Qwen3.5-9B-GLM5.1-Distill-v1-GGUF
Qwen3.5-9B-GLM5.1-Distill-v1 is a 9-billion-parameter language model fine-tuned via knowledge distillation from GLM-5.1 reasoning data. It emphasizes structured multi-step reasoning, problem decomposition, and instruction-following consistency. The model is quantized in GGUF format for local inference and targets analytical tasks (coding, math, logic). It is not gated and carries an Apache 2.0 license. The developer explicitly positions this as an academic/research release and cautions that gains from 9B-scale SFT are typically incremental rather than dramatic.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Jackrong |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | image-text-to-text |
| Gated on HuggingFace | No |
| Downloads | 39.7k |
| Likes | 106 |
| Last updated | 2026-07-07 |
| Source | Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1-GGUF |
What Qwen3.5-9B-GLM5.1-Distill-v1-GGUF is
Base model: Qwen3.5-9B. Training approach: Supervised fine-tuning (SFT) with LoRA using Unsloth, trained on ~700K cleaned reasoning examples derived from GLM-5.1 teacher traces. Key innovation is transfer of reasoning structure rather than token-level imitation. Model supports image-text-to-text and multilingual input (English, Chinese, Japanese). Quantized to GGUF format for efficient local deployment. Published 2026-07-07, 39,672 downloads, 106 likes. Parameter count, exact context length, and training hyperparameters not disclosed.
Run Qwen3.5-9B-GLM5.1-Distill-v1-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Jackrong/Qwen3.5-9B-GLM5.1-Distill-v1-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
ESTIMATE: GGUF quantization (typically int4 or int8) reduces memory footprint. Rough guidance: 4–6 GB VRAM for full model inference on GPU; CPU inference feasible with 8–16 GB RAM on modern systems. Exact precision and quantization scheme not specified in card; requires verification of published quantization artifacts. Recommended: GPU with 6+ GB VRAM for acceptable latency, or CPU-only for latency-tolerant workloads.
Model was trained using LoRA + Unsloth for efficiency. Card indicates LoRA is viable for further fine-tuning. Unsloth support suggests QLoRA and memory-efficient training remain practical on consumer-grade GPUs. Developer provides a detailed fine-tuning guide (PDF) covering data preparation, hyperparameter configuration, and Hugging Face publishing. Feasibility for downstream SFT is good; users should reference the provided GitHub repository for reproducible pipelines.
When to avoid it — and what to weigh
- Production systems requiring real-time factual accuracy — Model card explicitly warns of hallucination risk in autoregressive LLMs. Unsuitable for fact-critical applications (medical, legal, financial) without external verification and guardrails.
- Need for large-scale, current knowledge cutoff — 9B distilled model lacks the scale and fine-tuning depth of larger proprietary models; may not maintain state-of-the-art performance on rapidly evolving domains or very recent events.
- Requirement for rapid inference and high throughput — While GGUF enables local inference, 9B model's latency on consumer hardware may exceed cloud-hosted alternatives; not optimal for latency-critical user-facing applications.
- Expectation of dramatic capability gains from fine-tuning — Developer explicitly cautions that gains from 9B-scale reasoning SFT are typically gradual and consistency-focused, not dramatic capability jumps. Manage expectations accordingly.
License & commercial use
Licensed under Apache 2.0, a permissive OSI-approved license. Grants rights to use, modify, and distribute subject to preservation of copyright notice and license text. No patent indemnity; Apache 2.0 includes explicit patent grant from contributors.
Apache 2.0 is a permissive, commercial-friendly license that permits commercial use, modification, and distribution. However: (1) Model card states the release is 'intended solely for learning and demonstration purposes' and 'for academic research and technical exploration use only'—creating ambiguity between license permissions and stated intent. (2) Base model is Qwen3.5-9B; verify Qwen's upstream license and any usage restrictions. (3) Training data includes GLM-5.1 reasoning traces; confirm no restrictions on derived works exist in upstream datasets (Kassadin88/GLM-5.1-1000000x). Recommendation: Review upstream Qwen license and GLM-5.1 data terms, and obtain explicit legal counsel before deploying commercially. Apache 2.0 does not automatically resolve data-origin or base-model restrictions.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Strong |
| License clarity | Needs review |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | Medium |
No formal security audit or adversarial robustness claims provided. Considerations: (1) Distilled model may inherit vulnerabilities or biases from GLM-5.1 teacher data if not properly filtered. (2) GGUF quantization does not constitute a security mechanism; local inference still requires trust in the model artifact source. (3) Model card warns of hallucination risk; use in sensitive contexts requires fact-checking and guardrails. (4) No information on data privacy, training set composition beyond 'cleaned GLM-5.1 reasoning data', or content filtering. (5) Recommend threat-modeling for your deployment context before production use.
Alternatives to consider
Llama 3.1 8B or 70B (Meta)
Larger community, more stable maintenance, proven fine-tuning ecosystem (Unsloth, vLLM), and clearer licensing (Llama 2 Community License with commercial restrictions; Llama 3.1 under a modified license). Trade-off: less explicit reasoning-structure focus than Qwen distill.
Mistral 7B or Mixtral 8x7B
Permissive Apache 2.0 license, strong instruction-following, well-documented, active community. Smaller footprint than Qwen alternatives; good for local inference on limited hardware. Less specialized for chain-of-thought but solid baseline.
DeepSeek-LLM (various sizes, including 7B/67B)
Permissive license, strong reasoning capability (DeepSeek-R1 series focus on reasoning), active development, and explicit design for chain-of-thought tasks. May offer comparable or superior reasoning gains without relying on distillation from GLM-5.1.
Ship Qwen3.5-9B-GLM5.1-Distill-v1-GGUF with senior software developers
Qwen3.5-9B-GLM5.1-Distill-v1 is an excellent choice for offline analytical tasks and local inference. Before production deployment, verify commercial licensing terms with your legal team, test performance on your specific use cases, and review the model's hallucination limitations. Our team can help you integrate, fine-tune, and operationalize this model in your stack.
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Qwen3.5-9B-GLM5.1-Distill-v1-GGUF FAQ
Can I use this model for commercial products or services?
What GPU VRAM do I need to run this locally?
How much better is this than the base Qwen3.5-9B?
What is the context length?
Software development & web development with DEV.co
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Qwen3.5-9B-GLM5.1-Distill-v1-GGUF is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy a Private, Reasoning-Focused LLM?
Qwen3.5-9B-GLM5.1-Distill-v1 is an excellent choice for offline analytical tasks and local inference. Before production deployment, verify commercial licensing terms with your legal team, test performance on your specific use cases, and review the model's hallucination limitations. Our team can help you integrate, fine-tune, and operationalize this model in your stack.