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Llama 3.2 1B Synthia II Redmond: Instruction-Tuned Multilingual LLM

Fine-tuned Llama 3.2 1B on Synthia-v1.5-II for better instruction following across 8 languages. Training details, usage, and license.

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On this page

  1. What This Model Does
  2. Training Details
  3. How to Use
  4. License
  5. Common Errors and Fixes
  6. FAQ
  7. Sources

Llama 3.2 1B Synthia II Redmond

TL;DR

What This Model Does

Llama 3.2 1B Synthia II Redmond is a fine-tuned variant of Meta's Llama 3.2 1B (via NousResearch's release), adapted on the Synthia-v1.5-II instruction dataset. The base model is a compact multilingual transformer built for assistant-style chat, agentic retrieval, and summarization, with a 128k context window and Grouped-Query Attention. This fine-tune keeps those properties while aiming to strengthen instruction-following behavior on the Synthia dataset.

The 1B size makes it a practical choice for local and edge inference — the weights are roughly 2.5 GB in BF16 and run comfortably on modest hardware, including CPU-only setups. That makes it a useful playground for experimenting with Synthia-style instruction tuning without the VRAM appetite of larger models.

Training Details

Parameter | Value

|---|---

Base model | NousResearch/Llama-3.2-1B Dataset | migtissera/Synthia-v1.5-II Epochs | 3 Learning rate | 2e-05 Batch size | 1 (effective 8 via gradient accumulation) Optimizer | Paged AdamW 8-bit LR scheduler | Cosine, 100 warmup steps Framework | Axolotl 0.5.0 Transformers | 4.46.1 PyTorch | 2.3.1+cu121 Training infrastructure was provided by RedmondAI, which gives the model its Redmond suffix.

How to Use

With Transformers

Load the model with the standard causal LM pipeline (requires transformers >= 4.43):

import torch
from transformers import pipeline

model_id = "artificialguybr/LLAMA3.2-1B-Synthia-II-Redmond"

pipe = pipeline("text-generation", model=model_id, torch_dtype=torch.bfloat16, device_map="auto")
print(pipe("Explain the water cycle in one paragraph"))

You can also load it directly with AutoModelForCausalLM and AutoTokenizer, then call generate() with your own decoding parameters (temperature, top-p, max_new_tokens). The repo ships safetensors weights, tokenizer files, and a generation config.

Deployment Notes

The model is tagged as compatible with text-generation-inference and endpoints, so it can be served on standard inference platforms as well as run locally.

License

This model is governed by the Llama 3.2 Community License — a custom, commercial license. Key obligations for redistribution:

Common Errors and Fixes

Error | Cause | Fix | Source

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

Out of memory on load | 1.2B-parameter BF16 weights need ~2.5 GB | Use 4-bit/8-bit quantization or CPU offload | Model repo safetensors index Slow generation | Full-precision load on weak GPU | Quantize or use a smaller max_new_tokens | — Non-English output weaker | Base model trained primarily on English-heavy data | Fine-tune further on target-language data | Base model card

FAQ

Q: What is the Synthia-v1.5-II dataset? A: An instruction-tuning dataset by migtissera, hosted on Hugging Face, used here to adapt Llama 3.2 1B for better instruction following.

Q: Can I use this commercially? A: Yes, under the Llama 3.2 Community License terms, including the attribution requirements listed above.

Q: How does this differ from the base Llama 3.2 1B? A: The base model is the pretrained foundation. This variant was fine-tuned on Synthia-v1.5-II with the hyperparameters above, and is distributed as a ready-to-use transformers checkpoint.

Q: What hardware do I need? A: The BF16 weights are about 2.5 GB, so roughly 4 GB of free GPU VRAM (or CPU RAM) is a safe starting point; quantization lowers that further.

Sources