendations
Rigorous Validation: Double-check critical logic or domain outputs.
Content Moderation: Deployed systems should incorporate guardrails.
🚀 How to Get Started
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "TheMindExpansionNetwork/Torque_32B_LARGE_0.3" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).half().cuda()
prompt = "Draft a comprehensive research plan on gene editing and its ethical considerations." inputs = tokenizer(prompt, return_tensors="pt").to("cuda") outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.5) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🏗️ Training Details
⚙️ Training Data
1M+ Curated Samples: Rich coverage of advanced math, programming, specialized domains.
Sources: Merged open-source sets & large in-house data.
🔨 Training Procedure
🔍 Preprocessing [optional]
Extended coverage for scientific, technical tokens.
⚡ Training Hyperparameters
Precision: Mixed precision (fp16)
Batch Size: [More Info Needed]
Max Steps: [More Info Needed]
Learning Rate: [More Info Needed]
🏎️ Speeds, Sizes, Times [optional]
Training requires significant HPC resources.
✅ Evaluation
🔎 Testing Data, Factors & Metrics
📂 Testing Data
Comprehensive math & code sets, plus domain-specific tasks.
📊 Factors
Evaluated at multiple difficulty tiers (entry-level to expert).
🎯 Metrics
Pass@1 for math/code
Expert QA usage (human eval)
📈 Results
MATH-500: ~94.3% Pass@1
CodeForces: ~1700 rating
LiveCodeBench: ~58.1% Pass@1
(Approx. results; see DeepSeek-R1 paper)
🔬 Model Examination [optional]
Ongoing improvements to interpretability.
🌱 Environmental Impact
Hardware: HPC cluster with large GPU arrays.
Time: [More Info Needed]
Region: [More Info Needed]
🔧 Technical Specifications [optional]
🏗️ Model Architecture
32B Parameter scale, advanced chain-of-thought alignment.
🏭 Compute Infrastructure
Hardware: [More Info Needed]
Software: PyTorch, Transformers, CUDA
📖 Citation [optional]
@misc{deepseekai2025deepseekr1incentivizingreasoningcapability, title={{DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning}}, author={DeepSeek-AI and collaborators}, year={2025}, eprint={2501.12948}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2501.12948} }
🗒️ Glossary [optional]
Chain-of-Thought: The hidden reasoning steps bridging question to answer.
📚 More Information [optional]
Part of the “Torque Series” (TINY, MED, LARGE). Compare with Torque_1.5B_TINY_0.1 and Torque_14B_MED_0.2 for alternate scales. See the Torque Series Card for a full overview.
🤝 Model Card Authors [optional]
MindExpander, TheMindExpansionNetwork
📬 Model Card Contact
Email: [[email protected]]
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