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Experience next-gen AI model deployment on Cyfuture Cloud with Phind CodeLlama 34B v2. Engineered for advanced natural language understanding and large-scale inference workloads, optimized for speed and scalability.
Phind CodeLlama 34B v2 is a state-of-the-art, instruction-tuned language model fine-tuned from CodeLlama, achieving 73.8% pass@1 accuracy on HumanEval benchmarks for code generation tasks. With 34 billion parameters and support for a 4096-token context length, it excels in multi-lingual programming across Python, C/C++, TypeScript, Java, and more, making it ideal for complex software development and problem-solving workflows. Trained on 1.5B high-quality programming tokens using 32 A100 GPUs, the model delivers efficient, readable code output in Alpaca/Vicuna format for seamless integration.
Phind CodeLlama 34B v2 is an advanced, instruction-tuned large language model fine-tuned from CodeLlama 34B, specifically optimized for programming tasks. It achieves state-of-the-art performance with a 73.8% pass@1 score on the HumanEval benchmark, surpassing its predecessor Phind-CodeLlama-34B-v1. Trained on an additional 1.5 billion tokens of high-quality programming data using 32 A100-80GB GPUs in just 15 hours, this multilingual model excels in generating high-quality code across languages like Python, C/C++, TypeScript, Java, and more.
Follows Alpaca/Vicuna format for steerable responses, enabling precise control over code generation and problem-solving tasks through natural language prompts.
Iteratively refined from CodeLlama base models with 1.5B specialized programming tokens, enhancing accuracy for complex coding scenarios while maintaining efficiency.
Supports diverse programming languages simultaneously, processing inputs up to 4096 tokens to generate readable, maintainable code in Python, Java, C++, and others.
Leverages transformer architecture for high HumanEval performance (73.8% pass@1), solving programming problems correctly on first attempts through contextual understanding.
Compatible with APIs, Ollama, and GPU-accelerated environments for rapid inference, making it suitable for developer tools and integrated coding assistants.
| Category | Specification |
|---|---|
| Processor Architecture: |
AI-optimized x86_64 / ARM-based accelerated compute nodes |
| CPU Options: |
Up to 96 vCPUs per instance High-performance cores (3.7+ GHz burst) tailored for code generation, debugging, and inference Multi-threaded compiler and interpreter task optimization |
| Workload Optimization: |
Accelerated inference for code completion, refactoring, and documentation generation Parallel execution for multi-file code analysis and contextual reasoning Optimized for fine-tuning code LLMs & developer-assist pipelines |
| Scalability: |
Dynamic horizontal & vertical scaling Auto-policy based compute provisioning for peak software builds |
| Category | Specification |
|---|---|
| RAM Options: | 16 GB – 1 TB ECC DIMM configurations |
| Local NVMe Storage: | Low-latency NVMe SSD (Up to 4 TB per instance) |
| Premium Block Storage: | SAN storage up to 40 TB per deployment |
| Object Storage: | S3-compatible storage for model checkpoints, code repositories & dataset archives |
| Backup Snapshots: | Granular daily/weekly/monthly policies with point-in-time instant rollback |
| Category | Specification |
|---|---|
| GPU Acceleration: |
NVIDIA RTX, A-Series, and L-Series GPU clusters Up to 8 GPUs per node for LLM fine-tuning Distributed training support (DeepSpeed, FSDP, ZeRO) |
| AI Framework Optimization: |
CUDA, TensorRT & CuDNN optimized ONNX Runtime & PyTorch-native support |
| Model Enhancements: |
Reduced latency inference (<150ms for token generation) High-throughput batch processing for IDE-integrated code suggestions |
| Category | Specification |
|---|---|
| Public Bandwidth: | 1–20 Gbps dedicated connections |
| Private Network: | Encrypted VLAN-based secure multi-tenant topology |
| Load Balancing: | L7 intelligent load balancing optimized for token streaming |
| Anycast Acceleration: | Global low-latency LLM request routing |
| Firewall Protection: | Layer-3/4/7 security with intelligent DDoS shielding |
| Edge Compute: | Multi-region edge nodes for on-device coding assistant workloads |
| Category | Specification |
|---|---|
| Operating Systems: | Linux (Ubuntu, Rocky, Alma, Debian), Windows Server |
| Framework & SDK Compatibility: |
Python, Java, Rust, Node.js, Go, C++ Supports LangChain, FastAPI, gRPC, WebSockets |
| DevOps Integration: |
Docker & Kubernetes native deployment Helm charts for Phind CodeLlama cluster provisioning CI/CD-ready (GitLab, GitHub Actions, Jenkins) |
| Model Hosting & API Gateway: | REST, GraphQL, gRPC endpoints for custom developer assistants & copilots |
| Category | Specification |
|---|---|
| Encryption: | AES-256 at rest | TLS 1.3 in transit |
| Identity Security: | RBAC, IAM policies, OAuth2, MFA |
| Compliance Standards: | ISO 27001, SOC 2, GDPR, PCI-DSS capable |
| Developer Privacy: | Temporary memory-only inference, no persistent code logs |
| Category | Specification |
|---|---|
| Live Telemetry: | Full observability — CPU/GPU/Memory/Token latency |
| Predictive Scaling: | AI-based algorithm for usage spikes during compile & deploy cycles |
| Logging & Auditing: | Centralized SIEM & compliance logging |
| Automation Tools: | Terraform, Ansible, ArgoCD & GitOps integration |
| Category | Specification |
|---|---|
| Uptime SLA: | 99.99% Model & API uptime reliability |
| Support Coverage: | 24×7 expert cloud engineering support with AI-specialist escalation |
| Disaster Recovery: | Multi-zone replication & instant failover environments |
| Onboarding: | Free migration, model deployment assistance & architectural consulting |
Phind CodeLlama 34B v2 achieves 73.8% pass@1 on HumanEval, setting state-of-the-art benchmarks among open-source coding models.
