Qwen3.6-14B-A3B-VibeForged-v2-GGUF
Qwen3.6-14B-A3B-VibeForged-v2-GGUF is a 14B parameter quantized language model optimized for local deployment via llama.cpp. It includes vision capabilities and is offered in multiple quantization formats (F16 down to Q2_K) to fit various hardware constraints, from high-end GPUs to resource-limited systems. The model was fine-tuned on code-focused data with reasoning enforcement.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | tvall43 |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 34.3k |
| Likes | 29 |
| Last updated | 2026-05-29 |
| Source | tvall43/Qwen3.6-14B-A3B-VibeForged-v2-GGUF |
What Qwen3.6-14B-A3B-VibeForged-v2-GGUF is
A GGUF-quantized derivative of a pruned Qwen 35B base, fine-tuned via QLoRA on code and reasoning tasks. Supports multimodal inference (vision projector included). Multiple quantization levels provided (F16, Q8_0, Q6_K, Q4_K_M, Q3_K_M, Q2_K, MXFP4_MOE). Designed for llama.cpp-compatible engines. Context length, exact parameter count, and training dataset composition not fully documented.
Run Qwen3.6-14B-A3B-VibeForged-v2-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="tvall43/Qwen3.6-14B-A3B-VibeForged-v2-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated VRAM (ESTIMATE—verify with your inference engine): F16 ~28GB, Q8_0 ~15GB, Q6_K ~10GB, Q4_K_M ~8GB, Q3_K_M ~6GB, Q2_K ~4GB. Vision inference adds overhead depending on mmproj quantization. CPU-only inference possible at Q2_K/Q3_K_M with reduced throughput. Requires llama.cpp or compatible runtime.
Model card does not mention LoRA/QLoRA fine-tuning capabilities post-deployment. The base was fine-tuned via QLoRA by the developer, but further adaptation guidance is not provided. Quantized GGUF formats may not be ideal for efficient parameter-efficient fine-tuning; base (non-quantized) checkpoint would be required.
When to avoid it — and what to weigh
- High-throughput production inference required — llama.cpp is single-threaded or limited parallelism compared to vLLM or TensorRT. Not optimized for serving hundreds of concurrent requests at low latency.
- Strict model provenance and training transparency needed — Training data is described as 'Evol-Instruct code data' and 'OpenCode SQLite' extractions, but exact dataset composition, filtering, and ethics review are not documented.
- Enterprise support and SLA requirements — This is a community model from tvall43 with no stated support channel, SLA, or maintenance guarantee. Last modified date shows 2026-05-29, but long-term maintenance is unknown.
- Guaranteed model stability and reproducibility — The model is described as 'repaired' and 'fine-tuned by an AI agent.' No versioning scheme, rollback plan, or regression testing documentation provided.
License & commercial use
Apache 2.0 license, an OSI-approved permissive open-source license allowing modification, distribution, and commercial use with attribution and liability disclaimer.
Apache 2.0 permits commercial use. However, the model is derived from Qwen (origin and parent license not explicitly stated in the card). Verify that Qwen's base model license is compatible with your commercial use case and that no proprietary modifications are claimed. Attribution to tvall43 and the Apache 2.0 license must be retained.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Unknown |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Good |
| Assessment confidence | Medium |
No security audit or adversarial evaluation documented. Model was fine-tuned on user-provided code data ('OpenCode SQLite'), raising questions about data sanitization and potential code injection or unsafe pattern reproduction. Quantization and pruning may alter model behavior unpredictably. Use in production code-generation workflows should include output validation and sandboxing.
Alternatives to consider
Mistral 7B / Mistral Small
Smaller, widely adopted, better community support and benchmarks. Less multimodal support but easier deployment at scale.
LLaMA 2 13B / LLaMA 3 8B
Meta-backed, stronger documentation and ecosystem. LLaMA 3.2 includes vision capabilities. More predictable performance and maintenance.
StarCoder2 (Hugging Face / BigCode)
Purpose-built for code generation with official benchmarks. Smaller variants available; well-maintained by the BigCode consortium.
Ship Qwen3.6-14B-A3B-VibeForged-v2-GGUF with senior software developers
Evaluate Qwen3.6-14B-A3B-VibeForged-v2-GGUF in your environment with llama.cpp or Ollama. Choose your quantization, test latency on your hardware, and validate output quality against your code datasets. Consult legal review for commercial deployment.
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Qwen3.6-14B-A3B-VibeForged-v2-GGUF FAQ
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Custom software development services
Need help beyond evaluating Qwen3.6-14B-A3B-VibeForged-v2-GGUF? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and open-source llms integrations — and maintain them long-term.
Ready to deploy a local code-generation LLM?
Evaluate Qwen3.6-14B-A3B-VibeForged-v2-GGUF in your environment with llama.cpp or Ollama. Choose your quantization, test latency on your hardware, and validate output quality against your code datasets. Consult legal review for commercial deployment.