Instructions to use KrushangShah/telecom-expert-v11-alpha-candidate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use KrushangShah/telecom-expert-v11-alpha-candidate with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="KrushangShah/telecom-expert-v11-alpha-candidate", filename="granite-telecom-q4km.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use KrushangShah/telecom-expert-v11-alpha-candidate with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KrushangShah/telecom-expert-v11-alpha-candidate # Run inference directly in the terminal: llama-cli -hf KrushangShah/telecom-expert-v11-alpha-candidate
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf KrushangShah/telecom-expert-v11-alpha-candidate # Run inference directly in the terminal: llama-cli -hf KrushangShah/telecom-expert-v11-alpha-candidate
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf KrushangShah/telecom-expert-v11-alpha-candidate # Run inference directly in the terminal: ./llama-cli -hf KrushangShah/telecom-expert-v11-alpha-candidate
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf KrushangShah/telecom-expert-v11-alpha-candidate # Run inference directly in the terminal: ./build/bin/llama-cli -hf KrushangShah/telecom-expert-v11-alpha-candidate
Use Docker
docker model run hf.co/KrushangShah/telecom-expert-v11-alpha-candidate
- LM Studio
- Jan
- Ollama
How to use KrushangShah/telecom-expert-v11-alpha-candidate with Ollama:
ollama run hf.co/KrushangShah/telecom-expert-v11-alpha-candidate
- Unsloth Studio new
How to use KrushangShah/telecom-expert-v11-alpha-candidate with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for KrushangShah/telecom-expert-v11-alpha-candidate to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for KrushangShah/telecom-expert-v11-alpha-candidate to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for KrushangShah/telecom-expert-v11-alpha-candidate to start chatting
- Pi new
How to use KrushangShah/telecom-expert-v11-alpha-candidate with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf KrushangShah/telecom-expert-v11-alpha-candidate
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "KrushangShah/telecom-expert-v11-alpha-candidate" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use KrushangShah/telecom-expert-v11-alpha-candidate with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf KrushangShah/telecom-expert-v11-alpha-candidate
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default KrushangShah/telecom-expert-v11-alpha-candidate
Run Hermes
hermes
- Docker Model Runner
How to use KrushangShah/telecom-expert-v11-alpha-candidate with Docker Model Runner:
docker model run hf.co/KrushangShah/telecom-expert-v11-alpha-candidate
- Lemonade
How to use KrushangShah/telecom-expert-v11-alpha-candidate with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull KrushangShah/telecom-expert-v11-alpha-candidate
Run and chat with the model
lemonade run user.telecom-expert-v11-alpha-candidate-{{QUANT_TAG}}List all available models
lemonade list
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Check out the documentation for more information.
Private Model Card Draft โ v11
Prepared on: 2026-04-11
Status:
- Draft for private artifact management only.
- Not approved for public publication.
Model Identity
- Display name:
Telecom Expert Q&A โ v11 alpha candidate - Runtime model name:
telecom-expert - Serving image validated for alpha:
10.127.251.228:32000/telecom-rag:canary-retrieval-audit-v11 - Base model lineage: IBM Granite 3.3 2B Instruct derivative, fine-tuned internally
- Deployment context: internal RAG-backed telecom engineering assistant
Overview
This artifact supports an internal telecom-domain engineering assistant that answers questions using retrieval grounding over public 3GPP and Cisco packet-core documentation. The model is intended to help engineers explain standards behavior, compare interfaces, review call-flow concepts, and retrieve relevant source material with citations.
The current v11 candidate is a serving-path hardening and retrieval-improvement milestone rather than a broad new model family. The validated improvement comes from the deployed retrieval/query path plus guardrails, not only from model weights in isolation.
Intended Use
Allowed use:
- internal telecom standards and Cisco packet-core Q&A
- explanation of 3GPP concepts, interfaces, and procedures
- grounded citation-oriented assistance using retrieved documentation
- engineer productivity support where a human reviews the answer before acting
Out Of Scope
Do not use this artifact for:
- autonomous network configuration changes
- direct execution of generated commands against production systems
- legal, HR, finance, medical, customer-eligibility, or person-impacting decisions
- answers that require authoritative source-of-record treatment without human verification
Validation Snapshot
Current exact-image candidate evidence:
- Day 1 full benchmark on deployed v11 image:
96/96responsesresponse_rate = 1.0timeouts_or_errors = 0mean_duration = 52.84sp95_duration = 115.56scli_safety_pass_rate = 1.0
- Day 3 retrieval audit:
spec_hit_top_8 = 0.8769request_errors = 0
- Day 7 readiness:
GO
Safety And Controls
Controls currently relied on:
- retrieval grounding over indexed public 3GPP and Cisco documentation
- bounded generation settings and trimmed prompt context
- CLI safety validation and refusal behavior when verified CLI evidence is absent
- internal-only deployment and controlled alpha scope
- stable rollback path preserved separately from the candidate
Known Limitations
- Section-level citation exactness still lags spec-family retrieval exactness.
- The system remains an internal alpha candidate, not a broad production release.
- Output quality depends strongly on retrieval quality and runtime configuration.
- Human validation remains mandatory for technical conclusions and CLI use.
Distribution Position
Recommended packaging position:
- private artifact storage only
- internal governance and reproducibility support
- no public release at this stage
Open Items Before Private Registry Publication
- suggested private repository slug:
telecom-expert-v11-alpha-candidate - recommended primary upload payload: remote merged-HF directory at
/mnt/data/models/granite-telecom-merged-hf/ - optional runtime payload: remote GGUF artifact at
/mnt/data/models/granite-telecom-q4km.gguf - attach sanitized evaluation summary and governance checklist
- confirm Granite derivative redistribution obligations
- confirm internal approval for storing derivative artifacts in the selected private repository
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We're not able to determine the quantization variants.