Supports Python, C/C++, TypeScript, Java, and more for seamless multilingual code generation and problem-solving.
Fine-tuned in just 15 hours using 32 A100-80GB GPUs on 1.5B high-quality programming tokens.
Follows Alpaca/Vicuna prompting for precise, steerable responses in coding tasks.
Handles extended sequence lengths up to 4096 tokens for complex codebases and large programming contexts.
Available via APIs, Ollama, GGUF quantization, and GPU-accelerated environments for flexible integration.
Cyfuture Cloud stands out as the premier hosting platform for Phind CodeLlama 34B v2, delivering optimized GPU infrastructure tailored for this state-of-the-art coding model. With access to high-performance NVIDIA A100 and H100 GPU clusters, Cyfuture ensures Phind CodeLlama 34B v2 achieves its impressive 73.8% HumanEval pass@1 score through rapid inference and training capabilities. MeitY-empanelled data centers in India provide data sovereignty, enterprise-grade security, and 99.99% uptime, making it ideal for developers handling complex multilingual code generation in Python, C/C++, Java, and TypeScript.
Seamless scalability and cost-effective pricing further position Cyfuture Cloud as the top choice for Phind CodeLlama 34B v2 deployments. The Kubernetes-native environment supports up to 4096-token sequences with instruction-tuned Alpaca/Vicuna prompting, enabling effortless integration for coding assistants and automated workflows. Pay-as-you-go models eliminate upfront costs while offering dedicated resources for production-scale Phind CodeLlama 34B v2 applications, backed by 24/7 expert support and compliance with global standards.
Thanks to Cyfuture Cloud's reliable and scalable Cloud CDN solutions, we were able to eliminate latency issues and ensure smooth online transactions for our global IT services. Their team's expertise and dedication to meeting our needs was truly impressive.
Since partnering with Cyfuture Cloud for complete managed services, Boloro Global has experienced a significant improvement in their IT infrastructure, with 24x7 monitoring and support, network security and data management. The team at Cyfuture Cloud provided customized solutions that perfectly fit our needs and exceeded our expectations.
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With Cyfuture Cloud's secure and reliable co-location facilities, we were able to set up our Certifying Authority with peace of mind, knowing that our sensitive data is in good hands. We couldn't have done it without Cyfuture Cloud's unwavering commitment to our success.
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With Cyfuture's efficient solution, we were able to conduct our examinations and recruitment processes seamlessly without any interruptions. Their dedicated lease line and fully managed services ensured that our operations were always up and running.
Thanks to Cyfuture's private cloud services, our European and Indian teams are now working seamlessly together with improved coordination and efficiency.
The Cyfuture team helped us streamline our database management and provided us with excellent dedicated server and LMS solutions, ensuring seamless operations across locations and optimizing our costs.
Phind CodeLlama 34B v2 is a large, instruction-tuned AI language model optimized for coding tasks, capable of generating high-quality code in multiple programming languages.
It supports Python, C/C++, TypeScript, Java, and many other popular programming languages for cross-stack development assistance.
It achieves a 73.8% pass@1 score on the HumanEval benchmark, showing high accuracy in solving programming problems on the first attempt.
Phind CodeLlama 34B v2 can process sequences up to 4096 tokens, enabling support for larger codebases and complex programming contexts.
It was fine-tuned using 1.5 billion high-quality programming tokens on a 32×A100-80GB GPU cluster over approximately 15 hours.
Yes, it is a multilingual coding model that supports development across multiple language environments seamlessly.
Yes, it supports deployment via APIs and works efficiently in GPU-accelerated and containerized environments.
It improves on Phind CodeLlama 34B v1 with higher accuracy, broader multilingual support, and a significantly larger fine-tuning dataset.
Yes, its optimized architecture and training enable responsive real-time code generation and development workflows.
Cyfuture Cloud offers optimized GPU infrastructure, secure deployment, flexible scaling, and enterprise-grade support to run Phind CodeLlama 34B v2 efficiently in production environments.
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