Text Generation
Transformers
GGUF
English
sft
trl
llama-cpp
devops
sre
kubernetes
k8s
docker
containers
sysadmin
linux
terraform
ansible
iaac
cicd
gitlab-ci
github-actions
nginx
postgresql
mysql
infrastructure
troubleshooting
Instructions to use denysdenysevych/Lestro-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use denysdenysevych/Lestro-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="denysdenysevych/Lestro-7B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("denysdenysevych/Lestro-7B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use denysdenysevych/Lestro-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "denysdenysevych/Lestro-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "denysdenysevych/Lestro-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/denysdenysevych/Lestro-7B
- SGLang
How to use denysdenysevych/Lestro-7B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "denysdenysevych/Lestro-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "denysdenysevych/Lestro-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "denysdenysevych/Lestro-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "denysdenysevych/Lestro-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use denysdenysevych/Lestro-7B with Docker Model Runner:
docker model run hf.co/denysdenysevych/Lestro-7B
Lestro-7B (DevOps & Infrastructure Edition)
This repository hosts the compiled GGUF edition of a customized Mistral-7B-v0.1 model. It has been fine-tuned using Low-Rank Adaptation (LoRA) specifically to handle the messy, critical system infrastructure failures that generic models frequently hallucinate on.
Targeted Problem Domains & Capabilities
This model functions as an automated site reliability engineer (SRE) and infrastructure assistant, specifically trained to diagnose and solve issues across 5 core technical layers:
1. Container Orchestration & Microservices (Kubernetes & Docker)
- Cluster Crashes: Troubleshoots infrastructure failures like
CrashLoopBackOff,OOMKilled(out-of-memory errors), and persistent volume mounting stalls (PersistentVolumeClaims). - Cluster Networking: Resolves internal network complications including
CoreDNSresolution drops, invalidIngressrule setups, or misconfiguredNetworkPolicies.
2. Infrastructure as Code (IaC Automation)
- State Management: Diagnoses configuration deployment hangs in Terraform, specifically state file lock conflicts (
state lock), provider version mismatches, and cloud provider API connection failures (AWS/Azure/GCP). - Configuration Drift: Fixes broken complex Ansible playbooks and roles suffering from OS-level version dependencies or broken execution logic in staging and production environments.
3. Databases Under Heavy Load (Storage & Reliability)
- Locking Mechanisms: Identifies and clears data layer standstills like relational database
Deadlocksin PostgreSQL or MySQL environments. - Performance Tuning: Optimizes heavy, unindexed queries and handles connection pooling leakage through middleware adjustments like
PgBouncer. - Data Integrity: Debugs replication lag anomalies and underlying disk performance degradation (
I/O bottlenecks).
4. CI/CD Automation Pipelines
- Runner Failures: Instantly parses log errors behind unexpected runner crashes inside GitLab CI workflows or GitHub Actions.
- Build Efficiencies: Resolves bloated build times caused by unoptimized Docker layer caching strategies and credential validation drops inside registries like Harbor or Docker Hub.
5. Networking, Edge Infrastructure & Security
- Web Gateways: Fine-tunes high-traffic reverse proxies (Nginx / HAProxy) throwing proxy delivery failures like
502 Bad Gatewayand504 Gateway Timeout. - Security & Firewalls: Troubleshoots edge transport security renewals (
Certbot/Let's Encrypt), process memory leak dynamics, and routing tables viaiptablesorufwrule sets.
How to Run in LM Studio
- Launch LM Studio.
- In the top search bar, type:
Karmin66/Lestro-7B. - Download the compiled
.gguffile from the repository. - Select it from the top menu dropdown inside your AI Chat panel and start diagnosing your infrastructure.
Technical Summary
- Base Architecture: Mistral-7B-v0.1
- Training Focus: DevOps, SRE, Systems Architecture
- Format: GGUF (16-bit deployment build)
- Developer: Karmin66
- Downloads last month
- 10
Hardware compatibility
Log In to add your hardware
16-bit
Model tree for denysdenysevych/Lestro-7B
Base model
mistralai/Mistral-7B-v0.1