Qwen 2 1.5B Synthia II Redmond GGUF

TL;DR:

• GGUF quantization of a 1.5B Qwen 2 model fine-tuned on GPT-4 synthetic conversation data
• Runs locally through llama.cpp-compatible runtimes (llama.cpp, Ollama, LM Studio)
• Apache 2.0 licensed, instruct model using the ChatML prompt format

What This Model Is

This is a GGUF-quantized release of Qwen 2 1.5B Synthia II Redmond — a 1.5 billion parameter instruction model based on Qwen 2, fine-tuned on the Synthia v1.5-II dataset by migtissera. The Synthia dataset is built from synthetic conversations generated by GPT-4, a distillation approach that transfers GPT-4-style reasoning and formatting habits into a much smaller open model.

The quantization was done by ArtificialGuyBR, with GPU sponsorship from Redmond.AI, and targets the instruct fine-tune published at artificialguybr/QWEN-2-1.5B-Synthia-II-Redmond. The result is a single-file model that runs on modest hardware with no GPU required.

Training Background

• Base model: Qwen 2 1.5B
• Fine-tune dataset: migtissera/Synthia-v1.5-II (GPT-4 synthetic conversations)
• Format: ChatML (<|im_start|>, <|im_end|> roles)
• License: Apache 2.0
• Language: English (per the model card)

The Synthia lineage is a known distillation family: GPT-4 generates high-quality instruction-response pairs, and those pairs fine-tune smaller models. This release applies that recipe to Qwen 2 at the 1.5B scale, keeping the model small enough for embedded or offline use cases. The model card lists synthetic data and distillation among its tags, confirming the training approach.

How to Use

The GGUF file drops into any llama.cpp-compatible runtime:

• llama.cpp — direct CLI loading: llama-cli -m qwen-2-1.5b-synthia-ii-redmond.gguf -p "<|im_start|>user\nHello<|im_end|>\n<|im_start|>assistant\n"
• Ollama — create a Modelfile that points at the GGUF file (with TEMPLATE """<|im_start|>user\n{{ .Prompt }}<|im_end|>\n<|im_start|>assistant\n""")
• LM Studio — drag the GGUF into the models folder
• text-generation-webui — use the GGUF loader with llama.cpp backend

The model card explicitly points readers to TheBloke's GGUF guides for usage details, including how to concatenate multi-part GGUF files when a release is split into several parts. If you are new to GGUF files, our guide to running LLMs locally covers the same workflow across runtimes.

Prompt Template

This is a ChatML instruct model. Prompts must use the ChatML roles:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Most modern runtimes detect the tokenizer config from the GGUF file and apply this template automatically; in raw llama.cpp CLI usage you pass it manually.

Choosing a Quantization

Quantized GGUF files trade size and memory against quality. The standard guidance applies here as in our GGUF quantization walkthrough: start with a 4-bit or 5-bit K-quant (Q4_K_M, Q5_K_M) for the best size/quality balance, and only move to Q8_0 if memory allows and you want near-fp16 quality. The repo's file tree lists the available per-file quantizations; pick the one that fits your RAM budget.

At 1.5B parameters, even the largest quantizations stay under a couple of gigabytes, so the model remains practical for laptops, small VPS boxes, and single-board deployments. No exact VRAM figure is published on the card, so measure against your own hardware rather than relying on estimates.

License

Apache 2.0, per the model card metadata. That permits commercial and non-commercial use, modification, and redistribution with attribution. The upstream Qwen 2 base and Synthia dataset both carry permissive licenses, which is why this fine-tune can be distributed as-is.

Common Errors and Fixes

Error | Cause | Fix

|---|---|---

Garbled or repetitive output | Wrong prompt template | Apply ChatML roles explicitly; check the runtime's template override Slow generation | Heavy quantization or CPU-only | Use a 4-bit/5-bit K-quant; enable GPU offload in the runtime settings Multi-part file won't load | Parts not concatenated | Follow TheBloke's concatenation instructions referenced in the model card

FAQ

Q: Is this the same as the original Qwen 2 1.5B? A: No. It is Qwen 2 1.5B fine-tuned on Synthia v1.5-II (GPT-4 synthetic data), then quantized to GGUF by ArtificialGuyBR.

Q: Do I need a GPU? A: No. GGUF files run on CPU through llama.cpp; a GPU only speeds them up. The card publishes no exact VRAM numbers.

Q: Is commercial use allowed? A: Yes, the model is Apache 2.0 licensed.

Q: What languages does it support? A: The card lists English. The upstream Qwen 2 base is multilingual, but the Synthia fine-tune targets English conversation.

Q: Where is the unquantized version? A: artificialguybr/QWEN-2-1.5B-Synthia-II-Redmond, which this GGUF release quantizes.

Sources

• HF model card: artificialguybr/QWEN-2-1.5B-Synthia-II-Redmond-gguf — quantization details, license, usage pointer
• HF model card: artificialguybr/QWEN-2-1.5B-Synthia-II-Redmond — the fine-tune this GGUF quantizes
• Synthia v1.5-II dataset — the GPT-4 synthetic training data